Note: Descriptions are shown in the official language in which they were submitted.
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SYSTEMS, METHODS, AND DEVICES FOR MEDICAL IMAGE
ANALYSIS, DIAGNOSIS, RISK STRATIFICATION, DECISION
MAKING AND/OR DISEASE TRACKING
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001]
The present application is a continuation-in-part of U.S. Patent
Application No. 17/662,734, filed May 10, 2022. The present application also
claims the
benefit of U.S. Provisional Patent Application Nos. 63/235,010, filed August
19, 2021,
63/241,427, filed September 7, 2021, 63/276,268, filed November 5, 2021,
63/264,805,
filed December 2, 2021,63/264,913, filed December 3,2021, and 63/296,116,
filed January
3, 2022. U.S. Patent Application No. 17/662,734 is a continuation of U.S.
Patent
Application No. 17/367,549, filed July 5, 2021, which is a continuation of
U.S. Patent
Application No. 17/350,836, filed June 17, 2021, which is a continuation-in-
part of U.S.
Patent Application No. 17/213,966, filed March 26, 2021, which is a
continuation of U.S.
Patent Application No. 17/142,120, filed January 5, 2021, which claims the
benefit of U.S.
Provisional Patent Application No. 62/958,032, filed January 7, 2020. U.S.
Patent
Application No. 17/350,836 claims the benefit of U.S. Provisional Patent
Application Nos.
63/201,142, filed April 14, 2021, 63/041,252, filed June 19, 2020, 63/077,044,
filed
September 11, 2020, 63/077,058, filed September 11, 2020, 63/089,790, filed
October 9,
2020, and 63/142,873, filed January 28, 2021. Each one of the above-listed
disclosures is
incorporated herein by reference in its entirety. Also, U.S. Patent No.
10,813,612 is
incorporated herein by reference in its entirety. Any and all applications for
which a foreign
or domestic priority claim is identified in the Application Data Sheet as
filed with the
present application are hereby incorporated by reference under 37 C.F.R.
1.57.
BACKGROUND
Field
[0002]
The present application relates to systems, methods, and devices for
medical image analysis, diagnosis, risk stratification, decision making and/or
disease
tracking.
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Description
[0003]
Coronary heart disease affects over 17.6 million Americans. The current
trend in treating cardiovascular health issues is generally two-fold. First,
physicians
generally review a patient's cardiovascular health from a macro level, for
example, by
analyzing the biochemistry or blood content or biomarkers of a patient to
determine
whether there are high levels of cholesterol elements in the bloodstream of a
patient. In
response to high levels of cholesterol, some physicians will prescribe one or
more drugs,
such as statins, as part of a treatment plan in order to decrease what is
perceived as high
levels of cholesterol elements in the bloodstream of the patient.
100041
The second general trend for currently treating cardiovascular health
issues involves physicians evaluating a patient's cardiovascular health
through the use of
angiography- to identify large blockages in various arteries of a patient. In
response to
finding large blockages in various arteries, physicians in some cases will
perform an
angioplasty procedure wherein a balloon catheter is guided to the point of
narrowing in the
vessel. After properly positioned, the balloon is inflated to compress or
flatten the plaque
or fatty matter into the artery wall and/or to stretch the artery open to
increase the flow of
blood through the vessel and/or to the heart. In some cases, the balloon is
used to position
and expand a stent within the vessel to compress the plaque and/or maintain
the opening of
the vessel to allow more blood to flow. About 500,000 heart stent procedures
are performed
each year in the United States.
[0005]
However, a recent federally funded $100 million study calls into
question whether the current trends in treating cardiovascular disease are the
most effective
treatment for all types of patients. The recent study involved over 5,000
patients with
moderate to severe stable heart disease from 320 sites in 37 countries and
provided new
evidence showing that stents and bypass surgical procedures are likely no more
effective
than drugs combined with lifestyle changes for people with stable heart
disease.
Accordingly, it may be more advantageous for patients with stable heart
disease to forgo
invasive surgical procedures, such as angioplasty and/or heart bypass, and
instead be
prescribed heart medicines, such as statins, and certain lifestyle changes,
such as regular
exercise. This new treatment regimen could affect thousands of patients
worldwide. Of
the estimated 500,000 heart stent procedures performed annually in the United
States, it is
estimated that a fifth of those are for people with stable heart disease. It
is further estimated
that 25% of the estimated 100,000 people with stable heart disease, or roughly
23,000
people, are individuals that do not experience any chest pain. Accordingly,
over 20,000
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patients annually could potentially forgo invasive surgical procedures or the
complications
resulting from such procedures.
[0006]
To determine whether a patient should forego invasive surgical
procedures and opt instead for a drug regimen, it can be important to more
fully understand
the cardiovascular disease of a patient. Specifically, it can be advantageous
to better
understand the arterial vessel health of a patient.
SUMMARY
[0007]
Various embodiments described herein relate to systems, methods, and
devices for medical image analysis, diagnosis, risk stratification, decision
making and/or
disease tracking.
[0008]
In particular, in some embodiments, the systems, devices, and methods
described herein are configured to utilize non-invasive medical imaging
technologies, such
as a CT image for example, which can be inputted into a computer system
configured to
automatically and/or dynamically analyze the medical image to identify one or
more
coronary arteries and/or plaque within the same. For example, in some
embodiments, the
system can be configured to utilize one or more machine learning and/or
artificial
intelligence algorithms to automatically and/or dynamically analyze a medical
image to
identify, quantify, and/or classify one or more coronary arteries and/or
plaque. In some
embodiments, the system can be further configured to utilize the identified,
quantified,
and/or classified one or more coronary arteries and/or plaque to generate a
treatment plan,
track disease progression, and/or a patient-specific medical report, for
example using one
or more artificial intelligence and/or machine learning algorithms. In some
embodiments,
the system can be further configured to dynamically and/or automatically
generate a
visualization of the identified, quantified, and/or classified one or more
coronary arteries
and/or plaque, for example in the form of a graphical user interface. Further,
in some
embodiments, to calibrate medical images obtained from different medical
imaging
scanners and/or different scan parameters or environments, the system can be
configured
to utilize a normalization device comprising one or more compartments of one
or more
materials.
[0009]
In some embodiments, a normalization device configured to normalize
a medical image of a coronary region of a subject for an algorithm-based
medical imaging
analysis comprises: a substrate configured in size and shape to be placed in a
medical
imager along with a patient so that the normalization device and the patient
can be imaged
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together such that at least a region of interest of the patient and the
normalization device
appear in a medical image taken by the medical imager; a plurality of
compartments
positioned on or within the substrate, wherein an arrangement of the plurality
of
compartments is fixed on or within the substrate; a plurality of samples, each
of the plurality
of samples positioned within one of the plurality of compartments, and wherein
a volume,
an absolute density, and a relative density of each of the plurality of
samples is known, the
plurality of samples comprising: a set of contrast samples, each of the
contrast samples
comprising a different absolute density than absolute densities of the others
of the contrast
samples; a set of calcium samples, each of the calcium samples comprising a
different
absolute density than absolute densities of the others of the calcium samples;
and a set of
fat samples, each of the fat samples comprising a different absolute density
than absolute
densities of the others of the fat samples; and wherein the set contrast
samples are arranged
within the plurality of compartments such that the set of calcium samples and
the set of fat
samples surround the set of contrast samples.
[0010]
In some embodiments, the normalization device further comprises an
attachment mechanism disposed on the substrate, the attachment mechanism
configured to
attach the normalization device to the patient so that the normalization
device and the
patient can be imaged together such that the region of interest of the patient
and the
normalization device appear in the medical image taken by the medical imager.
In some
embodiments of the normalization device, the set of contrast samples comprise
four
contrast samples; the set of calcium samples comprise four calcium samples;
and the set of
fat samples comprise four fat samples. In some embodiments of the
normalization device,
the plurality of samples further comprises at least one of an air sample and a
water sample.
In some embodiments of the normalization device, the volume of a first
contrast sample is
different than a volume of a second contrast sample; the volume of a first
calcium sample
is different than a volume of a second calcium sample; and the volume of a
first fat sample
is different than a volume of a second fat sample. In some embodiments of the
normalization device, a first contrast sample is arranged within the plurality
of
compartments so as to be adjacent to a second contrast sample, a first calcium
sample, and
a first fat sample. In some embodiments of the normalization device, a first
calcium sample
is arranged within the plurality of compartments so as to be adjacent to a
second calcium
sample, a first contrast sample, and a first fat sample. In some embodiments
of the
normalization device, a first fat sample is arranged within the plurality of
compartments so
as to be adjacent to a second fat sample, a first contrast sample, and a first
calcium sample.
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In some embodiments of the normalization device, the set of contrast samples,
the set of
calcium samples, and the set of fat samples are arranged in a manner that
mimics a blood
vessel.
100111
In some embodiments, a computer implemented method for generating
a risk assessment of atherosclerotic cardiovascular disease (ASCVD) using the
normalization device, wherein normalization of the medical imaging improves
accuracy of
the algorithm-based imaging analysis, comprises: receiving a first set of
images of a first
arterial bed and a first set of images of a second arterial bed, the second
arterial bed being
noncontiguous with the first arterial bed, and wherein at least one of the
first set of images
of the first arterial bed and the first set of images of the second arterial
bed are normalized
using the normalization device; quantifying ASCVD in the first arterial bed
using the first
set of images of the first arterial bed; quantifying ASCVD in the second
arterial bed using
the first set of images of the second arterial bed; and determining a first
ASCVD risk score
based on the quantified ASCVD in the first arterial bed and the quantified
ASCVD in the
second arterial bed.
[0012]
In some embodiments, the method for generating a risk assessment of
atherosclerotic cardiovascular disease (ASCVD) further comprises: determining
a first
weighted assessment of the first arterial bed based on the quantified ASCVD of
the first
arterial bed and weighted adverse events for the first arterial bed; and
determining a second
weighted assessment of the second arterial bed based on the quantified ASCVD
of the
second arterial bed and weighted adverse events for the second arterial bed,
wherein
determining the first ASCVD risk score further comprises determining the ASCVD
risk
score based on the first weighted assessment and the second weighted
assessment. Further,
in some embodiments, the method for generating a risk assessment of
atherosclerotic
cardiovascular disease (ASCVD) further comprises: receiving a second set of
images of the
first arterial bed and a second set of images of the second arterial bed, the
second set of
images of the first arterial bed generated subsequent to generating the first
set of image of
the first arterial bed, and the second set of images of the second arterial
bed generated
subsequent to generating the first set of image of the second arterial bed;
quantifying
ASCVD in the first arterial bed using the second set of images of the first
arterial bed;
quantifying ASCVD in the second arterial bed using the second set of images of
the second
arterial bed; and determining a second ASCVD risk score based on the
quantified ASCVD
in the first arterial bed using the second set of images, and the quantified
ASCVD in the
second arterial bed using the second set of images. In some embodiments of the
method
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for generating a risk assessment of atherosclerotic cardiovascular disease
(ASCVD),
determining the second ASCVD risk score is further based on the first ASCVD
risk score.
In some embodiments of the method for generating a risk assessment of
atherosclerotic
cardiovascular disease (ASCVD), the first arterial bed includes arteries of
one of the aorta,
carotid arteries, lower extremity arteries, renal arteries, or cerebral
arteries, and wherein the
second arterial bed includes arteries of one of the aorta, carotid arteries,
lower extremity
arteries, renal arteries, or cerebral arteries that are different than the
arteries of the first
arterial bed.
[0013]
In some embodiments, a computer implemented method of generating a
multi-media medical report for a patient that is based on images generated
using the
normalization device, wherein the normalization device improves accuracy of
the non-
invasive medical image analysis, the medical report associated with one or
more tests of
the patient, comprises: receiving an input of a request to generate the
medical report for a
patient, the request indicating a format for the medical report; receiving
patient information
relating to the patient, the patient information associated with the report
generation request;
determining one or more patient characteristics associated with the patient
using the patient
information; accessing associations between types of medical reports and
patient medical
information, wherein the patient medical information includes medical images
relating to
the patient and test results of one or more test that were performed on the
patient, the
medical images generated using the normalization device; accessing report
content
associated with the patient's medical information and the medical report
requested, wherein
the report content comprises multimedia content that is not related to a
specific patient, the
multimedia content including a greeting segment in the language of the
patient, an
explanation segment explaining a type of test conducted, a results segment for
conveying
test results, and an explanation segment explaining results of the test, and a
conclusion
segment, wherein at least a portion of the multimedia content includes a test
result and one
or more medical images that are related to a test performed on the patient;
and generating,
based at least in part on the format of the medical report, the requested
medical report using
the patient information and report content.
[0014]
In some embodiments, a computer implemented method of assessing a
risk of coronary artery disease (CAD) for a subject by generating one or more
CAD risk
scores for the subject based on multi-dimensional information derived from non-
invasive
medical image analysis using the normalization device, wherein the
normalization device
improves accuracy of the non-invasive medical image analysis, comprises:
accessing, by a
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computer system, a medical image of a coronary region of a subject, wherein
the medical
image of the coronary region of the subject is obtained non-invasively;
identifying, by the
computer system, one or more segments of coronary arteries within the medical
image of
the coronary region of the subject; determining, by the computer system, for
each of the
identified one or more segments of coronary arteries one or more plaque
parameters, vessel
parameters, and clinical parameters, wherein the one or more plaque parameters
comprise
one or more of plaque volume, plaque composition, plaque attenuation, or
plaque location,
wherein the one or more vessel parameters comprise one or more of stenosis
severity,
lumen volume, percentage of coronary blood volume, or percentage of fractional
myocardial mass, and wherein the one or more clinical parameters comprise one
or more
of percentile health condition for age or percentile health condition for
gender; generating,
by the computer system, for each of the identified one or more segments of
coronary arteries
a weighted measure of the determined one or more plaque parameters, vessel
parameters,
and clinical parameters, wherein the weighted measure is generated by applying
a
correction factor; combining, by the computer system, the generated weighted
measure of
the determined one or more plaque parameters, vessel parameters, and clinical
parameters
for each of the identified one or more segments of coronary arteries to
generate one or more
per-vessel, per-vascular territory, or per-subject CAD risk scores; and
generating, by the
computer system, a graphical plot of the generated one or more per-vessel, per-
vascular
territory, or per-subject CAD risk scores for visualizing and quantifying risk
of CAD for
the subject on one or more of a per-vessel, per-vascular, or per-subject
basis, wherein the
computer system comprises a computer processor and an electronic storage
medium.
100151
In some embodiments, a computer implemented method of tracking
efficacy of a medical treatment for a plaque-based disease based on non-
invasive medical
image analysis using the normalization device, wherein the normalization
device improves
accuracy of the non-invasive medical image analysis, comprises: accessing, by
a computer
system, a first set of plaque parameters and a first set of vascular
parameters associated
with a subject, wherein the first set of plaque parameters and the first set
of vascular
parameters are derived from a first medical image of the subject comprising
one or more
regions of plaque, wherein the first medical image of the subject is obtained
non-invasively
at a first point in time, wherein the first set of plaque parameters comprises
one or more of
density, location, or volume of one or more regions of plaque from the medical
image of
the subject at the first point in time, and wherein the first set of vascular
parameters
comprises vascular remodeling of a vasculature at the first point in time;
accessing, by the
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computer system, a second medical image of the subject, wherein the second
medical image
of the subject is obtained non-invasively at a second point in time after the
subject is treated
with a medical treatment, the second point in time being later than the first
point in time,
wherein the second medical image of the subject comprises the one or more
regions of
plaque; identifying, by the computer system, the one or more regions of plaque
from the
second medical image; determining, by the computer system, a second set of
plaque
parameters and a second of vascular parameters associated with the subject by
analyzing
the one or more regions of plaque from the second medical image, wherein the
second set
of plaque parameters comprises one or more of density, location, or volume of
the one or
more regions of plaque from the medical image of the subject at the second
point in time,
and wherein the second set of vascular parameters comprises vascular
remodeling of the
vasculature at the second point in time; analyzing, by the computer system,
one or more
changes between the first set of plaque parameters and the second set of
plaque parameters;
analyzing, by the computer system, one or more changes between the first set
of vascular
parameters and the second set of vascular parameters; tracking, by the
computer system,
progression of the plaque-based disease based on one or more of the analyzed
one or more
changes between the first set of plaque parameters and the second set of
plaque parameters
or the analyzed one or more changes between the first set of vascular
parameters and the
second set of vascular parameters; and determining, by the computer system,
efficacy of
the medical treatment based on the tracked progression of the plaque-based
disease,
wherein the computer system comprises a computer processor and an electronic
storage
medium.
100161
In some embodiments, a computer implemented method of determining
continued personalized treatment for a subject with atherosclerotic
cardiovascular disease
(ASCVD) risk based on coronary CT angiography (CCTA) analysis using one or
more
quantitative imaging algorithms using the normalization device, wherein the
normalization
device improves accuracy of the one or more quantitative imaging algorithms,
comprises:
assessing, by a computer system, a baseline ASCVD risk of the subject by
analyzing
baseline CCTA analysis results using one or more quantitative imaging
algorithms, the
baseline CCTA analysis results based at least in part on one or more
atherosclerosis
parameters or perilesional tissue parameters, the one or more atherosclerosis
parameters
comprising one or more of presence, locality, extent, severity, or type of
atherosclerosis;
categorizing, by the computer system, the baseline ASCVD risk of the subject
into one or
more predetermined categories of ASCVD risk; determining, by the computer
system, an
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initial personalized proposed treatment for the subject based at least in part
on the
categorized baseline ASCVD risk of the subject, the initial personalized
proposed treatment
for the subject comprising one or more of medical therapy, lifestyle therapy,
or
interventional therapy; assessing, by the computer system, subject response to
the
determined initial personalized proposed treatment by subsequent CCTA analysis
using
one or more quantitative imaging algorithms and comparing the subsequent CCTA
analysis
results to the baseline CCTA analysis results, the subsequent CCTA analysis
performed
after applying the determined initial personalized proposed treatment to the
subject,
wherein the subject response is assessed based on one or more of progression,
stabilization,
or regression of ASCVD; and determining, by the computer system, a continued
personalized proposed treatment for the subject based at least in part on the
assessed subject
response, the continued personalized proposed treatment comprising a higher
tiered
approach than the initial personalized proposed treatment when the assessed
subject
response comprises progression of ASCVD, the continued personalized proposed
treatment
comprising one or more of medical therapy, lifestyle therapy, or
interventional therapy,
wherein the computer system comprises a computer processor and an electronic
storage
medium.
[0017]
In some embodiments, a computer implemented method of determining
volumetric stenosis severity in the presence of atherosclerosis based on non-
invasive
medical image analysis for risk assessment of coronary artery disease (CAD)
for a subject
using the normalization device, wherein the normalization device improves
accuracy of the
non-invasive medical image analysis, comprises: accessing, by a computer
system, a
medical image of a coronary region of a subject, wherein the medical image of
the coronary
region of the subject is obtained non-invasively; identifying, by the computer
system, one
or more segments of coronary arteries and one or more regions of plaque within
the medical
image of the coronary region of the subject; determining, by the computer
system, for the
identified one or more segments of coronary arteries a lumen wall boundary in
the presence
of the one or more regions of plaque and a hypothetical normal artery boundary
in case the
one or more regions of plaque were not present, wherein the determined lumen
wall
boundary and the hypothetical normal artery boundary comprise tapering of the
one or more
segments of coronary arteries, and wherein the determined lumen wall boundary
further
comprises a boundary of the one or more regions of plaque; quantifying, by the
computer
system, for the identified one or more segments of coronary arteries a lumen
volume based
on the determined lumen wall boundary, wherein the quantified lumen volume
takes into
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account the tapering of the one or more segments of coronary arteries and the
boundary of
the one or more regions of plaque; quantifying, by the computer system, for
the identified
one or more segments of coronary arteries a hypothetical normal vessel volume
based on
the determined hypothetical normal artery boundary, wherein the quantified
hypothetical
normal vessel volume takes into account the tapering of the one or more
segments of
coronary arteries; determining, by the computer system, for the identified one
or more
segments of coronary arteries volumetric stenosis by determining a percentage
or ratio of
the quantified lumen volume compared to the hypothetical normal vessel volume;
and
determining, by the computer system, a risk of CAD for the subject based at
least in part
on the determined volumetric stenosis for the identified one or more segments
of coronary
arteries, wherein the computer system comprises a computer processor and an
electronic
storage medium.
[0018]
In some embodiments, a computer implemented method of quantifying
ischemia for a subject based on non-invasive medical image analysis using the
normalization device, wherein the normalization device improves accuracy of
the non-
invasive medical image analysis, comprises: accessing, by a computer system, a
medical
image of a coronary region of a subject, wherein the medical image of the
coronary region
of the subject is obtained non-invasively; identifying, by the computer
system, one or more
segments of coronary arteries and one or more regions of plaque within the
medical image
of the coronary region of the subject; quantifying, by the computer system, a
proximal
volume of a proximal section and a distal volume of a distal section along the
one or more
segments of coronary arteries, wherein the proximal section does not comprise
the one or
more regions of plaque, and wherein the distal section comprises at least one
of the one or
more regions of plaque; accessing, by the computer system, an assumed velocity
of blood
flow at the proximal section; quantifying, by the computer system, a velocity
of blood flow
at the distal section based at least in part on the assumed velocity of blood
flow at the
proximal section, the quantified proximal volume of the proximal section, and
the distal
volume of the distal section along the one or more segments of coronary
arteries;
determining, by the computer system, a velocity time integral of blood flow at
the distal
section based at least in part on the quantified velocity of blood flow at the
distal section;
and quantifying, by the computer system, ischemia along the one or more
segments of
coronary arteries based at least in part on the determined velocity time
integral of blood
flow at the distal section, wherein the computer system comprises a computer
processor
and an electronic storage medium.
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[0019]
For purposes of this summary, certain aspects, advantages, and novel
features of the invention are described herein. It is to be understood that
not necessarily all
such advantages may be achieved in accordance with any particular embodiment
of the
invention. Thus, for example, those skilled in the art will recognize that the
invention may
be embodied or carried out in a manner that achieves one advantage or group of
advantages
as taught herein without necessarily achieving other advantages as may be
taught or
suggested herein.
[0020]
All of these embodiments are intended to be within the scope of the
invention herein disclosed. These and other embodiments will become readily
apparent to
those skilled in the art from the following detailed description having
reference to the
attached figures, the invention not being limited to any particular disclosed
embodiment(s).
BRIEF DESCRIPTION OF THE DRAWINGS
[0021]
The disclosed aspects will hereinafter be described in conjunction with
the accompanying drawings, which are incorporated in and constitute a part of
this
specification, and are provided to illustrate and provide a further
understanding of example
embodiments, and not to limit the disclosed aspects. In the drawings, like
designations
denote like elements unless otherwise stated.
[0022]
Figure 1 is a flowchart illustrating an overview of an example
embodiment(s) of a method for medical image analysis, visualization, risk
assessment,
disease tracking, treatment generation, and/or patient report generation.
[0023]
Figure 2A is a flowchart illustrating an overview of an example
embodiment(s) of a method for analysis and classification of plaque from a
medical image.
[0024]
Figure 2B is a flowchart illustrating an overview of an example
embodiment(s) of a method for determination of non-calcified plaque from a non-
contrast
CT image(s).
[0025]
Figure 3A is a flowchart illustrating an overview of an example
embodiment(s) of a method for risk assessment based on medical image analysis.
[0026]
Figure 3B is a flowchart illustrating an overview of an example
embodiment(s) of a method for quantification of atherosclerosis based on
medical image
analysis.
[0027]
Figure 3C is a flowchart illustrating an overview of an example
embodiment(s) of a method for quantification of stenosis and generation of a
CAD-RADS
score based on medical image analysis.
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[0028]
Figure 3D is a flowchart illustrating an overview of an example
embodiment(s) of a method for disease tracking based on medical image
analysis.
[0029]
Figure 3E is a flowchart illustrating an overview of an example
embodiment(s) of a method for determination of cause of change in calcium
score based
on medical image analysis.
[0030]
Figure 4A is a flowchart illustrating an overview of an example
embodiment(s) of a method for prognosis of a cardiovascular event based on
medical image
analysis.
[0031]
Figure 4B is a flowchart illustrating an overview of an example
embodiment(s) of a method for determination of patient-specific stent
parameters based on
medical image analysis.
[0032]
Figure 5A is a flowchart illustrating an overview of an example
embodiment(s) of a method for generation of a patient-specific medical report
based on
medical image analysis.
[0033]
Figures 5B-5I illustrate example embodiment(s) of a patient-specific
medical report generated based on medical image analysis.
[0034]
Figure 6A illustrates an example of a user interface that can be generated
and displayed on the system, the user interface having multiple panels (views)
that can
show various corresponding views of a patient's arteries.
[0035]
Figure 6B illustrates an example of a user interface that can be generated
and displayed on the system, the user interface having multiple panels that
can show various
corresponding views of a patient's arteries.
[0036]
Figures 6C, 6D, and 6E illustrate certain details of a multiplanar
reformat (MPR) vessel view in the second panel, and certain functionality
associated with
this view.
[0037]
Figure 6F illustrates an example of a three-dimensional (3D) rendering
of a coronary artery tree that allows a user to view the vessels and modify
the labels of a
vessel.
[0038]
Figure 6G illustrates an example of a panel of the user interface that
provides shortcut commands that a user may employ while analyzing information
in the
user interface in a coronary artery tree view, an axial view, a sagittal view,
and a coronal
view.
[0039]
Figure 6H illustrates examples of panels of the user interface for viewing
DICOM images in three anatomical planes: axial, coronal, and sagittal.
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[0040]
Figure 61 illustrates an example of a panel of the user interface showing
a cross-sectional view of a vessel, in the graphical overlay of an extracted
feature of the
vessel.
[0041]
Figure 6J illustrates an example of a toolbar that allows a user to select
different vessels for review and analysis.
[0042]
Figure 6K illustrates an example of a series selection panel of the user
interface in an expanded view of the toolbar illustrated in Figure 6J, which
allows a user to
expand the menu to view all the series (set of images) that are available for
review and
analysis for a particular patient.
100431
Figure 6L illustrates an example of a selection panel that can be
displayed on the user interface that may be uses to select a vessel segment
for analysis.
[0044]
Figure 6M illustrates an example of a panel that can be displayed on the
user interface to add a new vessel on the image.
[0045]
Figure 6N illustrates examples of two panels that can be displayed on
the user interface to name, or to rename, a vessel in the 3-D artery tree
view.
[0046]
Figure 7A illustrates an example of an editing toolbar which allows
users to modify and improve the accuracy of the findings resulting from
processing CT
scans with a machine learning algorithm and then by an analyst.
[0047]
Figures 7B and 7C illustrate examples of certain functionality of the
tracker tool.
[0048]
Figures 7D and 7E illustrate certain functionality of the vessel and
lumen wall tools, which are used to modify the lumen and vessel wall contours.
[0049]
Figure 7F illustrates the lumen snap tool button (left) in the vessel snap
tool button (right) on a user interface which can be used to activate these
tools.
[0050]
Figure 7G illustrates an example of a panel that can be displayed on the
user interface while using the lumen snap tool in the vessel snap tool.
[0051]
Figure 7H illustrates an example of a panel of the user interface that can
be displayed while using the segment tool which allows for marking the
boundaries
between individual coronary segments on the MPR.
[0052]
Figure 71 illustrates an example of a panel of the user interface that
allows a different name to be selected for a segment.
[0053]
Figure 7J illustrates an example of a panel of the user interface that can
be displayed while using the stenosis tool, which allows a user to indicate
markers to mark
areas of stenosis on a vessel.
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[0054]
Figure 7K illustrates an example of a stenosis button of the user interface
which can be used to drop five evenly spaced stenosis markers.
[0055]
Figure 7L illustrates an example of a stenosis button of the user interface
which can be used to drop stenosis markers based on the user edited lumen and
vessel wall
contours.
[0056]
Figure 7M illustrates the stenosis markers on segments on a curved
multiplanar vessel (CMPR) view.
[0057]
Figure 7N illustrates an example of a panel of the user interface that can
be displayed while using the plaque overlay tool.
100581
Figures 70 and 7P illustrate a button on the user interface that can be
selected to the plaque thresholds.
[0059]
Figure 7Q illustrates a panel of the user interface which can receive a
user input to adjust plaque threshold levels for low-density plaque, non-
calcified plaque,
and calcified plaque.
[0060]
Figure 7R illustrates a cross-sectional view of a vessel indicating areas
of plaque which are displayed in the user interface in accordance with the
plaque thresholds.
[0061]
Figure 7S illustrates a panel can be displayed showing plaque thresholds
in a vessel statistics panel that includes information on the vessel being
viewed.
[0062]
Figure 7T illustrates a panel showing a cross-sectional view of a vessel
that can be displayed while using the centerline tool, which allows adjustment
of the center
of the lumen.
[0063]
Figures 7U, 7V, 7W illustrate examples of panels showing other views
of a vessel that can be displayed when using the centerline tool. Figure 7U is
an example
of a view that can be displayed when extending the centerline of a vessel.
Figure 7V
illustrates an example of a view that can be displayed when saving or
canceling centerline
edits. Figure 7W is an example of a CMPR view that can be displayed when
editing the
vessel centerline.
[0064]
Figure 7X illustrates an example of a panel that can be displayed while
using the chronic total occlusion (CTO) tool, which is used to indicate a
portion of artery
with 100% stenosis and no detectable blood flow.
[0065]
Figure 7Y illustrates an example of a panel that can be displayed while
using the stent tool, which allows a user to mark the extent of a stent in a
vessel.
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[0066] Figures 7Z and 7AA illustrates examples of panels
that can be displayed
while using the exclude tool, which allows a portion of the vessel to be
excluded from the
analysis, for example, due to image aberrations. A row
[0067] Figures 7AB and 7AC illustrate examples of
additional panels that can
be displayed while using the exclude tool. Figure 7 AB illustrates a panel
that can be used
to add a new exclusion. Figure 7AC illustrates a panel that can be used to add
a reason for
the exclusion.
[0068] Figures 7AD, 7AE, 7AF, and 7AG illustrate examples
of panels that can
be displayed while using the distance tool, which can be used to measure the
distance
between two points on an image. For example, Figure 7AD illustrates the
distance tool
being used to measure a distance on an SMPR view. Figure 7AE illustrates the
distance
tool being used to measure a distance on an CMPR view. Figure 7AF illustrates
the distance
will be used to measure a distance on a cross-sectional view of the vessel.
Figure 7AG
illustrates the distance tool being used to measure a distance on an axial
view.
[0069] Figure 7AH illustrates a "vessel statistics-
portion (button) of a panel
which can be selected to display the vessel statistics tab.
[0070] Figure 7A1 illustrates the vessel statistics tab.
[0071] Figure 7AJ illustrates functionality on the vessel
statistics tab that allows
a user to click through the details of multiple lesions.
[0072] Figure 7AK further illustrates an example of the
vessel panel which the
user can use to toggle between vessels_
[0073] Figure 8A illustrates an example of a panel of the
user interface that
shows stenosis, atherosclerosis, and CAD-RADS results of the analysis.
100741 Figure 8B illustrates an example of a portion of a
panel displayed on the
user interface that allows selection of a territory or combination of
territories (e.g., left main
artery (LM), left anterior descending artery (LAD), left circumflex artery
(LCx), right
coronary artery (RCA), according to various embodiments.
[0075] Figure 8C illustrates an example of a panel that
can be displayed on the
user interface showing a cartoon representation of a coronary artery tree
("cartoon artery
tree-).
[0076] Figure 8D illustrates an example of a panel that
can be displayed on the
user interface illustrating territory selection using the cartoon artery tree.
[0077] Figure 8E illustrates an example panel that can be
displayed on the user
interface showing per-territory summaries.
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[0078]
Figure 8F illustrates an example panel that can be displayed on the user
interface showing a SMPR view of a selected vessel, and corresponding
statistics of the
selected vessel.
[0079]
Figure 8G illustrates an example of a portion of a panel that can be
displayed in the user interface indicating the presence of a stent, which is
displayed at the
segment level.
[0080]
Figure 8H illustrates an example of a portion of a panel that can be
displayed in the user interface indicating CTO presence at the segment level.
100811
Figure 81 illustrates an example of a portion of a panel that can be
displayed in the user interface indicating left or right dominance of the
patient.
[0082]
Figure 8J illustrates an example of a panel that can be displayed on the
user interface showing cartoon artery tree with indications of anomalies that
were found.
[0083]
Figure 8K illustrates an example of a portion of a panel that can be
displayed on the panel of Figure Si that can be selected to show details of an
anomaly.
[0084]
Figure 9A illustrates an example of an atherosclerosis panel that can be
displayed on the user interface which displays a summary of atherosclerosis
information
based on the analysis.
[0085]
Figure 9B illustrates an example of a vessel selection panel which can
be used to select a vessel such that the summary of atherosclerosis
information is displayed
on a per segment basis.
[0086]
Figure 9C illustrates an example of a panel that can be displayed on the
user interface which shows per segment atherosclerosis information.
[0087]
Figure 9D illustrates an example of a panel that can be displayed on the
user interface that contains stenosis per patient data.
[0088]
Figure 9E illustrates an example of a portion of a panel that can be
displayed on the user interface that when a count is selected (e.g., by
hovering over the
number) segment details are displayed.
[0089]
Figure 9F illustrates an example of a portion of a panel that can be
displayed on the user interface that shows stenosis per segment in a graphical
format, for
example, in a stenosis per segment bar graph.
[0090]
Figure 9G illustrates another example of a panel that can be displayed
on the user interface showing information of the vessel, for example, diameter
stenosis and
minimum luminal diameter.
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[0091]
Figure 9H illustrates an example of a portion of a panel that can be
displayed on the user interface indicating a diameter stenosis legend.
[0092]
Figure 91 illustrates an example of a panel that can be displayed on the
user interface indicating minimum and reference lumen diameters.
[0093]
Figure 9J illustrates a portion of the panel shown in Figure 91, and shows
how specific minimum lumen diameter details can be quickly and efficiently
displayed by
selecting (e.g., by hovering over) a desired graphic of a lumen.
[0094]
Figure 9K illustrates an example of a panel that can be displayed in user
interface indicating CADS-RADS score selection.
100951
Figure 9L illustrates an example of a panel that can be displayed in the
user interface showing further CAD-RADS details generated in the analysis.
[0096]
Figure 9M illustrates an example of a panel that can be displayed in the
user interface showing a table indicating quantitative stenosis and vessel
outputs which are
determined during the analysis.
[0097]
Figure 9N illustrates an example of a panel that can be displayed in the
user interface showing a table indicating quantitative plaque outputs.
[0098]
Figure 10 is a flowchart illustrating a process 1000 for analyzing and
displaying CT images and corresponding information.
[0099]
Figures 11A and 11B are example CT images illustrating how plaque
can appear differently depending on the image acquisition parameters used to
capture the
CT images. Figure 11A illustrates a CT image reconstructed using filtered back
projection,
while Figure 11B illustrates the same CT image reconstructed using iterative
reconstruction.
101001
Figures 11C and 11D provide another example that illustrates that
plaque can appear differently in CT images depending on the image acquisition
parameters
used to capture the CT images. Figure 11C illustrates a CT image reconstructed
by using
iterative reconstruction, while Figure 11D illustrates the same image
reconstructed using
machine learning.
[0101]
Figure 12A is a block diagram representative of an embodiment of a
normalization device that can be configured to normalize medical images for
use with the
methods and systems described herein.
[0102]
Figure 12B is a perspective view of an embodiment of a normalization
device including a multilayer substrate.
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[0103] Figure 12C is a cross-sectional view of the
normalization device of
Figure 12B illustrating various compartments positioned therein for holding
samples of
known materials for use during normalization.
[0104] Figure 12D illustrates a top down view of an
example arrangement of a
plurality of compartments within a normalization device. In the illustrated
embodiment,
the plurality of compartments are arranged in a rectangular or grid-like
pattern.
[0105] Figure 12E illustrates a top down view of another
example arrangement
of a plurality of compartments within a normalization device. In the
illustrated
embodiment, the plurality of compartments are arranged in a circular pattern.
101061 Figure 12F is a cross-sectional view of another
embodiment of a
normalization device illustrating various features thereof, including
adjacently arranged
compartments, self-sealing fillable compartments, and compartments of various
sizes.
[0107] Figure 12G is a perspective view illustrating an
embodiment of an
attachment mechanism for a normalization device that uses hook and loop
fasteners to
secure a substrate of the normalization device to a fastener of the
normalization device.
[0108] Figures 12H and 121 illustrate an embodiment of a
normalization device
that includes an indicator configured to indicate an expiration status of the
normalization
device.
[0109] Figure 121 is a flowchart illustrating an example
method for normalizing
medical images for an algorithm-based medical imaging analysis, wherein
normalization
of the medical images improves accuracy of the algorithm-based medical imaging
analysis.
[0110] Figure 13 is a block diagram depicting an
embodiment(s) of a system
for medical image analysis, visualization, risk assessment, disease tracking,
treatment
generation, and/or patient report generation.
[0111] Figure 14 is a block diagram depicting an
embodiment(s) of a computer
hardware system configured to run software for implementing one or more
embodiments
of a system for medical image analysis, visualization, risk assessment,
disease tracking,
treatment generation, and/or patient report generation.
[0112] Figure 15 illustrates an embodiment of a
normalization device.
[0113] Figure 16 is a system diagram which shows various
components of an
example of a system for automatically generating patient medical reports, for
example,
patient medical reports based on CT scans and analysis, utilizing certain
systems and
methods described herein.
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[0114]
Figure 17 is a block diagram that shows an example of data flow
functionality for generating the patient medical report based on one or more
scans of the
patient, patient information, medical practitioner's analysis of the scans,
and/or previous
test results.
[0115]
Figure 18A is a block diagram of a first portion of a process for
generating medical report using the functionality and data described in
reference to Figure
2, according to some embodiments.
[0116]
Figure 18B is a block diagram of a second portion of a process for
generating medical report using the functionality and data described in
reference to Figure
2, according to some embodiments.
[0117]
Figure 18C is a block diagram of a third portion of a process for
generating medical report using the functionality and data described in
reference to Figure
2, according to some embodiments.
[0118]
Figure 18D is a diagram illustrating various portions that can make up
the medical report, and input can be provided by the medical practitioner and
by patient
information or patient input.
[0119]
Figure 18E is a schematic illustrating an example of a medical report
generation data flow and communication of data used to generate a report.
[0120]
Figure 18F is a diagram illustrating multiple structures for storing
information that is used in a medical report, the information associated with
a patient based
on one or more characteristics of the patient, the patient's medical
condition, and/or the
input from the patient or a medical practitioner.
[0121]
Figure 19A illustrates an example of a process for determining a risk
assessment using sequential imaging of noncontiguous arterial beds of a
patient, according
to some embodiments.
[0122]
Figure 19B illustrates an example where sequential noncontiguous
arterial bed imaging is performed for the coronary arteries.
[0123]
Figure 19C is an example of a process for determining a risk assessment
using sequential imaging of non-contiguous arterial beds, according to some
embodiments.
[0124]
Figure 19D is an example of a process for determining a risk assessment
using sequential imaging of non-contiguous arterial beds, according to some
embodiments.
[0125]
Figure 19E is a block diagram depicting an embodiment of a computer
hardware system configured to run software for implementing one or more
embodiments
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of systems and methods for determining a risk assessment using sequential
imaging of
noncontiguous arterial beds of a patient.
[0126]
Figure 20A illustrates one or more features of an example ischemic
pathway.
[0127]
Figure 20B is a block diagram depicting one or more contributors and
one or more temporal sequences of consequences of ischemia utilized by an
example
embodiment(s) described herein.
[0128]
Figure 20C is a block diagram depicting one or more features of an
example embodiment(s) for determining ischemia by weighting different factors
differently.
[0129]
Figure 20D is a block diagram depicting one or more features of an
example embodiment(s) for calculating a global ischemia index.
[0130]
Figure 20E is a flowchart illustrating an overview of an example
embodiment(s) of a method for generating a global ischemia index for a subject
and using
the same to assist assessment of risk of ischemia for the subject.
[0131]
Figure 21 is a flowchart illustrating an overview of an example
embodiment(s) of a method for generating a coronary artery disease (CAD)
Score(s) for a
subject and using the same to assist assessment of risk of CAD for the
subject.
[0132]
Figure 22A illustrates an example(s) of tracking the attenuation of
plaque for analysis and/or treatment of coronary artery and/or other vascular
disease.
[0133]
Figure 22B is a flowchart illustrating an overview of an example
embodiment(s) of a method for treating to the image.
[0134]
Figure 23A illustrates an example embodiment(s) of systems and
methods for determining treatments for reducing cardiovascular risk and/or
events.
[0135]
FIGS. 23B-C illustrate an example embodiment(s) of definitions or
categories of atherosclerosis severity used by an example embodiment(s) of
systems and
methods for determining treatments for reducing cardiovascular risk and/or
events.
[0136]
Figure 23D illustrates an example embodiment(s) of definitions or
categories of disease progression, stabilization, and/or regression used by an
example
embodiment(s) of systems and methods for determining treatments for reducing
cardiovascular risk and/or events.
[0137]
Figure 23E illustrates an example embodiment(s) of a time-to-treatment
goal(s) for an example embodiment(s) of systems and methods for determining
treatments
for reducing cardiovascular risk and/or events.
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[0138] Figures 23F-G illustrate an example embodiment(s)
of a treatment(s)
employing lipid lowering medication(s) and/or treatment(s) generated by an
example
embodiment(s) of systems and methods for determining treatments for reducing
cardiovascular risk and/or events.
[0139] Figures 23H-I illustrate an example embodiment(s)
of a treatment(s)
employing diabetic medication(s) and/or treatment(s) generated by an example
embodiment(s) of systems and methods for determining treatments for reducing
cardiovascular risk and/or events.
[0140] Figure 23J is a flowchart illustrating an overview
of an example
embodiment(s) of a method for determining treatments for reducing
cardiovascular risk
and/or events.
[0141] Figure 24A is a schematic illustration of an
artery.
[0142] Figure 24B illustrates an embodiment(s) of
determining percentage
stenosis and remodeling index.
[0143] Figure 24C is a schematic illustration of an
artery.
[0144] Figure 24D is a schematic illustration of an
artery with long
atherosclerotic regions of plaque.
[0145] Figure 24E is a example illustrating how an
inaccurately estimated RO
can significantly affect the resulting percent stenosis and/or remodeling
index.
[0146] Figure 24F is a schematic illustration of lumen
diameter v. outer wall
diameter
[0147] Figure 24G is a schematic illustration of
calculation of an estimated
reference diameter(s) along a vessel where plaque is present.
101481 Figure 24H is a schematic illustration of an
embodiment(s) of
determining volumetric stenosis.
[0149] Figure 241 is a schematic illustration of an
embodiment(s) of
determining volumetric stenosis.
[0150] Figure 241 is a schematic illustration of an
embodiment(s) of
determining volumetric remodeling.
[0151] Figure 24K illustrates an embodiment(s) of
coronary vessel blood
volume assessment based on total coronary volume.
[0152] Figure 24L illustrates an embodiment(s) of
coronary vessel blood
volume assessment based on territory or artery-specific volume.
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[0153]
Figure 24M illustrates an embodiment(s) of coronary vessel blood
volume assessment based on within-artery % fractional blood volume.
[0154]
Figure 24N illustrates an embodiment(s) of assessment of coronary
vessel blood volume.
[0155]
Figure 240 illustrates an embodiment(s) of assessment of % vessel
volume stenosis as a measure of ischemia.
[0156]
Figure 24P illustrates an embodiment(s) of assessment of pressure
difference across a lesion as a measure of ischemia.
[0157]
Figure 24Q illustrates an embodiment(s) of application of the continuity
equation to coronary arteries.
[0158]
Figure 24R is a flowchart illustrating an overview of an example
embodiment(s) of a method for determining volumetric stenosis and/or
volumetric vascular
remodeling.
[0159]
Figure 24S is a flowchart illustrating an overview of an example
embodiment(s) of a method for determining ischemia.
[0160]
Figure 25A is a flowchart illustrating a process for determining an
indicator of risk that an atherosclerotic lesion will contribute to a
myocardial infarction or
other major adverse cardiovascular event.
[0161]
Figure 25B is schematic illustration of a human heart, illustrating certain
coronary arteries.
[0162]
Figure 25C is a flowchart illustrating a process for determining an
indicator of a myocardial risk posed by an atherosclerotic lesion.
[0163]
Figure 25D is a flowchart illustrating a process for determining an
indicator of a segmental myocardial risk posed by an atherosclerotic lesion.
[0164]
Figure 25E is a flowchart illustrating a process for determining a risk of
adverse clinical events caused by an atherosclerotic lesion.
[0165]
Figure 25F is a flowchart illustrating a process for updating a risk of
adverse clinical events caused by an atherosclerotic lesion.
[0166]
Figure 25G is a block diagram depicting an embodiment(s) of a
computer hardware system configured to run software for implementing one or
more
embodiments of systems, devices, and methods for determining a myocardial risk
factor
from image-based quantification and characterizations of coronary
atherosclerosis,
vascular morphology, and myocardium.
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[0167]
Figure 26 is a flowchart illustrating a process for analyzing a CFD-based
indication of ischemia using a characterization of atherosclerosis and
vascular morphology.
[0168]
Figure 27A is a block diagram illustrating an example embodiment(s) of
systems, devices, and methods for determining patient-specific and/or subject-
specific
coronary artery disease (CAD) risk factor goals from image-based quantified
pheno-typing
of atherosclerosis.
[0169]
Figure 27B is a block diagram of an example of a computing system that
can be used to implement the systems, processes, and methods described herein
relating to
functionality described in reference to Figure 27A.
101701
Figures 28A-28B illustrate an example embodiment of identification of
coronary and aortic disease / atherosclerosis identified on a coronary CT
angiogram
(CCTA) utilizing embodiments of the systems, devices, and methods described
herein.
[0171]
Figure 28C is a flowchart illustrating an example embodiment(s) of
systems, devices, and methods for image-based diagnosis, risk assessment,
and/or
characterization of a major adverse cardiovascular event.
[0172]
Figure 28D is a flowchart illustrating an example embodiment(s) of
systems, devices, and methods for image-based diagnosis, risk assessment,
and/or
characterization of a major adverse cardiovascular event.
[0173]
Figure. 28E is a flowchart illustrating an example embodiment(s) of
systems, devices, and methods for image-based diagnosis, risk assessment,
and/or
characterization of a major adverse cardiovascular event
[0174]
Figure 28F is a block diagram depicting an embodiment(s) of a
computer hardware system configured to run software for implementing one or
more
embodiments of systems, devices, and methods described herein.
[0175]
Figure 29A is a block diagram illustrating an example embodiment of a
system, device, and method for improving the accuracy of CAD measurements in
non-
invasive imaging; and
[0176]
Figure 29B is a block diagram depicting an embodiment(s) of a
computer hardware system configured to run software for improving the accuracy
of CAD
measurements in non-invasive imaging.
[0177]
Figure 30A is a block diagram illustrating an example embodiment of a
system, device, and method for longitudinal image-based phenotyping to enhance
drug
discovery or development.
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[0178]
Figure 30B is a block diagram depicting an embodiment of a computer
hardware system configured to run software for implementing one or more
embodiments
of systems, devices, and methods for determining patient-specific coronary
artery disease
(CAD) risk factor goals from image-based quantification and characterization
of coronary
atherosclerosis burden, type, and/or rate of progression.
DETAILED DESCRIPTION
[0179]
Although several embodiments, examples, and illustrations are
disclosed below, it will be understood by those of ordinary skill in the art
that the inventions
described herein extend beyond the specifically disclosed embodiments,
examples, and
illustrations and includes other uses of the inventions and obvious
modifications and
equivalents thereof. Embodiments of the inventions are described with
reference to the
accompanying figures, wherein like numerals refer to like elements throughout.
The
terminology used in the description presented herein is not intended to be
interpreted in any
limited or restrictive manner simply because it is being used in conjunction
with a detailed
description of certain specific embodiments of the inventions. In addition,
embodiments
of the inventions can comprise several novel features and no single feature is
solely
responsible for its desirable attributes or is essential to practicing the
inventions herein
described.
Introduction
[0180]
Disclosed herein are systems, methods, and devices for medical image
analysis, diagnosis, risk stratification, decision making and/or disease
tracking. Coronary
heart disease affects over 17.6 million Americans. The current trend in
treating
cardiovascular health issues is generally two-fold. First, physicians
generally review a
patient's cardiovascular health from a macro level, for example, by analyzing
the
biochemistry or blood content or biomarkers of a patient to determine whether
there are
high levels of cholesterol elements in the bloodstream of a patient. In
response to high
levels of cholesterol, some physicians will prescribe one or more drugs, such
as statins, as
part of a treatment plan in order to decrease what is perceived as high levels
of cholesterol
elements in the bloodstream of the patient.
[0181]
The second general trend for currently treating cardiovascular health
issues involves physicians evaluating a patient's cardiovascular health
through the use of
angiography to identify large blockages in various arteries of a patient. In
response to
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finding large blockages in various arteries, physicians in some cases will
perform an
angioplasty procedure wherein a balloon catheter is guided to the point of
narrowing in the
vessel. After properly positioned, the balloon is inflated to compress or
flatten the plaque
or fatty matter into the artery wall and/or to stretch the artery open to
increase the flow of
blood through the vessel and/or to the heart. In some cases, the balloon is
used to position
and expand a stent within the vessel to compress the plaque and/or maintain
the opening of
the vessel to allow more blood to flow. About 500,000 heart stent procedures
are performed
each year in the United States.
[0182]
However, a recent federally funded 5100 million study calls into
question whether the current trends in treating cardiovascular disease are the
most effective
treatment for all types of patients. The recent study involved over 5,000
patients with
moderate to severe stable heart disease from 320 sites in 37 countries and
provided new
evidence showing that stents and bypass surgical procedures are likely no more
effective
than drugs combined with lifestyle changes for people with stable heart
disease.
Accordingly, it may be more advantageous for patients with stable heart
disease to forgo
invasive surgical procedures, such as angioplasty and/or heart bypass, and
instead be
prescribed heart medicines, such as statins, and certain lifestyle changes,
such as regular
exercise. This new treatment regimen could affect thousands of patients
worldwide. Of
the estimated 500,000 heart stent procedures performed annually in the United
States, it is
estimated that a fifth of those are for people with stable heart disease. It
is further estimated
that 25% of the estimated 100,000 people with stable heart disease, or roughly
23,000
people, are individuals that do not experience any chest pain. Accordingly,
over 20,000
patients annually could potentially forgo invasive surgical procedures or the
complications
resulting from such procedures.
[0183]
To determine whether a patient should forego invasive surgical
procedures and opt instead for a drug regimen and/or to generate a more
effective treatment
plan, it can be important to more fully understand the cardiovascular disease
of a patient.
Specifically, it can be advantageous to better understand the arterial vessel
health of a
patient. For example, it is helpful to understand whether plaque build-up in a
patient is
mostly fatty matter build-up or mostly calcified matter build-up, because the
former
situation may warrant treatment with heart medicines, such as statins, whereas
in the latter
situation a patient should be subject to further periodic monitoring without
prescribing heart
medicine or implanting any stents. However, if the plaque build-up is
significant enough
to cause severe stenosis or narrowing of the arterial vessel such that blood
flow to heart
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muscle might be blocked, then an invasive angioplasty procedure to implant a
stent may
likely be required because heart attack or sudden cardiac death (SCD) could
occur in such
patients without the implantation of a stent to enlarge the vessel opening.
Sudden cardiac
death is one of the largest causes of natural death in the United States,
accounting for
approximately 325,000 adult deaths per year and responsible for nearly half of
all deaths
from cardiovascular disease. For males, SCD is twice as common as compared to
females.
In general, SCD strikes people in the mid-30 to mid-40 age range. In over 50%
of cases,
sudden cardiac arrest occurs with no warning signs.
[0184]
With respect to the millions suffering from heart disease, there is a need
to better understand the overall health of the artery vessels within a patient
beyond just
knowing the blood chemistry or content of the blood flowing through such
artery vessels.
For example, in some embodiments of systems, devices, and methods disclosed
herein,
arteries with -good- or stable plaque or plaque comprising hardened calcified
content are
considered non-life threatening to patients whereas arteries containing -bad"
or unstable
plaque or plaque comprising fatty material are considered more life
threatening because
such bad plaque may rupture within arteries thereby releasing such fatty
material into the
arteries. Such a fatty material release in the blood stream can cause
inflammation that may
result in a blood clot. A blood clot within an artery can prevent blood from
traveling to
heart muscle thereby causing a heart attack or other cardiac event. Further,
in some
instances, it is generally more difficult for blood to flow through fatty
plaque buildup than
it is for blood to flow through calcified plaque build-up. Therefore, there is
a need for
better understanding and analysis of the arterial vessel walls of a patient.
[0185]
Further, while blood tests and drug treatment regimens are helpful in
reducing cardiovascular health issues and mitigating against cardiovascular
events (for
example, heart attacks), such treatment methodologies are not complete or
perfect in that
such treatments can misidentify and/or fail to pinpoint or diagnose
significant
cardiovascular risk areas. For example, the mere analysis of the blood
chemistry of a
patient will not likely identify that a patient has artery vessels having
significant amounts
of fatty deposit material bad plaque buildup along a vessel wall. Similarly,
an angiogram,
while helpful in identifying areas of stenosis or vessel narrowing, may not be
able to clearly
identify areas of the artery vessel wall where there is significant buildup of
bad plaque.
Such areas of buildup of bad plaque within an artery vessel wall can be
indicators of a
patient at high risk of suffering a cardiovascular event, such as a heart
attack. In certain
circumstances, areas where there exist areas of bad plaque can lead to a
rupture wherein
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there is a release of the fatty materials into the bloodstream of the artery,
which in turn can
cause a clot to develop in the artery. A blood clot in the artery can cause a
stoppage of
blood flow to the heart tissue, which can result in a heart attack.
Accordingly, there is a
need for new technology for analyzing artery vessel walls and/or identifying
areas within
artery vessel walls that comprise a buildup of plaque whether it be bad or
otherwise.
[0186]
Various systems, methods, and devices disclosed herein are directed to
embodiments for addressing the foregoing issues. In particular, various
embodiments
described herein relate to systems, methods, and devices for medical image
analysis,
diagnosis, risk stratification, decision making and/or disease tracking.
In some
embodiments, the systems, devices, and methods described herein are configured
to utilize
non-invasive medical imaging technologies, such as a CT image for example,
which can
be inputted into a computer system configured to automatically and/or
dynamically analyze
the medical image to identify' one or more coronary arteries and/or plaque
within the same.
For example, in some embodiments, the system can be configured to utilize one
or more
machine learning and/or artificial intelligence algorithms to automatically
and/or
dynamically analyze a medical image to identify, quantify, and/or classify one
or more
coronary arteries and/or plaque. In some embodiments, the system can be
further
configured to utilize the identified, quantified, and/or classified one or
more coronary
arteries and/or plaque to generate a treatment plan, track disease
progression, and/or a
patient-specific medical report, for example using one or more artificial
intelligence and/or
machine learning algorithms. In some embodiments, the system can be further
configured
to dynamically and/or automatically generate a visualization of the
identified, quantified,
and/or classified one or more coronary arteries and/or plaque, for example in
the form of a
graphical user interface. Further, in some embodiments, to calibrate medical
images
obtained from different medical imaging scanners and/or different scan
parameters or
environments, the system can be configured to utilize a normalization device
comprising
one or more compartments of one or more materials.
[0187]
As will be discussed in further detail, the systems, devices, and methods
described herein allow for automatic and/or dynamic quantified analysis of
various
parameters relating to plaque, cardiovascular arteries, and/or other
structures. More
specifically, in some embodiments described herein, a medical image of a
patient, such as
a coronary CT image, can be taken at a medical facility. Rather than having a
physician
eyeball or make a general assessment of the patient, the medical image is
transmitted to a
backend main server in some embodiments that is configured to conduct one or
more
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analyses thereof in a reproducible manner. As such, in some embodiments, the
systems,
methods, and devices described herein can provide a quantified measurement of
one or
more features of a coronary CT image using automated and/or dynamic processes.
For
example, in some embodiments, the main server system can be configured to
identify one
or more vessels, plaque, and/or fat from a medical image. Based on the
identified features,
in some embodiments, the system can be configured to generate one or more
quantified
measurements from a raw medical image, such as for example radiodensity of one
or more
regions of plaque, identification of stable plaque and/or unstable plaque,
volumes thereof,
surface areas thereof, geometric shapes, heterogeneity thereof, and/or the
like. In some
embodiments, the system can also generate one or more quantified measurements
of vessels
from the raw medical image, such as for example diameter, volume, morphology,
and/or
the like. Based on the identified features and/or quantified measurements, in
some
embodiments, the system can be configured to generate a risk assessment and/or
track the
progression of a plaque-based disease or condition, such as for example
atherosclerosis,
stenosis, and/or ischemia, using raw medical images. Further, in some
embodiments, the
system can be configured to generate a visualization of GUI of one or more
identified
features and/or quantified measurements, such as a quantized color mapping of
different
features. In some embodiments, the systems, devices, and methods described
herein are
configured to utilize medical image-based processing to assess for a subject
his or her risk
of a cardiovascular event, major adverse cardiovascular event (MACE), rapid
plaque
progression, and/or non-response to medication. In particular, in some
embodiments, the
system can be configured to automatically and/or dynamically assess such
health risk of a
subject by analyzing only non-invasively obtained medical images. In some
embodiments,
one or more of the processes can be automated using an Al and/or ML algorithm.
In some
embodiments, one or more of the processes described herein can be performed
within
minutes in a reproducible manner. This is stark contrast to existing measures
today which
do not produce reproducible prognosis or assessment, take extensive amounts of
time,
and/or require invasive procedures.
[0188]
As such, in some embodiments, the systems, devices, and methods
described herein are able to provide physicians and/or patients specific
quantified and/or
measured data relating to a patient's plaque that do not exist today. For
example, in some
embodiments, the system can provide a specific numerical value for the volume
of stable
and/or unstable plaque, the ratio thereof against the total vessel volume,
percentage of
stenosis, and/or the like, using for example radiodensity values of pixels
and/or regions
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within a medical image. In some embodiments, such detailed level of quantified
plaque
parameters from image processing and downstream analytical results can provide
more
accurate and useful tools for assessing the health and/or risk of patients in
completely novel
ways.
General Overview
101891
In some embodiments, the systems, devices, and methods described
herein are configured to automatically and/or dynamically perform medical
image analysis,
diagnosis, risk stratification, decision making and/or disease tracking.
Figure 1 is a
flowchart illustrating an overview of an example embodiment(s) of a method for
medical
image analysis, visualization. risk assessment, disease tracking, treatment
generation,
and/or patient report generation. As illustrated in Figure 1, in some
embodiments, the
system is configured to access and/or analyze one or more medical images of a
subject,
such as for example a medical image of a coronary region of a subject or
patient.
[0190]
In some embodiments, before obtaining the medical image, a
normalization device is attached to the subject and/or is placed within a
field of view of a
medical imaging scanner at block 102. For example, in some embodiments, the
normalization device can comprise one or more compartments comprising one or
more
materials, such as water, calcium, and/or the like. Additional detail
regarding the
normalization device is provided below. Medical imaging scanners may produce
images
with different scalable radiodensities for the same object. This, for example,
can depend
not only on the type of medical imaging scanner or equipment used but also on
the scan
parameters and/or environment of the particular day and/or time when the scan
was taken.
As a result, even if two different scans were taken of the same subject, the
brightness and/or
darkness of the resulting medical image may be different, which can result in
less than
accurate analysis results processed from that image. To account for such
differences, in
some embodiments, a normalization device comprising one or more known elements
is
scanned together with the subject, and the resulting image of the one or more
known
elements can be used as a basis for translating, converting, and/or
normalizing the resulting
image. As such, in some embodiments, a normalization device is attached to the
subject
and/or placed within the field of view of a medical imaging scan at a medical
facility.
[0191]
In some embodiments, at block 104, the medical facility then obtains
one or more medical images of the subject. For example, the medical image can
be of the
coronary region of the subject or patient. In some embodiments, the systems
disclosed
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herein can be configured to take in CT data from the image domain or the
projection domain
as raw scanned data or any other medical data, such as but not limited to: x-
ray; Dual-
Energy Computed Tomography (DECT), Spectral CT, photon-counting detector CT,
ultrasound, such as echocardiography or intravascular ultrasound (IV US);
magnetic
resonance (MR) imaging; optical coherence tomography (OCT); nuclear medicine
imaging, including positron-emission tomography (PET) and single photon
emission
computed tomography (SPECT); near-field infrared spectroscopy (NIRS); and/or
the like.
As used herein, the term CT image data or CT scanned data can be substituted
with any of
the foregoing medical scanning modalities and process such data through an
artificial
intelligence (Al) algorithm system in order to generate processed CT image
data. In some
embodiments, the data from these imaging modalities enables determination of
cardiovascular phenotype, and can include the image domain data, the
projection domain
data, and/or a combination of both.
[0192]
In some embodiments, at block 106, the medical facility can also obtain
non-imaging data from the subject. For example, this can include blood tests,
biomarkers,
panomics and/or the like. In some embodiments, at block 108, the medical
facility can
transmit the one or more medical images and/or other non-imaging data at block
108 to a
main server system. In some embodiments, the main server system can be
configured to
receive and/or otherwise access the medical image and/or other non-imaging
data at block
110.
[0193]
In some embodiments, at block 112, the system can be configured to
automatically and/or dynamically analyze the one or more medical images which
can be
stored and/or accessed from a medical image database 100. For example, in some
embodiments, the system can be configured to take in raw CT image data and
apply an
artificial intelligence (Al) algorithm, machine learning (ML) algorithm,
and/or other
physics-based algorithm to the raw CT data in order to identify, measure,
and/or analyze
various aspects of the identified arteries within the CT data. In some
embodiments, the
inputting of the raw medical image data involves uploading the raw medical
image data
into cloud-based data repository system. In some embodiments, the processing
of the
medical image data involves processing the data in a cloud-based computing
system using
an Al and/or ML algorithm. in some embodiments, the system can be configured
to analyze
the raw CT data in about 1 minute, about 2 minutes, about 3 minutes, about 4
minutes,
about 5 minutes, about 6 minutes, about 7 minutes, about 8 minutes, about 9
minutes, about
minutes, about 15 minutes, about 20 minutes, about 30 minutes, about 35
minutes, about
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40 minutes, about 45 minutes, about 50 minutes, about 55 minutes, about 60
minutes,
and/or within a range defined by two of the aforementioned values.
[0194]
In some embodiments, the system can be configured to utilize a vessel
identification algorithm to identify and/or analyze one or more vessels within
the medical
image. In some embodiments, the system can be configured to utilize a coronary
artery
identification algorithm to identify and/or analyze one or more coronary
arteries within the
medical image. In some embodiments, the system can be configured to utilize a
plaque
identification algorithm to identify and/or analyze one or more regions of
plaque within the
medical image. In some embodiments, the vessel identification algorithm,
coronary artery
identification algorithm, and/or plaque identification algorithm comprises an
AT and/or ML
algorithm. For example, in some embodiments, the vessel identification
algorithm,
coronary artery identification algorithm, and/or plaque identification
algorithm can be
trained on a plurality of medical images wherein one or more vessels, coronary
arteries,
and/or regions of plaque are pre-identified. Based on such training, for
example by use of
a Convolutional Neural Network in some embodiments, the system can be
configured to
automatically and/or dynamically identify from raw medical images the presence
and/or
parameters of vessels, coronary arteries, and/or plaque.
[0195]
As such, in some embodiments, the processing of the medical image or
raw CT scan data can comprise analysis of the medical image or CT data in
order to
determine and/or identify the existence and/or nonexistence of certain artery
vessels in a
patient_ As a natural occurring phenomenon, certain arteries may be present in
certain
patients whereas such certain arteries may not exist in other patients.
[0196]
In some embodiments, at block 112, the system can be further
configured to analyze the identified vessels, coronary arteries, and/or
plaque, for example
using an AT and/or ML algorithm. In particular, in some embodiments, the
system can be
configured to determine one or more vascular morphology parameters, such as
for example
arterial remodeling, curvature, volume, width, diameter, length, and/or the
like. In some
embodiments, the system can be configured to determine one or more plaque
parameters,
such as for example volume, surface area, geometry, radiodensity, ratio or
function of
volume to surface area, heterogeneity index, and/or the like of one or more
regions of
plaque shown within the medical image. "Radiodensity" as used herein is a
broad term that
refers to the relative inability of electromagnetic relation (e.g., X-rays) to
pass through a
material. In reference to an image, radiodensity values refer to values
indicting a density
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in image data (e.g., film, print, or in an electronic format) where the
radiodensity values in
the image corresponds to the density of material depicted in the image.
[0197]
In some embodiments, at block 114, the system can be configured to
utilize the identified and/or analyzed vessels, coronary arteries, and/or
plaque from the
medical image to perform a point-in-time analysis of the subject. In some
embodiments,
the system can be configured to use automatic and/or dynamic image processing
of one or
more medical images taken from one point in time to identify and/or analyze
one or more
vessels, coronary arteries, and/or plaque and derive one or more parameters
and/or
classifications thereof. For example, as will be described in more detail
herein, in some
embodiments, the system can be configured to generate one or more
quantification metrics
of plaque and/or classify the identified regions of plaque as good or bad
plaque. Further,
in some embodiments, at block 114, the system can be configured to generate
one or more
treatment plans for the subject based on the analysis results. In some
embodiments, the
system can be configured to utilize one or more Al and/or ML algorithms to
identify and/or
analyze vessels or plaque, derive one or more quantification metrics and/or
classifications,
and/or generate a treatment plan.
[0198]
In some embodiments, if a previous scan or medical image of the subject
exists, the system can be configured to perform at block 126 one or more time-
based
analyses, such as disease tracking. For example, in some embodiments, if the
system has
access to one or more quantified parameters or classifications derived from
previous scans
or medical images of the subject, the system can be configured to compare the
same with
one or more quantified parameters or classifications derived from a current
scan or medical
image to determine the progression of disease and/or state of the subject.
101991
In some embodiments, at block 116, the system is configured to
automatically and/or dynamically generate a Graphical User Interface (GUI) or
other
visualization of the analysis results at block 116, which can include for
example identified
vessels, regions of plaque, coronary arteries, quantified metrics or
parameters, risk
assessment, proposed treatment plan, and/or any other analysis result
discussed herein. In
some embodiments, the system is configured to analyze arteries present in the
CT scan data
and display various views of the arteries present in the patient, for example
within 10-15
minutes or less. In contrast, as an example, conducting a visual assessment of
a CT to
identify stenosis alone, without consideration of good or bad plaque or any
other factor,
can take anywhere between 15 minutes to more than an hour depending on the
skill level,
and can also have substantial variability across radiologists and/or cardiac
imagers.
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[0200]
In some embodiments, at block 118, the system can be configured to
transmit the generated GUI or other visualization, analysis results, and/or
treatment to the
medical facility. In some embodiments, at block 120, a physician at the
medical facility
can then review and/or confirm and/or revise the generated GUI or other
visualization,
analysis results, and/or treatment.
[0201]
In some embodiments, at block 122, the system can be configured to
further generate and transmit a patient-specific medical report to a patient,
who can receive
the same at block 124. In some embodiments, the patient-specific medical
report can be
dynamically generated based on the analysis results derived from and/or other
generated
from the medical image processing and analytics. For example, the patient-
specific report
can include identified vessels, regions of plaque, coronary arteries,
quantified metrics or
parameters, risk assessment, proposed treatment plan, and/or any other
analysis result
discussed herein.
[0202]
In some embodiments, one or more of the process illustrated in Figure 1
can be repeated, for example for the same patient at a different time to track
progression of
a disease and/or the state of the patient.
Image Processing-Based Classification of Good v. Bad Plaque
[0203]
As discussed, in some embodiments, the systems, methods, and devices
described herein are configured to automatically and/or dynamically identify
and/or
classify good v. bad plaque or stable v. unstable plaque based on medical
image analysis
and/or processing. For example, in some embodiments, the system can be
configured to
utilize an Al and/or ML algorithm to identify areas in an artery that exhibit
plaque buildup
within, along, inside and/or outside the arteries. In some embodiments, the
system can be
configured to identify the outline or boundary of plaque buildup associated
with an artery
vessel wall. In some embodiments, the system can be configured to draw or
generate a line
that outlines the shape and configuration of the plaque buildup associated
with the artery.
In some embodiments, the system can be configured to identify whether the
plaque buildup
is a certain kind of plaque and/or the composition or characterization of a
particular plaque
buildup. In some embodiments, the system can be configured to characterize
plaque
binarily, ordinally and/or continuously. In some embodiments, the system can
be
configured to determine that the kind of plaque buildup identified is a "bad"
kind of plaque
due to the dark color or dark gray scale nature of the image corresponding to
the plaque
area, and/or by determination of its attenuation density (e.g., using a
Hounsfi el d unit scale
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or other). For example, in some embodiments, the system can be configured to
identify
certain plaque as "bad" plaque if the brightness of the plaque is darker than
a pre-
determined level. In some embodiments, the system can be configured to
identify good
plaque areas based on the white coloration and/or the light gray scale nature
of the area
corresponding to the plaque buildup. For example, in some embodiments, the
system can
be configured to identify certain plaque as "good- plaque if the brightness of
the plaque is
lighter than a pre-determined level. In some embodiments, the system can be
configured
to determine that dark areas in the CT scan are related to "bad" plaque,
whereas the system
can be configured to identify good plaque areas corresponding to white areas.
In some
embodiments, the system can be configured to identify and determine the total
area and/or
volume of total plaque, good plaque, and/or bad plaque identified within an
artery vessel
or plurality of vessels. In some embodiments, the system can be configured to
determine
the length of the total plaque area, good plaque area, and/or bad plaque area
identified. In
some embodiments, the system can be configured to determine the width of the
total plaque
area, good plaque area, and/or bad plaque area identified. The "good- plaque
may be
considered as such because it is less likely to cause heart attack, less
likely to exhibit
significant plaque progression, and/or less likely to be ischemia, amongst
others.
Conversely, the -bad" plaque be considered as such because it is more likely
to cause heart
attack, more likely to exhibit significant plaque progression, and/or more
likely to be
ischemia, amongst others. In some embodiments, the "good- plaque may be
considered as
such because it is less likely to result in the no-reflow phenomenon at the
time of coronary
revascularization. Conversely, the "bad" plaque may be considered as such
because it is
more likely to cause the no-reflow phenomenon at the time of coronary
revascularization.
102041
Figure 2A is a flowchart illustrating an overview of an example
embodiment(s) of a method for analysis and classification of plaque from a
medical image,
which can be obtained non-invasively. As illustrated in Figure 2A, at block
202, in some
embodiments, the system can be configured to access a medical image, which can
include
a coronary region of a subject and/or be stored in a medical image database
100. The
medical image database 100 can be locally accessible by the system and/or can
be located
remotely and accessible through a network connection. The medical image can
comprise
an image obtain using one or more modalities such as for example, CT, Dual-
Energy
Computed Tomography (DECT), Spectral CT, photon-counting CT, x-ray,
ultrasound,
echocardiography, intravascular ultrasound (IVUS), Magnetic Resonance (MR)
imaging,
optical coherence tomography (OCT), nuclear medicine imaging, positron-
emission
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tomography (PET), single photon emission computed tomography (SPECT), or near-
field
infrared spectroscopy (NIRS). In some embodiments, the medical image comprises
one or
more of a contrast-enhanced CT image, non-contrast CT image, MR image, and/or
an
image obtained using any of the modalities described above.
[0205]
In some embodiments, the system can be configured to automatically
and/or dynamically perform one or more analyses of the medical image as
discussed herein.
For example, in some embodiments, at block 204, the system can be configured
to identify
one or more arteries. The one or more arteries can include coronary arteries,
carotid
arteries, aorta, renal artery, lower extremity artery, upper extremity artery,
and/or cerebral
artery, amongst others. In some embodiments, the system can be configured to
utilize one
or more Al and/or ML algorithms to automatically and/or dynamically identify
one or more
arteries or coronary arteries using image processing. For example, in some
embodiments,
the one or more Al and/or ML algorithms can be trained using a Convolutional
Neural
Network (CNN) on a set of medical images on which arteries or coronary
arteries have
been identified, thereby allowing the Al and/or ML algorithm automatically
identify
arteries or coronary arteries directly from a medical image. In some
embodiments, the
arteries or coronary arteries are identified by size and/or location.
[0206]
In some embodiments, at block 206, the system can be configured to
identify one or more regions of plaque in the medical image. In some
embodiments, the
system can be configured to utilize one or more Al and/or ML algorithms to
automatically
and/or dynamically identify one or more regions of plaque using image
processing. For
example, in some embodiments, the one or more Al and/or ML algorithms can be
trained
using a Convolutional Neural Network (CNN) on a set of medical images on which
regions
of plaque have been identified, thereby allowing the AT and/or ML algorithm
automatically
identify regions of plaque directly from a medical image. In some embodiments,
the system
can be configured to identify a vessel wall and a lumen wall for each of the
identified
coronary arteries in the medical image. In some embodiments, the system is
then
configured to determine the volume in between the vessel wall and the lumen
wall as
plaque. In some embodiments, the system can be configured to identify regions
of plaque
based on the radiodensity values typically associated with plaque, for example
by setting a
predetermined threshold or range of radiodensity values that are typically
associated with
plaque with or without normalizing using a normalization device.
[0207]
In some embodiments, the system is configured to automatically and/or
dynamically determine one or more vascular morphology parameters and/or plaque
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parameters at block 208 from the medical image. In some embodiments, the one
or more
vascular morphology parameters and/or plaque parameters can comprise
quantified
parameters derived from the medical image. For example, in some embodiments,
the
system can be configured to utilize an Al and/or ML algorithm or other
algorithm to
determine one or more vascular morphology parameters and/or plaque parameters.
As
another example, in some embodiments, the system can be configured to
determine one or
more vascular morphology parameters, such as classification of arterial
remodeling due to
plaque, which can further include positive arterial remodeling, negative
arterial
remodeling, and/or intermediate arterial remodeling. In some embodiments, the
classification of arterial remodeling is determined based on a ratio of the
largest vessel
diameter at a region of plaque to a normal reference vessel diameter of the
same region
which can be retrieved from a normal database. In some embodiments, the system
can be
configured to classify arterial remodeling as positive when the ratio of the
largest vessel
diameter at a region of plaque to a normal reference vessel diameter of the
same region is
more than 1.1. In some embodiments, the system can be configured to classify
arterial
remodeling as negative when the ratio of the largest vessel diameter at a
region of plaque
to a normal reference vessel diameter is less than 0.95. In some embodiments,
the system
can be configured to classify arterial remodeling as intermediate when the
ratio of the
largest vessel diameter at a region of plaque to a normal reference vessel
diameter is
between 0.95 and 1.1.
[0208]
Further, as part of block 208, in some embodiments, the system can be
configured to determine a geometry and/or volume of one or more regions of
plaque and/or
one or more vessels or arteries at block 201. For example, the system can be
configured to
determine if the geometry of a particular region of plaque is round or oblong
or other shape.
In some embodiments, the geometry of a region of plaque can be a factor in
assessing the
stability of the plaque. As another example, in some embodiments, the system
can be
configured to determine the curvature, diameter, length, volume, and/or any
other
parameters of a vessel or artery from the medical image.
[0209]
In some embodiments, as part of block 208, the system can be
configured to determine a volume and/or surface area of a region of plaque
and/or a ratio
or other function of volume to surface area of a region of plaque at block
203, such as for
example a diameter, radius, and/or thickness of a region of plaque. In some
embodiments,
a plaque having a low ratio of volume to surface area can indicate that the
plaque is stable.
As such, in some embodiments, the system can be configured to determine that a
ratio of
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volume to surface area of a region of plaque below a predetermined threshold
is indicative
of stable plaque.
[0210]
In some embodiments, as part of block 208, the system can be
configured to determine a heterogeneity index of a region of plaque at block
205. For
instance, in some embodiments, a plaque having a low heterogeneity or high
homogeneity
can indicate that the plaque is stable. As such, in some embodiments, the
system can be
configured to determine that a heterogeneity of a region of plaque below a
predetermined
threshold is indicative of stable plaque. In some embodiments, heterogeneity
or
homogeneity of a region of plaque can be determined based on the heterogeneity
or
homogeneity of radiodensity values within the region of plaque. As such, in
some
embodiments, the system can be configured to determine a heterogeneity index
of plaque
by generating spatial mapping, such as a three-dimensional histogram, of
radiodensity
values within or across a geometric shape or region of plaque. In some
embodiments, if a
gradient or change in radiodensity values across the spatial mapping is above
a certain
threshold, the system can be configured to assign a high heterogeneity index.
Conversely,
in some embodiments, if a gradient or change in radiodensity values across the
spatial
mapping is below a certain threshold, the system can be configured to assign a
low
heterogeneity index.
[0211]
In some embodiments, as part of block 208, the system can be
configured to determine a radiodensity of plaque and/or a composition thereof
at block 207.
For example, a high radiodensity value can indicate that a plaque is highly
calcified or
stable, whereas a low radiodensity value can indicate that a plaque is less
calcified or
unstable. As such, in some embodiments, the system can be configured to
determine that
a radiodensity of a region of plaque above a predetermined threshold is
indicative of stable
stabilized plaque. In addition, different areas within a region of plaque can
be calcified at
different levels and thereby show different radiodensity values. As such, in
some
embodiments, the system can be configured to determine the radiodensity values
of a region
of plaque and/or a composition or percentage or change of radiodensity values
within a
region of plaque. For instance, in some embodiments, the system can be
configured to
determine how much or what percentage of plaque within a region of plaque
shows a
radiodensity value within a low range, medium range, high range, and/or any
other
classification.
[0212]
Similarly, in some embodiments, as part of block 208, the system can be
configured to determine a ratio of radiodensity value of plaque to a volume of
plaque at
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block 209. For instance, it can be important to assess whether a large or
small region of
plaque is showing a high or low radiodensity value. As such, in some
embodiments, the
system can be configured to determine a percentage composition of plaque
comprising
different radiodensity values as a function or ratio of volume of plaque.
[0213]
In some embodiments, as part of block 208, the system can be
configured to determine the diffusivity and/or assign a diffusivity index to a
region of
plaque at block 211. For example, in some embodiments, the diffusivity of a
plaque can
depend on the radiodensity value of plaque, in which a high radiodensity value
can indicate
low diffusivity or stability of the plaque.
102141
In some embodiments, at block 210, the system can be configured to
classify one or more regions of plaque identified from the medical image as
stable v.
unstable or good v. bad based on the one or more vascular morphology
parameters and/or
quantified plaque parameters determined and/or derived from raw medical
images. In
particular, in some embodiments, the system can be configured to generate a
weighted
measure of one or more vascular morphology parameters and/or quantified plaque
parameters determined and/or derived from raw medical images. For example, in
some
embodiments, the system can be configured to weight one or more vascular
morphology
parameters and/or quantified plaque parameters equally. In some embodiments,
the system
can be configured to weight one or more vascular morphology parameters and/or
quantified
plaque parameters differently. In some embodiments, the system can be
configured to
weight one or more vascular morphology parameters and/or quantified plaque
parameters
logarithmically, algebraically, and/or utilizing another mathematical
transform. In some
embodiments, the system is configured to classify one or more regions of
plaque at block
210 using the generated weighted measure and/or using only some of the
vascular
morphology parameters and/or quantified plaque parameters.
[0215]
In some embodiments, at block 212, the system is configured to generate
a quantized color mapping based on the analyzed and/or determined parameters.
For
example, in some embodiments, the system is configured to generate a
visualization of the
analyzed medical image by generating a quantized color mapping of calcified
plaque, non-
calcified plaque, good plaque, bad plaque, stable plaque, and/or unstable
plaque as
determined using any of the analytical techniques described herein. Further,
in some
embodiments, the quantified color mapping can also include arteries and/or
epicardial fat,
which can also be determined by the system, for example by utilizing one or
more Al and/or
ML algorithms.
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[0216]
In some embodiments, at block 214, the system is configured to generate
a proposed treatment plan for the subject based on the analysis, such as for
example the
classification of plaque derived automatically from a raw medical image. In
particular, in
some embodiments, the system can be configured to assess or predict the risk
of
atherosclerosis, stenosis, and/or ischemia of the subject based on a raw
medical image and
automated image processing thereof
[0217]
In some embodiments, one or more processes described herein in
connection with Figure 2A can be repeated. For example, if a medical image of
the same
subject is taken again at a later point in time, one or more processes
described herein can
be repeated and the analytical results thereof can be used for disease
tracking and/or other
purposes.
Determination of Non-Calcified Plaque from a Non-Contrast CT Image(s)
[0218]
As discussed herein, in some embodiments, the system can be
configured to utilize a CT or other medical image of a subject as input for
performing one
or more image analysis techniques to assess a subject, including for example
risk of a
cardiovascular event. In some embodiments, such CT image can comprise a
contrast-
enhanced CT image, in which case some of the analysis techniques described
herein can be
directly applied, for example to identify or classify plaque. However, in some
embodiments, such CT image can comprise a non-contrast CT image, in which case
it can
be more difficult to identify and/or determine non-calcified plaque due to its
low
radiodensity value and overlap with other low radiodensity values components,
such as
blood for example. As such, in some embodiments, the systems, devices, and
methods
described herein provide a novel approach to determining non-calcified plaque
from a non-
contrast CT image, which can be more widely available.
[0219]
Also, in some embodiments, in addition to or instead of analyzing a
contrast-enhanced CT scan, the system can also be configured to examine the
attenuation
densities within the arteries that are lower than the attenuation density of
the blood flowing
within them in a non-contrast CT scan. In some embodiments, these "low
attenuation"
plaques may be differentiated between the blood attenuation density and the
fat that
sometimes surrounds the coronary artery and/or may represent non-calcified
plaques of
different materials. In some embodiments, the presence of these non-calcified
plaques may
offer incremental prediction for whether a previously calcified plaque is
stabilizing or
worsening or progressing or regressing. These findings that are measurable
through these
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embodiments may be linked to the prognosis of a patient, wherein calcium
stabilization
(that is, higher attenuation densities) and lack of non-calcified plaque by
may associated
with a favorable prognosis, while lack of calcium stabilization (that is, no
increase in
attenuation densities), or significant progression or new calcium formation
may be
associated with a poorer prognosis, including risk of rapid progression of
disease, heart
attack or other major adverse cardiovascular event.
[0220]
Figure 2B is a flowchart illustrating an overview of an example
embodiment(s) of a method for determination of non-calcified and/or low-
attenuated
plaque from a medical image, such as a non-contrast CT image. As discussed
herein and
as illustrated in Figure 2B, in some embodiments, the system can be configured
to
determine non-calcified and/or low-attenuated plaque from a medical image. In
some
embodiments, the medical image can be of the coronary region of the subject or
patient. In
some embodiments, the medical image can be obtained using one or more
modalities such
as CT, Dual-Energy Computed Tomography (DECT), Spectral CT, x-ray, ultrasound,
echocardiography, IVUS, MR, OCT, nuclear medicine imaging, PET, SPECT, NIRS,
and/or the like. In some embodiments, the system can be configured to access
one or more
medical images at block 202, for example from a medical image database 100.
[0221]
In some embodiments, in order to determine non-calcified and/or low-
attenuated plaque from the medical image or non-contrast CT image, the system
can be
configured to utilize a stepwise approach to first identify areas within the
medical image
that are clearly non-calcified plaque. In some embodiments, the system can
then conduct
a more detailed analysis of the remaining areas in the image to identify other
regions of
non-calcified and/or low-attenuated plaque. By utilizing such
compartmentalized or a
stepwise approach, in some embodiments, the system can identify or determine
non-
calcified and/or low-attenuated plaque from the medical image or non-contrast
CT image
with a faster turnaround rather than having to apply a more complicated
analysis to every
region or pixel of the image.
[0222]
In particular, in some embodiments, at block 224, the system can be
configured to identify epicardial fat from the medical image. In some
embodiments, the
system can be configured to identify epicardial fat by determining every pixel
or region
within the image that has a radiodensity value below a predetermined threshold
and/or
within a predetermined range. The exact predetermined threshold value or range
of
radiodensity for identifying epicardial fat can depend on the medical image,
scanner type,
scan parameters, and/or the like, which is why a normalization device can be
used in some
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instances to normalize the medical image. For example, in some embodiments,
the system
can be configured to identify as epicardial fat pixels and/or regions within
the medical
image or non-contrast CT image with a radiodensity value that is around -100
Hounsfield
units and/or within a range that includes -100 Hounsfield units. In
particular, in some
embodiments, the system can be configured to identify as epicardial fat pixels
and/or
regions within the medical image or non-contrast CT image with a radiodensity
value that
is within a range with a lower limit of about -100 Hounsfield units, about -
110 Hounsfield
units, about -120 Hounsfield units, about -130 Hounsfield units, about -140
Hounsfield
units, about -150 Hounsfield units, about -160 Hounsfield units, about -170
Hounsfield
units, about -180 Hounsfield units, about -190 Hounsfield units, or about -200
Hounsfield
units, and an upper limit of about 30 Hounsfield units, about 20 Hounsfield
units, about 10
Hounsfield units, about 0 Hounsfield units, about -10 Hounsfield units, about -
20
Hounsfield units, about -30 Hounsfield units, about -40 Hounsfield units,
about -50
Hounsfield units, about -60 Hounsfield units, about -70 Hounsfield units,
about -80
Hounsfield units, or about -90 Hounsfield units.
[0223]
In some embodiments, the system can be configured to identify and/or
segment arteries on the medical image or non-contrast CT image using the
identified
epicardial fat as outer boundaries of the arteries. For example, the system
can be configured
to first identify regions of epicardial fat on the medical image and assign a
volume in
between epicardial fat as an artery, such as a coronary artery.
[0224]
In some embodiments, at block 226, the system can be configured to
identify a first set of pixels or regions within the medical image, such as
within the
identified arteries, as non-calcified or low-attenuated plaque. More
specifically, in some
embodiments, the system can be configured to identify as an initial set low-
attenuated or
non-calcified plaque by identifying pixels or regions with a radiodensity
value that is below
a predetermined threshold or within a predetermined range. For example, the
predetermined threshold or predetermined range can be set such that the
resulting pixels
can be confidently marked as low-attenuated or non-calcified plaque without
likelihood of
confusion with another matter such as blood. In particular, in some
embodiments, the
system can be configured to identify the initial set of low-attenuated or non-
calcified plaque
by identifying pixels or regions with a radiodensity value below around 30
Hounsfield
units. In some embodiments, the system can be configured to identify the
initial set of low-
attenuated or non-calcified plaque by identifying pixels or regions with a
radiodensity value
at or below around 60 Hounsfield units, around 55 Hounsfield units, around 50
Hounsfield
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units, around 45 Hounsfield units, around 40 Hounsfield units, around 35
Hounsfield
units, around 30 Hounsfield units, around 25 Hounsfield units, around 20
Hounsfield units,
around 15 Hounsfield units, around 10 Hounsfield units, around 5 Hounsfield
units, and/or
with a radiodensity value at or above around 0 Hounsfield units, around 5
Hounsfield units,
around 10 Hounsfield units, around 15 Hounsfield units, around 20 Hounsfield
units,
around 25 Hounsfield units, and/or around 30 Hounsfield units. In some
embodiments, the
system can be configured classify pixels or regions that fall within or below
this
predetermined range of radiodensity values as a first set of identified non-
calcified or low-
attenuated plaque at block 238.
102251
In some embodiments, the system at block 228 can be configured to
identify a second set of pixels or regions within the medical image, such as
within the
identified arteries, that may or may not represent low-attenuated or non-
calcified plaque.
As discussed, in some embodiments, this second set of candidates of pixels or
regions may
require additional analysis to confirm that they represent plaque. In
particular, in some
embodiments, the system can be configured to identify this second set of
pixels or regions
that may potentially be low-attenuated or non-calcified plaque by identifying
pixels or
regions of the image with a radiodensity value within a predetermined range.
In some
embodiments, the predetermined range for identifying this second set of pixels
or regions
can be between around 30 Hounsfield units and 100 Hounsfield units. In some
embodiments, the predetermined range for identifying this second set of pixels
or regions
can have a lower limit of around 0 Hounsfield units, 5 Hounsfield units, 10
Hounsfield
units, 15 Hounsfield units, 20 Hounsfield units, 25 Hounsfield units, 30
Hounsfield units,
35 Hounsfield units, 40 Hounsfield units, 45 Hounsfield units, 50 Hounsfield
units, and/or
an upper limit of around 55 Hounsfield units, 60 Hounsfield units, 65
Hounsfield units, 70
Hounsfield units, 75 Hounsfield units, 80 Hounsfield units, 85 Hounsfield
units, 90
Hounsfield units, 95 Hounsfield units, 100 Hounsfield units, 110 Hounsfield
units, 120
Hounsfield units, 130 Hounsfield units, 140 Hounsfield units, 150 Hounsfield
units.
[0226]
In some embodiments, at block 230, the system can be configured
conduct an analysis of the heterogeneity of the identified second set of
pixels or regions.
For example, depending on the range of radiodensity values used to identify
the second set
of pixels, in some embodiments, the second set of pixels or regions may
include blood
and/or plaque. Blood can typically show a more homogeneous gradient of
radiodensity
values compared to plaque. As such, in some embodiments, by analyzing the
homogeneity
or heterogeneity of the pixels or regions identified as part of the second
set, the system can
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be able to distinguish between blood and non-calcified or low attenuated
plaque. As such,
in some embodiments, the system can be configured to determine a heterogeneity
index of
the second set of regions of pixels identified from the medical image by
generating spatial
mapping, such as a three-dimensional histogram, of radiodensity values within
or across a
geometric shape or region of plaque. In some embodiments, if a gradient or
change in
radiodensity values across the spatial mapping is above a certain threshold,
the system can
be configured to assign a high heterogeneity index and/or classify as plaque.
Conversely,
in some embodiments, if a gradient or change in radiodensity values across the
spatial
mapping is below a certain threshold, the system can be configured to assign a
low
heterogeneity index and/or classify as blood.
[0227]
In some embodiments, at block 240, the system can be configured to
identify a subset of the second set of regions of pixels identified from the
medical image as
plaque or non-calcified or low-attenuated plaque. In some embodiments, at
block 242, the
system can be configured to combine the first set of identified non-calcified
or low-
attenuated plaque from block 238 and the second set of identified non-
calcified or low-
attenuated plaque from block 240. As such, even using non-contrast CT images,
in some
embodiments, the system can be configured to identify low-attenuated or non-
calcified
plaque which can be more difficult to identify compared to calcified or high-
attenuated
plaque due to possible overlap with other matter such as blood.
[0228]
In some embodiments, the system can also be configured to determine
calcified or high-attenuated plaque from the medical image at block 232. This
process can
be more straightforward compared to identifying low-attenuated or non-
calcified plaque
from the medical image or non-contrast CT image. In particular, in some
embodiments,
the system can be configured to identify calcified or high-attenuated plaque
from the
medical image or non-contrast CT image by identifying pixels or regions within
the image
that have a radiodensity value above a predetermined threshold and/or within a
predetermined range. For example, in some embodiments, the system can be
configured
to identify as calcified or high-attenuated plaque regions or pixels from the
medical image
or non-contrast CT image having a radiodensity value above around 100
Hounsfield units,
around 150 Hounsfield units, around 200 Hounsfield units, around 250
Hounsfield units,
around 300 Hounsfield units, around 350 Hounsfield units, around 400
Hounsfield units,
around 450 Hounsfield units, around 500 Hounsfield units, around 600
Hounsfield units,
around 700 Hounsfield units, around 800 Hounsfield units, around 900
Hounsfield units,
around 1000 Hounsfield units, around 1100 Hounsfield units, around 1200
Hounsfield
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units, around 1300 Hounsfield units, around 1400 Hounsfield units, around 1500
Hounsfield units, around 1600 Hounsfield units, around 1700 Hounsfield units,
around
1800 Hounsfield units, around 1900 Hounsfield units, around 2000 Hounsfield
units,
around 2500 Hounsfield units, around 3000 Hounsfield units, and/or any other
minimum
threshold.
[0229]
In some embodiments, at block 234, the system can be configured to
generate a quantized color mapping of one or more identified matters from the
medical
image. For example, in some embodiments, the system can be configured assign
different
colors to each of the different regions associated with different matters,
such as non-
calcified or low-attenuated plaque, calcified or high-attenuated plaque, all
plaque, arteries,
epicardial fat, and/or the like. In some embodiments, the system can be
configured to
generate a visualization of the quantized color map and/or present the same to
a medical
personnel or patient via a GUI. In some embodiments, at block 236, the system
can be
configured to generate a proposed treatment plan for a disease based on one or
more of the
identified non-calcified or low-attenuated plaque, calcified or high-
attenuated plaque, all
plaque, arteries, epicardial fat, and/or the like. For example, in some
embodiments, the
system can be configured to generate a treatment plan for an arterial disease,
renal artery
disease, abdominal atherosclerosis, carotid atherosclerosis, and/or the like,
and the medical
image being analyzed can be taken from any one or more regions of the subject
for such
disease analysis.
[0230]
In some embodiments, one or more processes described herein in
connection with Figure 2B can be repeated. For example, if a medical image of
the same
subject is taken again at a later point in time, one or more processes
described herein can
be repeated and the analytical results thereof can be used for disease
tracking and/or other
purposes.
[0231]
Further, in some embodiments, the system can be configured to identify
and/or determine non-calcified plaque from a DECT or spectral CT image.
Similar to the
processes described above, in some embodiments, the system can be configured
to access
a DECT or spectral CT image, identify epicardial fat on the DECT image or
spectral CT
and/or segment one or more arteries on the DECT image or spectral CT, identify
and/or
classify a first set of pixels or regions within the arteries as a first set
of low-attenuated or
non-calcified plaque, and/or identify a second set of pixels or regions within
the arteries as
a second set of low-attenuated or non-calcified plaque. However, unlike the
techniques
described above, in some embodiments, such as for example where a DECT or
spectral CT
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image is being analyzed, the system can be configured to identify a subset of
those second
set of pixels without having to perform a heterogeneity and/or homogeneity
analysis of the
second set of pixels. Rather, in some embodiments, the system can be
configured to
distinguish between blood and low-attenuated or non-calcified plaque directly
from the
image, for example by utilizing the dual or multispectral aspect of a DECT or
spectral CT
image. In some embodiments, the system can be configured to combine the first
set of
identified pixels or regions and the subset of the second set of pixels or
regions identified
as low-attenuated or non-calcified plaque to identify a whole set of the same
on the medical
image. In some embodiments, even if analyzing a DECT or spectral CT image, the
system
can be configured to further analyze the second set of pixels or regions by
performing a
heterogeneity or homogeneity analysis, similar to that described above in
relation to block
230. For example, even if analyzing a DECT or spectral CT image, in some
embodiments,
the distinction between certain areas of blood and/or low-attenuated or non-
calcified plaque
may not be complete and/or accurate.
Imaging Analysis-Based Risk Assessment
[0232]
In some embodiments, the systems, devices, and methods described
herein are configured to utilize medical image-based processing to assess for
a subject his
or her risk of a cardiovascular event, major adverse cardiovascular event
(MACE), rapid
plaque progression, and/or non-response to medication.
In particular, in some
embodiments, the system can be configured to automatically and/or dynamically
assess
such health risk of a subject by analyzing only non-invasively obtained
medical images, for
example using Al and/or ML algorithms, to provide a full image-based analysis
report
within minutes.
[0233]
In particular, in some embodiments, the system can be configured to
calculate the total amount of plaque (and/or amounts of specific types of
plaque) within a
specific artery and/or within all the arteries of a patient. In some
embodiments, the system
can be configured to determine the total amount of bad plaque in a particular
artery and/or
within a total artery area across some or all of the arteries of the patient.
In some
embodiments, the system can be configured to determine a risk factor and/or a
diagnosis
for a particular patient to suffer a heart attack or other cardiac event based
on the total
amount of plaque in a particular artery and/or a total artery area across some
or all of the
arteries of a patient. Other risk factors that can be determined from the
amount of -bad"
plaque, or the relative amount of -bad" versus -good" plaque, can include the
rate of disease
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progression and/or the likelihood of ischemia. In some embodiments, plaques
can be
measured by total volume (or area on cross-sectional imaging) as well as by
relative amount
when normalized to the total vessel volumes, total vessel lengths or subtended
myocardium.
[0234]
In some embodiments, the imaging data of the coronary arteries can
include measures of atherosclerosis, stenosis and vascular morphology. In some
embodiments, this information can be combined with other cardiovascular
disease
phenotyping by quantitative characterization of left and right ventricles,
left and right atria;
aortic, mitral, tricuspid and pulmonic valves; aorta, pulmonary artery,
pulmonary vein,
coronary sinus and inferior and superior vena cava; epicardial or pericoronary
fat; lung
densities; bone densities; pericardium and others. As an example, in some
embodiments,
the imaging data for the coronary arteries may be integrated with the left
ventricular mass,
which can be segmented according to the amount and location of the artery it
is subtended
by. This combination of left ventricular fractional myocardial mass to
coronary artery
information may enhance the prediction of whether a future heart attack will
be a large one
or a small one. As another example, in some embodiments, the vessel volume of
the
coronary arteries can be related to the left ventricular mass as a measure of
left ventricular
hypertrophy, which can be a common finding in patients with hypertension.
Increased left
ventricular mass (relative or absolute) may indicate disease worsening or
uncontrolled
hypertension. As another example, in some embodiments, the onset, progression,
and/or
worsening of atrial fibrillation may be predicted by the atrial size, volume,
atrial free wall
mass and thickness, atrial function and fat surrounding the atrium. In some
embodiments,
these predictions may be done with a ML or AT algorithm or other algorithm
type.
[0235]
Sequentially, in some embodiments, the algorithms that allow for
segmentation of atherosclerosis, stenosis and vascular morphology¨along with
those that
allow for segmentation of other cardiovascular structures, and thoracic
structures¨may
serve as the inputs for the prognostic algorithms. In some embodiments, the
outputs of the
prognostic algorithms, or those that allow for image segmentation, may be
leveraged as
inputs to other algorithms that may then guide clinical decision making by
predicting future
events. As an example, in some embodiments, the integrated scoring of
atherosclerosis,
stenosis, and/or vascular morphology may identify patients who may benefit
from coronary
revascularization, that is, those who will achieve symptom benefit, reduced
risk of heart
attack and death. As another example, in some embodiments, the integrated
scoring of
atherosclerosis, stenosis and vascular morphology may identify individuals who
may
benefit from specific types of medications, such as lipid lowering medications
(such as
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statin medications, PCSK-9 inhibitors, icosapent ethyl, and others); Lp(a)
lowering
medications; anti-thrombotic medications (such as clopidogrel, rivaroxaban and
others). In
some embodiments, the benefit that is predicted by these algorithms may be for
reduced
progression, determination of type of plaque progression (progression,
regression or mixed
response), stabilization due to the medical therapy, and/or need for
heightened intensified
therapy. In some embodiments, the imaging data may be combined with other data
to
identify areas within a coronary vessel that are normal and without plaque now
but may be
at higher likelihood of future plaque formation.
[0236]
In some embodiments, an automated or manual co-registration method
can be combined with the imaging segmentation data to compare two or more
images over
time. In some embodiments, the comparison of these images can allow for
determination
of differences in coronary artery atherosclerosis, stenosis and vascular
morphology over
time, and can be used as an input variable for risk prediction.
[0237]
In some embodiments, the imaging data of the coronary arteries for
atherosclerosis, stenosis, and vascular morphology¨coupled or not coupled to
thoracic and
cardiovascular disease measurements¨can be integrated into an algorithm that
determines
whether a coronary vessel is ischemia, or exhibits reduced blood flow or
pressure (either at
rest or hyperemic states).
[0238]
In some embodiments, the algorithms for coronary atherosclerosis,
stenosis and ischemia can be modified by a computer system and/or other to
remove plaque
or "seal" plaque. in some embodiments, a comparison can be made before or
after the
system has removed or sealed the plaque to determine whether any changes have
occurred.
For example, in some embodiments, the system can be configured to determine
whether
coronary ischemia is removed with the plaque sealing.
[0239]
In some embodiments, the characterization of coronary atherosclerosis,
stenosis and/or vascular morphology can enable relating a patient's biological
age to their
vascular age, when compared to a population-based cohort of patients who have
undergone
similar scanning. As an example, a 60-year old patient may have X units of
plaque in their
coronary arteries that is equivalent to the average 70-year old patient in the
population-
based cohort. In this case, the patient's vascular age may be 10 years older
than the
patient's biological age.
[0240]
In some embodiments, the risk assessment enabled by the image
segmentation prediction algorithms can allow for refined measures of disease
or death
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likelihood in people being considered for disability or life insurance. In
this scenario, the
risk assessment may replace or augment traditional actuarial algorithms.
[0241]
In some embodiments, imaging data may be combined with other data
to augment risk assessment for future adverse events, such as heart attacks,
strokes, death,
rapid progression, non-response to medical therapy, no-reflow phenomenon and
others. In
some embodiments, other data may include a multi-omic approach wherein an
algorithm
integrates the imaging phenotype data with genotype data, proteomic data,
transcriptomic
data, metabolomic data, microbiomic data and/or activity and lifestyle data as
measured by
a smart phone or similar device.
102421
Figure 3A is a flowchart illustrating an overview of an example
embodiment(s) of a method for risk assessment based on medical image analysis.
As
illustrated in Figure 3A, in some embodiments, the system can be configured to
access a
medical image at block 202. Further, in some embodiments, the system can be
configured
to identify one or more arteries at block 204 and/or one or more regions of
plaque at block
206. In addition, in some embodiments, the system can be configured to
determine one or
more vascular morphology and/or quantified plaque parameters at block 208
and/or classify
stable or unstable plaque based on the determined one or more vascular
morphology and/or
quantified plaque parameters and/or a weighted measure thereof at block 210.
Additional
detail regarding the processes and techniques represented in blocks 202, 204,
206, 208, and
210 can be found in the description above in relation to Figure 2A.
[0243]
In some embodiments, the system can automatically and/or dynamically
determine and/or generate a risk of cardiovascular event for the subject at
block 302, for
example using the classified stable and/or unstable regions of plaque. More
specifically,
in some embodiments, the system can utilize an Al, ML, or other algorithm to
generate a
risk of cardiovascular event, MACE, rapid plaque progression, and/or non-
response to
medication at block 302 based on the image analysis.
[0244]
In some embodiments, at block 304, the system can be configured to
compare the determined one or more vascular morphology parameters, quantified
plaque
parameters, and/or classified stable v. unstable plaque and/or values thereof,
such as
volume, ratio, and/or the like, to one or more known datasets of coronary
values derived
from one or more other subjects. The one or more known datasets can comprise
one or
more vascular morphology parameters, quantified plaque parameters, and/or
classified
stable v. unstable plaque and/or values thereof, such as volume, ratio, and/or
the like,
derived from medical images taken from other subjects, including healthy
subjects and/or
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subjects with varying levels of risk. For example, the one or more known
datasets of
coronary values can be stored in a coronary values database 306 that can be
locally
accessible by the system and/or remotely accessible via a network connection
by the
system.
[0245]
In some embodiments, at block 308, the system can be configured to
update the risk of cardiovascular event for the subject based on the
comparison to the one
or more known datasets. For example, based on the comparison, the system may
increase
or decrease the previously generated risk assessment. In some embodiments, the
system
may maintain the previously generated risk assessment even after comparison.
In some
embodiments, the system can be configured to generate a proposed treatment for
the subject
based on the generated and/or updated risk assessment after comparison with
the known
datasets of coronary values.
[0246]
In some embodiments, at block 310, the system can be configured to
further identify one or more other cardiovascular structures from the medical
image and/or
determine one or more parameters associated with the same. For example, the
one or more
additional cardiovascular structures can include the left ventricle, right
ventricle, left
atrium, right atrium, aortic valve, mitral valve, tricuspid valve, pulmonic
valve, aorta,
pulmonary artery, inferior and superior vena cava, epicardial fat, and/or
pericardium.
[0247]
In some embodiments, parameters associated with the left ventricle can
include size, mass, volume, shape, eccentricity, surface area, thickness,
and/or the like.
Similarly, in some embodiments, parameters associated with the right ventricle
can include
size, mass, volume, shape, eccentricity, surface area, thickness, and/or the
like. In some
embodiments, parameters associated with the left atrium can include size,
mass, volume,
shape, eccentricity, surface area, thickness, pulmonary vein angulation,
atrial appendage
morphology, and/or the like. In some embodiments, parameters associated with
the right
atrium can include size, mass, volume, shape, eccentricity, surface area,
thickness, and/or
the like.
[0248]
Further, in some embodiments, parameters associated with the aortic
valve can include thickness, volume, mass, calcifications, three-dimensional
map of
calcifications and density, eccentricity of calcification, classification by
individual leaflet,
and/or the like. in some embodiments, parameters associated with the mitral
valve can
include thickness, volume, mass, calcifications, three-dimensional map of
calcifications
and density, eccentricity of calcification, classification by individual
leaflet, and/or the like.
In some embodiments, parameters associated with the tricuspid valve can
include thickness,
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volume, mass, calcifications, three-dimensional map of calcifications and
density,
eccentricity of calcification, classification by individual leaflet, and/or
the like. In some
embodiments, parameters associated with the pulmonic valve can include
thickness,
volume, mass, calcifications, three-dimensional map of calcifications and
density,
eccentricity of calcification, classification by individual leaflet, and/or
the like.
[0249]
In some embodiments, parameters associated with the aorta can include
dimensions, volume, diameter, area, enlargement, outpouching, and/or the like.
In some
embodiments, parameters associated with the pulmonary artery can include
dimensions,
volume, diameter, area, enlargement, outpouching, and/or the like. In some
embodiments,
parameters associated with the inferior and superior vena cava can include
dimensions,
volume, diameter, area, enlargement, outpouching, and/or the like.
[0250]
In some embodiments, parameters associated with epicardial fat can
include volume, density, density in three dimensions, and/or the like. In some
embodiments, parameters associated with the pericardium can include thickness,
mass,
and/or the like.
[0251]
In some embodiments, at block 312, the system can be configured to
classify one or more of the other identified cardiovascular structures, for
example using the
one or more determined parameters thereof In some embodiments, for one or more
of the
other identified cardiovascular structures, the system can be configured to
classify each as
normal v. abnormal, increased or decreased, and/or static or dynamic over
time.
[0252]
In some embodiments, at block 314, the system can be configured to
compare the determined one or more parameters of other cardiovascular
structures to one
or more known datasets of cardiovascular structure parameters derived from one
or more
other subjects. The one or more known datasets of cardiovascular structure
parameters can
include any one or more of the parameters mentioned above associated with the
other
cardiovascular structures. In some embodiments, the cardiovascular structure
parameters
of the one or more known datasets can be derived from medical images taken
from other
subjects, including healthy subjects and/or subjects with varying levels of
risk. In some
embodiments, the one or more known datasets of cardiovascular structure
parameters can
be stored in a cardiovascular structure values or cardiovascular disease (CVD)
database
316 that can be locally accessible by the system and/or remotely accessible
via a network
connection by the system.
[0253]
In some embodiments, at block 318, the system can be configured to
update the risk of cardiovascular event for the subject based on the
comparison to the one
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or more known datasets of cardiovascular structure parameters. For example,
based on the
comparison, the system may increase or decrease the previously generated risk
assessment.
In some embodiments, the system may maintain the previously generated risk
assessment
even after comparison.
[0254]
In some embodiments, at block 320, the system can be configured to
generate a quantified color map, which can include color coding for one or
more other
cardiovascular structures identified from the medical image, stable plaque,
unstable plaque,
arteries, and/or the like. In some embodiments, at block 322, the system can
be configured
to generate a proposed treatment for the subject based on the generated and/or
updated risk
assessment after comparison with the known datasets of cardiovascular
structure
parameters.
[0255]
In some embodiments, at block 324, the system can be configured to
further identify one or more non-cardiovascular structures from the medical
image and/or
determine one or more parameters associated with the same. For example, the
medical
image can include one or more non-cardiovascular structures that are in the
field of view.
In particular, the one or more non-cardiovascular structures can include the
lungs, bones,
liver, and/or the like.
[0256]
In some embodiments, parameters associated with the non-
cardiovascular structures can include volume, surface area, ratio or function
of volume to
surface area, heterogeneity of radiodensity values, radiodensity values,
geometry (such as
oblong, spherical, and/or the like), spatial radiodensity, spatial scarring,
and/or the like. In
addition, in some embodiments, parameters associated with the lungs can
include density,
scarring, and/or the like. For example, in some embodiments, the system can be
configured
to associate a low Hounsfield unit of a region of the lungs with emphysema. In
some
embodiments, parameters associated with bones, such as the spine and/or ribs,
can include
radiodensity, presence and/or extent of fractures, and/or the like. For
example, in some
embodiments, the system can be configured to associate a low Hounsfield unit
of a region
of bones with osteoporosis. In some embodiments, parameters associated with
the liver
can include density for non-alcoholic fatty liver disease which can be
assessed by the
system by analyzing and/or comparing to the Hounsfield unit density of the
liver.
[0257]
In some embodiments, at block 326, the system can be configured to
classify one or more of the identified non-cardiovascular structures, for
example using the
one or more determined parameters thereof In some embodiments, for one or more
of the
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identified non-cardiovascular structures, the system can be configured to
classify each as
normal v. abnormal, increased or decreased, and/or static or dynamic over
time.
[0258]
In some embodiments, at block 328, the system can be configured to
compare the determined one or more parameters of non-cardiovascular structures
to one or
more known datasets of non-cardiovascular structure parameters or non-CVD
values
derived from one or more other subjects. The one or more known datasets of non-
cardiovascular structure parameters or non-CVD values can include any one or
more of the
parameters mentioned above associated with non-cardiovascular structures. In
some
embodiments, the non-cardiovascular structure parameters or non-CVD values of
the one
or more known datasets can be derived from medical images taken from other
subjects,
including healthy subjects and/or subjects with varying levels of risk. In
some
embodiments, the one or more known datasets of non-cardiovascular structure
parameters
or non-CVD values can be stored in a non-cardiovascular structure values or
non-CVD
database 330 that can be locally accessible by the system and/or remotely
accessible via a
network connection by the system.
[0259]
In some embodiments, at block 332, the system can be configured to
update the risk of cardiovascular event for the subject based on the
comparison to the one
or more known datasets of non-cardiovascular structure parameters or non-CVD
values.
For example, based on the comparison, the system may increase or decrease the
previously
generated risk assessment. In some embodiments, the system may maintain the
previously
generated risk assessment even after comparison.
[0260]
In some embodiments, at block 334, the system can be configured to
generate a quantified color map, which can include color coding for one or
more non-
cardiovascular structures identified from the medical image, as well as for
the other
cardiovascular structures identified from the medical image, stable plaque,
unstable plaque,
arteries, and/or the like. In some embodiments, at block 336, the system can
be configured
to generate a proposed treatment for the subject based on the generated and/or
updated risk
assessment after comparison with the known datasets of non-cardiovascular
structure
parameters or non-CVD values.
[0261]
In some embodiments, one or more processes described herein in
connection with Figure 3A can be repeated. For example, if a medical image of
the same
subject is taken again at a later point in time, one or more processes
described herein can
be repeated and the analytical results thereof can be used for tracking of
risk assessment of
the subject based on image processing and/or other purposes.
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Quantification of Atherosclerosis
[0262]
In some embodiments, the system is configured to analyze one or more
arteries present in a medical image, such as CT scan data, to automatically
and/or
dynamically quantify atherosclerosis. In some embodiments, the system is
configured to
quantify atherosclerosis as the primary disease process, while stenosis and/or
ischemia can
be considered surrogates thereof. Prior to the embodiments described herein,
it was not
feasible to quantify the primary disease due to the lengthy manual process and
manpower
needed to do so, which could take anywhere from 4 to 8 or more hours. In
contrast, in
some embodiments, the system is configured to quantify atherosclerosis based
on analysis
of a medical image and/or CT scan using one or more AT, ML, and/or other
algorithms that
can segment, identify, and/or quantify atherosclerosis in less than about 1
minute, about 2
minutes, about 3 minutes, about 4 minutes, about 5 minutes, about 6 minutes,
about 7
minutes, about 8 minutes, about 9 minutes, about 10 minutes, about 11 minutes,
about 12
minutes, about 13 minutes, about 14 minutes, about 15 minutes, about 20
minutes, about
25 minutes, about 30 minutes, about 40 minutes, about 50 minutes, and/or about
60
minutes. In some embodiments, the system is configured to quantify
atherosclerosis within
a time frame defined by two of the aforementioned values. In some embodiments,
the
system is configured to calculate stenosis rather than simply eyeballing,
thereby allowing
users to better understand whole heart atherosclerosis and/or guaranteeing the
same
calculated stenosis result if the same medical image is used for analysis.
Importantly, the
type of atherosclerosis can also be quantified and/or classified by this
method_ Types of
atherosclerosis can be determined binarily (calcified vs. non-calcified
plaque), ordinally
(dense calcified plaque, calcified plaque, fibrous plaque, fibrofatty plaque,
necrotic core,
or admixtures of plaque types), or continuously (by attenuation density on a
Hounsfield
unit scale or similar).
[0263]
Figure 3B is a flowchart illustrating an overview of an example
embodiment(s) of a method for quantification and/or classification of
atherosclerosis based
on medical image analysis. As illustrated in Figure 3B, in some embodiments,
the system
can be configured to access a medical image at block 202, such as a CT scan of
a coronary
region of a subject. Further, in some embodiments, the system can be
configured to identify
one or more arteries at block 204 and/or one or more regions of plaque at
block 206. In
addition, in some embodiments, the system can be configured to determine one
or more
vascular morphology and/or quantified plaque parameters at block 208. For
example, in
some embodiments, the system can be configured to determine a geometry and/or
volume
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of a region of plaque and/or a vessel at block 201, a ratio or function of
volume to surface
area of a region of plaque at block 203, a heterogeneity or homogeneity index
of a region
of plaque at block 205, radiodensity of a region of plaque and/or a
composition thereof by
ranges of radiodensity values at block 207, a ratio of radiodensity to volume
of a region of
plaque at block 209, and/or a diffusivity of a region of plaque at block 211.
Additional
detail regarding the processes and techniques represented in blocks 202, 204,
206, 208,
201, 203, 205, 207, 209, and 211 can be found in the description above in
relation to Figure
2A.
[0264]
In some embodiments, the system can be configured quantify and/or
classify atherosclerosis at block 340 based on the determined one or more
vascular
morphology and/or quantified plaque parameters. In some embodiments, the
system can
be configured to generate a weighted measure of one or more vascular
morphology
parameters and/or quantified plaque parameters determined and/or derived from
raw
medical images. For example, in some embodiments, the system can be configured
to
weight one or more vascular morphology parameters and/or quantified plaque
parameters
equally. In some embodiments, the system can be configured weight one or more
vascular
morphology parameters and/or quantified plaque parameters differently. In some
embodiments, the system can be configured weight one or more vascular
morphology
parameters and/or quantified plaque parameters logarithmically, algebraically,
and/or
utilizing another mathematical transform. In some embodiments, the system is
configured
to quantify and/or classify atherosclerosis at block 340 using the weighted
measure and/or
using only some of the vascular morphology parameters and/or quantified plaque
parameters.
102651
In some embodiments, the system is configured to generate a weighted
measure of the one or more vascular morphology parameters and/or quantified
plaque
parameters by comparing the same to one or more known vascular morphology
parameters
and/or quantified plaque parameters that are derived from medical images of
other subjects.
For example, the one or more known vascular morphology parameters and/or
quantified
plaque parameters can be derived from one or more healthy subjects and/or
subjects at risk
of coronary vascular disease.
[0266]
In some embodiments, the system is configured to classify
atherosclerosis of a subject based on the quantified atherosclerosis as one or
more of high
risk, medium risk, or low risk. In some embodiments, the system is configured
to classify
atherosclerosis of a subject based on the quantified atherosclerosis using an
Al, ML, and/or
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other algorithm. In some embodiments, the system is configured to classify
atherosclerosis
of a subject by combining and/or weighting one or more of a ratio of volume of
surface
area, volume, heterogeneity index, and radiodensity of the one or more regions
of plaque.
[0267]
In some embodiments, a plaque having a low ratio of volume to surface
area or a low absolute volume itself can indicate that the plaque is stable.
As such, in some
embodiments, the system can be configured to determine that a ratio of volume
to surface
area of a region of plaque below a predetermined threshold is indicative of a
low risk
atherosclerosis. Thus, in some embodiments, the system can be configured to
take into
account the number and/or sides of a plaque. For example, if there are a
higher number of
plaques with smaller sides, then that can be associated with a higher surface
area or more
irregularity, which in turn can be associated with a higher surface area to
volume ratio. In
contrast, if there are fewer number of plaques with larger sides or more
regularity, then that
can be associated with a lower surface area to volume ratio or a higher volume
to surface
area ratio. In some embodiments, a high radiodensity value can indicate that a
plaque is
highly calcified or stable, whereas a low radiodensity value can indicate that
a plaque is
less calcified or unstable. As such, in some embodiments, the system can be
configured to
determine that a radiodensity of a region of plaque above a predetermined
threshold is
indicative of a low risk atherosclerosis. In some embodiments, a plaque having
a low
heterogeneity or high homogeneity can indicate that the plaque is stable. As
such, in some
embodiments, the system can be configured to determine that a heterogeneity of
a region
of plaque below a predetermined threshold is indicative of a low risk
atherosclerosis.
[0268]
In some embodiments, at block 342, the system is configured to
calculate or determine a numerical calculation or representation of coronary
stenosis based
on the quantified and/or classified atherosclerosis derived from the medical
image. In some
embodiments, the system is configured to calculate stenosis using the one or
more vascular
morphology parameters and/or quantified plaque parameters derived from the
medical
image of a coronary region of the subject.
[0269]
In some embodiments, at block 344, the system is configured to predict
a risk of ischemia for the subject based on the quantified and/or classified
atherosclerosis
derived from the medical image. In some embodiments, the system is configured
to
calculate a risk of ischemia using the one or more vascular morphology
parameters and/or
quantified plaque parameters derived from the medical image of a coronary
region of the
subj ect.
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[0270]
In some embodiments, the system is configured to generate a proposed
treatment for the subject based on the quantified and/or classified
atherosclerosis, stenosis,
and/or risk of ischemia, wherein all of the foregoing are derived
automatically and/or
dynamically from a raw medical image using image processing algorithms and
techniques.
[0271]
In some embodiments, one or more processes described herein in
connection with Figure 3A can be repeated. For example, if a medical image of
the same
subject is taken again at a later point in time, one or more processes
described herein can
be repeated and the analytical results thereof can be used for tracking of
quantified
atherosclerosis for a subject and/or other purposes.
Quantification of Plaque, Stenosis, and/or CAD-RADS Score
[0272]
As discussed herein, in some embodiments, the system is configured to
take the guesswork out of interpretation of medical images and provide
substantially exact
and/or substantially accurate calculations or estimates of stenosis
percentage,
atherosclerosis, and/or Coronary Artery Disease ¨ Reporting and Data System
(CAD-
RADS) score as derived from a medical image. As such, in some embodiments, the
system
can enhance the reads of the imagers by providing comprehensive quantitative
analyses
that can improve efficiency, accuracy, and/or reproducibility.
[0273]
Figure 3C is a flowchart illustrating an overview of an example
embodiment(s) of a method for quantification of stenosis and generation of a
CAD-RADS
score based on medical image analysis. As illustrated in Figure 3A, in some
embodiments,
the system can be configured to access a medical image at block 202.
Additional detail
regarding the types of medical images and other processes and techniques
represented in
block 202 can be found in the description above in relation to Figure 2A.
[0274]
In some embodiments, at block 354, the system is configured to identify
one or more arteries, plaque, and/or fat in the medical image, for example
using AT, ML,
and/or other algorithms. The processes and techniques for identifying one or
more arteries,
plaque, and/or fat can include one or more of the same features as described
above in
relation to blocks 204 and 206. In particular, in some embodiments, the system
can be
configured to utilize one or more Al and/or ML algorithms to automatically
and/or
dynamically identify one or more arteries, including for example coronary
arteries, carotid
arteries, aorta, renal artery, lower extremity artery, and/or cerebral artery.
In some
embodiments, one or more Al and/or ML algorithms can be trained using a
Convolutional
Neural Network (CNN) on a set of medical images on which arteries have been
identified,
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thereby allowing the Al and/or ML algorithm automatically identify arteries
directly from
a medical image. In some embodiments, the arteries are identified by size
and/or location.
[0275]
Further, in some embodiments, the system can be configured to identify
one or more regions of plaque in the medical image, for example using one or
more Al
and/or ML algorithms to automatically and/or dynamically identify one or more
regions of
plaque. In some embodiments, the one or more AI and/or ML algorithms can be
trained
using a Convolutional Neural Network (CNN) on a set of medical images on which
regions
of plaque have been identified, thereby allowing the AT and/or ML algorithm
automatically
identify regions of plaque directly from a medical image. In some embodiments,
the system
can be configured to identify a vessel wall and a lumen wall for each of the
identified
coronary arteries in the medical image. In some embodiments, the system is
then
configured to determine the volume in between the vessel wall and the lumen
wall as
plaque. In some embodiments, the system can be configured to identify regions
of plaque
based on the radiodensity values typically associated with plaque, for example
by setting a
predetermined threshold or range of radiodensity values that are typically
associated with
plaque with or without normalizing using a normalization device.
[0276]
Similarly, in some embodiments, the system can be configured to
identify one or more regions of fat, such as epicardial fat, in the medical
image, for example
using one or more AT and/or ML algorithms to automatically and/or dynamically
identify
one or more regions of fat. In some embodiments, the one or more Al and/or ML
algorithms can be trained using a Convolutional Neural Network (CNN) on a set
of medical
images on which regions of fat have been identified, thereby allowing the AT
and/or ML
algorithm automatically identify regions of fat directly from a medical image.
In some
embodiments, the system can be configured to identify regions of fat based on
the
radiodensity values typically associated with fat, for example by setting a
predetermined
threshold or range of radiodensity values that are typically associated with
fat with or
without normalizing using a normalization device.
[0277]
In some embodiments, the system can be configured to determine one
or more vascular morphology and/or quantified plaque parameters at block 208.
For
example, in some embodiments, the system can be configured to determine a
geometry
and/or volume of a region of plaque and/or a vessel at block 201, a ratio or
function of
volume to surface area of a region of plaque at block 203, a heterogeneity or
homogeneity
index of a region of plaque at block 205, radiodensity of a region of plaque
and/or a
composition thereof by ranges of radiodensity values at block 207, a ratio of
radiodensity
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to volume of a region of plaque at block 209, and/or a diffusivity of a region
of plaque at
block 211. Additional detail regarding the processes and techniques
represented in blocks
208, 201, 203, 205, 207, 209, and 211 can be found in the description above in
relation to
Figure 2A.
[0278]
In some embodiments, at block 358, the system is configured to
calculate or determine a numerical calculation or representation of coronary
stenosis based
on the one or more vascular morphology parameters and/or quantified plaque
parameters
derived from the medical image of a coronary region of the subject. In some
embodiments,
the system can be configured to generate a weighted measure of one or more
vascular
morphology parameters and/or quantified plaque parameters determined and/or
derived
from raw medical images. For example, in some embodiments, the system can be
configured weight one or more vascular morphology parameters and/or quantified
plaque
parameters equally. In some embodiments, the system can be configured to
weight one or
more vascular morphology parameters and/or quantified plaque parameters
differently. In
some embodiments, the system can be configured weight one or more vascular
morphology
parameters and/or quantified plaque parameters logarithmically, algebraically,
and/or
utilizing another mathematical transform. In some embodiments, the system is
configured
to calculate stenosis at block 358 using the weighted measure and/or using
only some of
the vascular morphology parameters and/or quantified plaque parameters. In
some
embodiments, the system can be configured to calculate stenosis on a vessel-by-
vessel basis
or a region-by-region basis.
[0279]
In some embodiments, based on the calculated stenosis, the system is
configured to determine a CAD-RADS score at block 360. This is in contrast to
preexisting
methods of determining a CAD-RADS based on eyeballing or general assessment of
a
medical image by a physician, which can result in unreproducible results. In
some
embodiments described herein, however, the system can be configured to
generate a
reproducible and/or objective calculated CAD-RADS score based on automatic
and/or
dynamic image processing of a raw medical image.
[0280]
In some embodiments, at block 362, the system can be configured to
determine a presence or risk of ischemia based on the calculated stenosis, one
or more
quantified plaque parameters and/or vascular morphology parameters derived
from the
medical image. For example, in some embodiments, the system can be configured
to
determine a presence or risk of ischemia by combining one or more of the
foregoing
parameters, either weighted or not, or by using some or all of these
parameters on an
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individual basis. In some embodiments, the system can be configured to
determine a
presence of risk of ischemia by comparing one or more of the calculated
stenosis, one or
more quantified plaque parameters and/or vascular morphology parameters to a
database
of known such parameters derived from medical images of other subjects,
including for
example healthy subjects and/or subjects at risk of a cardiovascular event. In
some
embodiments, the system can be configured to calculate presence or risk of
ischemia on a
vessel-by-vessel basis or a region-by-region basis.
102811
In some embodiments, at block 364, the system can be configured to
determine one or more quantified parameters of fat for one or more regions of
fat identified
from the medical image. For example, in some embodiments, the system can
utilize any
of the processes and/or techniques discussed herein in relation to deriving
quantified
parameters of plaque, such as those described in connection with blocks 208,
201, 203, 205,
207, 209, and 211. In particular, in some embodiments, the system can be
configured to
determine one or more parameters of fat, including volume, geometry,
radiodensity, and/or
the like of one or more regions of fat within the medical image.
[0282]
In some embodiments, at block 366, the system can be configured to
generate a risk assessment of cardiovascular disease or event for the subject.
In some
embodiments, the generated risk assessment can comprise a risk score
indicating a risk of
coronary disease for the subject. In some embodiments, the system can generate
a risk
assessment based on an analysis of one or more vascular morphology parameters,
one or
more quantified plaque parameters, one or more quantified fat parameters,
calculated
stenosis, risk of ischemia, CAD-RADS score, and/or the like. In some
embodiments, the
system can be configured to generate a weighted measure of one or more
vascular
morphology parameters, one or more quantified plaque parameters, one or more
quantified
fat parameters, calculated stenosis, risk of ischemia, and/or CAD-RADS score
of the
subject. For example, in some embodiments, the system can be configured weight
one or
more of the foregoing parameters equally. In some embodiments, the system can
be
configured weight one or more of these parameters differently. In some
embodiments, the
system can be configured weight one or more of these parameters
logarithmically,
algebraically, and/or utilizing another mathematical transform. In some
embodiments, the
system is configured to generate a risk assessment of coronary disease or
cardiovascular
event for the subject at block 366 using the weighted measure and/or using
only some of
these parameters.
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[0283]
In some embodiments, the system can be configured to generate a risk
assessment of coronary disease or cardiovascular event for the subject by
combining one
or more of the foregoing parameters, either weighted or not, or by using some
or all of these
parameters on an individual basis. In some embodiments, the system can be
configured to
generate a risk assessment of coronary disease or cardiovascular event by
comparing one
or more vascular morphology parameters, one or more quantified plaque
parameters, one
or more quantified fat parameters, calculated stenosis, risk of ischemia,
and/or CAD-RADS
score of the subject to a database of known such parameters derived from
medical images
of other subjects, including for example healthy subjects and/or subjects at
risk of a
cardiovascular event.
[0284]
Further, in some embodiments, the system can be configured to
automatically and/or dynamically generate a CAD-RADS modifier based on one or
more
of the determined one or more vascular morphology parameters, the set of
quantified plaque
parameters of the one or more regions of plaque, the quantified coronary
stenosis, the
determined presence or risk of ischemia, and/or the determined set of
quantified fat
parameters. In particular, in some embodiments, the system can be configured
to
automatically and/or dynamically generate one or more applicable CAD-RADS
modifiers
for the subject, including for example one or more of nondiagnostic (N), stent
(S), graft
(G), or vulnerability (V), as defined by and used by CAD-RADS. For example, N
can
indicate that a study is non-diagnostic. S can indicate the presence of a
stent, G can indicate
the presence of a coronary artery bypass graft, and V can indicate the
presence of vulnerable
plaque, for example showing a low radiodensity value.
[0285]
In some embodiments, the system can be configured to generate a
proposed treatment for the subject based on the generated risk assessment of
coronary
disease, one or more vascular morphology parameters, one or more quantified
plaque
parameters, one or more quantified fat parameters, calculated stenosis, risk
of ischemia,
CAD-RADS score, and/or CAD-RADS modifier derived from the raw medical image
using
image processing.
[0286]
In some embodiments, one or more processes described herein in
connection with Figure 3B can be repeated. For example, if a medical image of
the same
subject is taken again at a later point in time, one or more processes
described herein can
be repeated and the analytical results thereof can be used for tracking of
quantified plaque,
calculated stenosis, CAD-RADS score and/or modifier derived from a medical
image(s),
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risk determined risk of ischemia, quantified fat parameters, generated risk
assessment of
coronary disease for a subject, and/or other purposes.
Disease Tracking
[0287]
In some embodiments, the systems, methods, and devices described
herein can be configured to track the progression and/or regression of an
arterial and/or
plaque-based disease, such as a coronary disease. For example, in some
embodiments, the
system can be configured to track the progression and/or regression of a
disease by
automatically and/or dynamically analyzing a plurality of medical images
obtained from
different times using one or more techniques discussed herein and comparing
different
parameters derived therefrom. As such, in some embodiments, the system can
provide an
automated disease tracking tool using non-invasive raw medical images as an
input, which
does not rely on subjective assessment.
[0288]
In particular, in some embodiments, the system can be configured to
utilize a four-category system to determine whether plaque stabilization or
worsening is
occurring in a subject. For example, in some embodiments, these categories can
include:
(1) "plaque progression- or "rapid plaque progression-; (2) "mixed response -
calcium
dominant" or "non-rapid calcium dominant mixed response"; (3) "mixed response -
non-
calcium dominant" or "non-rapid non-calcium dominant mixed response"; or (4)
"plaque
regression."
102891
In some embodiments, in "plaque progression" or "rapid plaque
progression," the overall volume or relative volume of plaque increases. In
some
embodiments, in "mixed response - calcium dominant" or "non-rapid calcium
dominant
mixed response," the plaque volume remains relatively constant or does not
increase to the
threshold level of -rapid plaque progression" but there is a general
progression of calcified
plaque and a general regression of non-calcified plaque. In some embodiments,
in "mixed
response - non-calcium dominant- or -non-rapid non-calcium dominant mixed
response,"
the plaque volume remains relatively constant but there is a general
progression of non-
calcified plaque and a general regression of calcified plaque. In some
embodiments, in
"plaque regression," the overall volume or relative volume of plaque
decreases.
[0290]
In some embodiments, these 4 categories can be expanded to be more
granular, for example including for higher vs. lower density calcium plaques
(e.g., for those
> vs. <1000 Hounsfield units) and/or to categorize more specifically in
calcium-dominant
and non-calcified plaque-dominant mixed response. For example, for the non-
calcified
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plaque-dominant mixed response, the non-calcified plaque can further include
necrotic
core, fibrofatty plaque and/or fibrous plaque as separate categories within
the overall
umbrella of non-calcified plaque. Similarly, calcified plaques can be
categorized as lower
density calcified plaques, medium density calcified plaques and high density
calcified
plaques.
[0291]
Figure 3D is a flowchart illustrating an overview of an example
embodiment(s) of a method for disease tracking based on medical image
analysis. For
example, in some embodiments, the system can be configured to track the
progression
and/or regression of a plaque-based disease or condition, such as a coronary
disease relating
to or involving atherosclerosis, stenosis, ischemia, and/or the like, by
analyzing one or more
medical images obtained non-invasively.
[0292]
As illustrated in Figure 3D, in some embodiments, the system at block
372 is configured to access a first set of plaque parameters derived from a
medical image
of a subject at a first point in time. In some embodiments, the medical image
can be stored
in a medical image database 100 and can include any of the types of medical
images
described above, including for example CT, non-contrast CT, contrast-enhanced
CT, MR,
DECT, Spectral CT, and/or the like. In some embodiments, the medical image of
the
subject can comprise the coronary region, coronary arteries, carotid arteries,
renal arteries,
abdominal aorta, cerebral arteries, lower extremities, and/or upper
extremities of the
subject. In some embodiments, the set of plaque parameters can be stored in a
plaque
parameter database 370, which can include any of the quantified plaque
parameters
discussed above in relation to blocks 208, 201, 203, 205, 207, 209, and/or
211.
[0293]
In some embodiments, the system can be configured to directly access
the first set of plaque parameters that were previously derived from a medical
image(s)
and/or stored in a plaque parameter database 370. In some embodiments, the
plaque
parameter database 370 can be locally accessible and/or remotely accessible by
the system
via a network connection. In some embodiments, the system can be configured to
dynamically and/or automatically derive the first set of plaque parameters
from a medical
image taken from a first point in time.
[0294]
In some embodiments, at block 374, the system can be configured to
access a second medical image(s) of the subject, which can be obtained from
the subject at
a later point in time than the medical image from which the first set of
plaque parameters
were derived. In some embodiments, the medical image can be stored in a
medical image
database 100 and can include any of the types of medical images described
above, including
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for example CT, non-contrast CT, contrast-enhanced CT, MR, DECT, Spectral CT,
and/or
the like.
[0295]
In some embodiments, at block 376, the system can be configured to
dynamically and/or automatically derive a second set of plaque parameters from
the second
medical image taken from the second point in time. In some embodiments, the
second set
of plaque parameters can include any of the quantified plaque parameters
discussed above
in relation to blocks 208, 201, 203, 205, 207, 209, and/or 211. In some
embodiments, the
system can be configured to store the derived or determined second set of
plaque
parameters in the plaque parameter database 370.
102961
In some embodiments, at block 378, the system can be configured to
analyze changes in one or more plaque parameters between the first set derived
from a
medical image taken at a first point in time to the second set derived from a
medical image
taken at a later point in time. For example, in some embodiments, the system
can be
configured to compare a quantified plaque parameter between the two scans,
such as for
example radiodensity, volume, geometry, location, ratio or function of volume
to surface
area, heterogeneity index, radiodensity composition, radiodensity composition
as a
function of volume, ratio of radiodensity to volume, diffusivity, any
combinations or
relations thereof, and/or the like of one or more regions of plaque. In some
embodiments,
the system can be configured to determine the heterogeneity index of one or
more regions
of plaque by generating a spatial mapping or a three-dimensional histogram of
radiodensity
values across a geometric shape of one or more regions of plaque. In some
embodiments,
the system is configured to analyze changes in one or more non-image based
metrics, such
as for example serum biomarkers, genetics, omics, transcriptomics,
microbiomics, and/or
metabolomics.
[0297]
In some embodiments, the system is configured to determine a change
in plaque composition in terms of radiodensity or stable v. unstable plaque
between the two
scans. For example, in some embodiments, the system is configured to determine
a change
in percentage of higher radiodensity or stable plaques v. lower radiodensity
or unstable
plaques between the two scans. In some embodiments, the system can be
configured to
track a change in higher radiodensity plaques v. lower radiodensity plaques
between the
two scans. In some embodiments, the system can be configured to define higher
radiodensity plaques as those with a Hounsfield unit of above 1000 and lower
radiodensity
plaques as those with a Hounsfield unit of below 1000.
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[0298]
In some embodiments, at block 380, the system can be configured to
determine the progression or regression of plaque and/or any other related
measurement,
condition, assessment, or related disease based on the comparison of the one
or more
parameters derived from two or more scans and/or change in one or more non-
image based
metrics, such as serum biomarkers, genetics, omics, transcriptomics,
microbiomics, and/or
metabolomics. For example, in some embodiments, the system can be configured
to
determine the progression and/or regression of plaque in general,
atherosclerosis, stenosis,
risk or presence of ischemia, and/or the like. Further, in some embodiments,
the system
can be configured to automatically and/or dynamically generate a CAD-RADS
score of the
subject based on the quantified or calculated stenosis, as derived from the
two medical
images. Additional detail regarding generating a CAD-RADS score is described
herein in
relation to Figure 3C. In some embodiments, the system can be configured to
determine a
progression or regression in the CAD-RADS score of the subject. In some
embodiments,
the system can be configured to compare the plaque parameters individually
and/or
combining one or more of them as a weighted measure. For example, in some
embodiments, the system can be configured to weight the plaque parameters
equally,
differently, logarithmically, algebraically, and/or utilizing another
mathematical transform.
In some embodiments, the system can be configured to utilize only some or all
of the
quantified plaque parameters.
[0299]
In some embodiments, the state of plaque progression as determined by
the system can include one of four categories, including rapid plaque
progression, non-
rapid calcium dominant mixed response, non-rapid non-calcium dominant mixed
response,
or plaque regression. In some embodiments, the system is configured to
classify the state
of plaque progression as rapid plaque progression when a percent atheroma
volume
increase of the subject is more than 1% per year. In some embodiments, the
system is
configured to classify the state of plaque progression as non-rapid calcium
dominant mixed
response when a percent atheroma volume increase of the subject is less than
1% per year
and calcified plaque represents more than 50% of total new plaque formation.
In some
embodiments, the system is configured to classify the state of plaque
progression as non-
rapid non-calcium dominant mixed response when a percent atheroma volume
increase of
the subject is less than 1% per year and non-calcified plaque represents more
than 50% of
total new plaque formation. In some embodiments, the system is configured to
classify the
state of plaque progression as plaque regression when a decrease in total
percent atheroma
volume is present.
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[0300]
In some embodiments, at block 382, the system can be configured to
generate a proposed treatment plan for the subject. For example, in some
embodiments,
the system can be configured to generate a proposed treatment plan for the
subject based
on the determined progression or regression of plaque and/or any other related
measurement, condition, assessment, or related disease based on the comparison
of the one
or more parameters derived from two or more scans.
[0301]
In some embodiments, one or more processes described herein in
connection with Figure 3D can be repeated. For example, one or more processes
described
herein can be repeated and the analytical results thereof can be used for
continued tracking
of a plaque-based disease and/or other purposes.
Determination of Cause of Change in Calcium
[0302]
In some embodiments, the systems, methods and devices disclosed
herein can be configured to generate analysis and/or reports that can
determine the likely
cause of an increased calcium score. A high or increased calcium score alone
is not
representative of any specific cause, either positive or negative. Rather, in
general, there
can be various possible causes for a high or increased calcium score. For
example, in some
cases, a high or increased calcium score can be an indicator of significant
heart disease
and/or that the patient is at increased risk of a heart attack. Also, in some
cases, a high or
increased calcium score can be an indicator that the patient is increasing the
amount of
exercise performed, because exercise can convert fatty material plaque within
the artery
vessel. In some cases, a high or increased calcium score can be an indicator
of the patient
beginning a statin regimen wherein the statin is converting the fatty material
plaque into
calcium. Unfortunately, a blood test alone cannot be used to determine which
of the
foregoing reasons is the likely cause of an increased calcium score. In some
embodiments,
by utilizing one or more techniques described herein, the system can be
configured to
determine the cause of an increased or high calcium score.
[0303]
More specifically, in some embodiments, the system can be configured
to track a particular segment of an artery wall vessel of a patient in such a
way to monitor
the conversion of a fatty deposit material plaque lesion to a mostly calcified
plaque deposit,
which can be helpful in determining the cause of an increase calcium score,
such as one or
more of the causes identified above. In addition, in some embodiments, the
system can be
configured to determine and/or use the location, size, shape, diffusivity
and/or the
attenuation radiodensity of one or more regions of calcified plaque to
determine the cause
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of an increase in calcium score. As a non-limiting example, if a calcium
plaque increases
in density, this may represent a stabilization of plaque by treatment or
lifestyle, whereas if
a new calcium plaque forms where one was not there before (particularly with a
lower
attenuation density), this may represent an adverse finding of disease
progression rather
than stabilization. In some embodiments, one or more processes and techniques
described
herein may be applied for non-contrast CT scans (such as an ECG gated coronary
artery
calcium score or non-ECG gated chest CT) as well as contrast-enhanced CT scans
(such as
a coronary CT angiogram).
[0304]
As another non-limiting example, the CT scan image acquisition
parameters can be altered to improve understanding of calcium changes over
time. As an
example, traditional coronary artery calcium imaging is done using a 2.5-3.0
mm slice
thickness and detecting voxels/pixels that are 130 Hounsfield units or
greater. An
alternative may be to do "thin- slice imaging with 0.5 mm slice thickness or
similar; and
detecting all Hounsfield units densities below 130 and above a certain
threshold (e.g., 100)
that may identify less dense calcium that may be missed by an arbitrary 130
Hounsfield
unit threshold.
[0305]
Figure 3E is a flowchart illustrating an overview of an example
embodiment(s) of a method for determination of cause of change in calcium
score, whether
an increase or decrease, based on medical image analysis.
[0306]
As illustrated in Figure 3E, in some embodiments, the system can be
configured to access a first calcium score and/or a first set of plaque
parameters of a subject
at block 384. The first calcium score and/or a first set of plaque parameters
can be derived
from a medical image of a subject and/or from a blood test at a first point in
time. In some
embodiments, the medical image can be stored in a medical image database 100
and can
include any of the types of medical images described above, including for
example CT,
non-contrast CT, contrast-enhanced CT, MR, DECT, Spectral CT, and/or the like.
In some
embodiments, the medical image of the subject can comprise the coronary
region, coronary
arteries, carotid arteries, renal arteries, abdominal aorta, cerebral
arteries, lower extremities,
and/or upper extremities of the subject. In some embodiments, the set of
plaque parameters
can be stored in a plaque parameter database 370, which can include any of the
quantified
plaque parameters discussed above in relation to blocks 208, 201, 203, 205,
207, 209,
and/or 211.
[0307]
In some embodiments, the system can be configured to directly access
and/or retrieve the first calcium score and/or first set of plaque parameters
that are stored
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in a calcium score database 398 and/or plaque parameter database 370
respectively. In
some embodiments, the plaque parameter database 370 and/or calcium score
database 298
can be locally accessible and/or remotely accessible by the system via a
network
connection. In some embodiments, the system can be configured to dynamically
and/or
automatically derive the first set of plaque parameters and/or calcium score
from a medical
image and/or blood test of the subject taken from a first point in time.
[0308]
In some embodiments, at block 386, the system can be configured to
access a second calcium score and/or second medical image(s) of the subject,
which can be
obtained from the subject at a later point in time than the first calcium
score and/or medical
image from which the first set of plaque parameters were derived. For example,
in some
embodiments, the second calcium score can be derived from the second medical
image
and/or a second blood test taken of the subject at a second point in time. In
some
embodiments, the second calcium score can be stored in the calcium score
database 398.
In some embodiments, the medical image can be stored in a medical image
database 100
and can include any of the types of medical images described above, including
for example
CT, non-contrast CT, contrast-enhanced CT, MR, DECT, Spectral CT, and/or the
like.
[0309]
In some embodiments, at block 388, the system can be configured to
compare the first calcium score to the second calcium score and determine a
change in the
calcium score. However, as discussed above, this alone typically does not
provide insight
as to the cause of the change in calcium score, if any. In some embodiments,
if there is no
statistically significant change in calcium score between the two readings,
for example if
any difference is below a predetermined threshold value, then the system can
be configured
to end the analysis of the change in calcium score. In some embodiments, if
there is a
statistically significant change in calcium score between the two readings,
for example if
the difference is above a predetermined threshold value, then the system can
be configured
to continue its analysis.
[0310]
In particular, in some embodiments, at block 390, the system can be
configured to dynamically and/or automatically derive a second set of plaque
parameters
from the second medical image taken from the second point in time. In some
embodiments,
the second set of plaque parameters can include any of the quantified plaque
parameters
discussed above in relation to blocks 208, 201, 203, 205, 207, 209, and/or
211. In some
embodiments, the system can be configured to store the derived or determined
second set
of plaque parameters in the plaque parameter database 370.
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[0311]
In some embodiments, at block 392, the system can be configured to
analyze changes in one or more plaque parameters between the first set derived
from a
medical image taken at a first point in time to the second set derived from a
medical image
taken at a later point in time. For example, in some embodiments, the system
can be
configured to compare a quantified plaque parameter between the two scans,
such as for
example radiodensity, volume, geometry, location, ratio or function of volume
to surface
area, heterogeneity index, radiodensity composition, radiodensity composition
as a
function of volume, ratio of radiodensity to volume, diffusivity, any
combinations or
relations thereof, and/or the like of one or more regions of plaque and/or one
or more
regions surrounding plaque. In some embodiments, the system can be configured
to
determine the heterogeneity index of one or more regions of plaque by
generating a spatial
mapping or a three-dimensional histogram of radiodensity values across a
geometric shape
of one or more regions of plaque. In some embodiments, the system is
configured to
analyze changes in one or more non-image based metrics, such as for example
serum
biomarkers, genetics, omics, transcriptomics, microbiomics, and/or
metabolomics.
[0312]
In some embodiments, the system is configured to determine a change
in plaque composition in terms of radiodensity or stable v. unstable plaque
between the two
scans. For example, in some embodiments, the system is configured to determine
a change
in percentage of higher radiodensity or stable plaques v. lower radiodensity
or unstable
plaques between the two scans. In some embodiments, the system can be
configured to
track a change in higher radiodensity plaques v. lower radiodensity plaques
between the
two scans. In some embodiments, the system can be configured to define higher
radiodensity plaques as those with a Hounsfield unit of above 1000 and lower
radiodensity
plaques as those with a Hounsfield unit of below 1000.
[0313]
In some embodiments, the system can be configured to compare the
plaque parameters individually and/or combining one or more of them as a
weighted
measure. For example, in some embodiments, the system can be configured to
weight the
plaque parameters equally, differently, logarithmically, algebraically, and/or
utilizing
another mathematical transform. In some embodiments, the system can be
configured to
utilize only some or all of the quantified plaque parameters.
[0314]
In some embodiments, at block 394, the system can be configured to
characterize the change in calcium score of the subject based on the
comparison of the one
or more plaque parameters, whether individually and/or combined or weighted.
In some
embodiments, the system can be configured to characterize the change in
calcium score as
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positive, neutral, or negative. For example, in some embodiments, if the
comparison of
one or more plaque parameters reveals that plaque is stabilizing or showing
high
radiodensity values as a whole for the subject without generation of any new
plaque, then
the system can report that the change in calcium score is positive. In
contrast, if the
comparison of one or more plaque parameters reveals that plaque is
destabilizing as a whole
for the subject, for example due to generation of new unstable regions of
plaque with low
radiodensity values, without generation of any new plaque, then the system can
report that
the change in calcium score is negative. In some embodiments, the system can
be
configured to utilize any or all techniques of plaque quantification and/or
tracking of
plaque-based disease analysis discussed herein, include those discussed in
connection with
Figures 3A, 3B, 3C, and 3D.
[0315]
As a non-limiting example, in some embodiments, the system can be
configured to characterize the cause of a change in calcium score based on
determining and
comparing a change in ratio between volume and radiodensity of one or more
regions of
plaque between the two scans. Similarly, in some embodiments, the system can
be
configured to characterize the cause of a change in calcium score based on
determining and
comparing a change in diffusivity and/or radiodensity of one or more regions
of plaque
between the two scans. For example, if the radiodensity of a region of plaque
has increased,
the system can be configured to characterize the change or increase in calcium
score as
positive. In some embodiments, if the system identifies one or more new
regions of plaque
in the second image that were not present in the first image, the system can
be configured
to characterize the change in calcium score as negative. In some embodiments,
if the
system determines that the volume to surface area ratio of one or more regions
of plaque
has decreased between the two scans, the system can be configured to
characterize the
change in calcium score as positive. In some embodiments, if the system
determines that
a heterogeneity or heterogeneity index of a region is plaque has decreased
between the two
scans, for example by generating and/or analyzing spatial mapping of
radiodensity values,
then the system can be configured to characterize the change in calcium score
as positive.
[0316]
In some embodiments, the system is configured to utilize an AT, ML,
and/or other algorithm to characterize the change in calcium score based on
one or more
plaque parameters derived from a medical image. For example, in some
embodiments, the
system can be configured to utilize an Al and/or ML algorithm that is trained
using a CNN
and/or using a dataset of known medical images with identified plaque
parameters
combined with calcium scores. In some embodiments, the system can be
configured to
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characterize a change in calcium score by accessing known datasets of the same
stored in
a database. For example, the known dataset may include datasets of changes in
calcium
scores and/or medical images and/or plaque parameters derived therefrom of
other subjects
in the past. In some embodiments, the system can be configured to characterize
a change
in calcium score and/or determine a cause thereof on a vessel-by-vessel basis,
segment-by-
segment basis, plaque-by-plaque basis, and/or a subject basis.
[0317]
In some embodiments, at block 396, the system can be configured to
generate a proposed treatment plan for the subject. For example, in some
embodiments,
the system can be configured to generate a proposed treatment plan for the
subject based
on the change in calcium score and/or characterization thereof for the
subject.
[0318]
In some embodiments, one or more processes described herein in
connection with Figure 3E can be repeated. For example, one or more processes
described
herein can be repeated and the analytical results thereof can be used for
continued tracking
and/or characterization of changes in calcium score for a subject and/or other
purposes.
Prognosis of Cardiovascular Event
[0319]
In some embodiments, the systems, devices, and methods described
herein are configured to generate a prognosis of a cardiovascular event for a
subject based
on one or more of the medical image-based analysis techniques described
herein. For
example, in some embodiments, the system is configured to determine whether a
patient is
at risk for a cardiovascular event based on the amount of bad plaque buildup
in the patient's
artery vessels. For this purpose, a cardiovascular event can include clinical
major
cardiovascular events, such as heart attack, stroke or death, as well as
disease progression
and/or ischemia.
[0320]
In some embodiments, the system can identify the risk of a
cardiovascular event based on a ratio of the amount and/or volume of bad
plaque buildup
versus the total surface area and/or volume of some or all of the artery
vessels in a patient.
In some embodiments, if the foregoing ratio exceeds a certain threshold, the
system can be
configured to output a certain risk factor and/or number and/or level
associated with the
patient. In some embodiments, the system is configured to determine whether a
patient is
at risk for a cardiovascular event based on an absolute amount or volume or a
ratio of the
amount or volume bad plaque buildup in the patient's artery vessels compared
to the total
volume of some or all of the artery vessels. In some embodiments, the system
is configured
to determine whether a patient is at risk for a cardiovascular event based on
results from
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blood chemistry or biomarker tests of the patient, for example whether certain
blood
chemistry or biomarker tests of the patient exceed certain threshold levels.
In some
embodiments, the system is configured to receive as input from the user or
other systems
and/or access blood chemistry or biomarker tests data of the patient from a
database system.
In some embodiments, the system can be configured to utilize not only artery
information
related to plaque, vessel morphology, and/or stenosis but also input from
other imaging
data about the non-coronary cardiovascular system, such as subtended left
ventricular mass,
chamber volumes and size, valvular morphology, vessel (e.g., aorta, pulmonary
artery)
morphology, fat, and/or lung and/or bone health. In some embodiments, the
system can
utilize the outputted risk factor to generate a treatment plan proposal. For
example, the
system can be configured to output a treatment plan that involves the
administration of
cholesterol reducing drugs, such as statins, in order to transform the soft
bad plaque into
hard plaque that is safer and more stable for a patient. In general, hard
plaque that is largely
calcified can have a significant lower risk of rupturing into the artery
vessel thereby
decreasing the chances of a clot forming in the artery vessel which can
decrease a patient's
risk of a heart attack or other cardiac event.
[0321]
Figure 4A is a flowchart illustrating an overview of an example
embodiment(s) of a method for prognosis of a cardiovascular event based on
and/or derived
from medical image analysis.
[0322]
As illustrated in Figure 4A, in some embodiments, the system can be
configured to access a medical image at block 202, such as a CT scan of a
coronary region
of a subject, which can be stored in a medical image database 100. Further, in
some
embodiments, the system can be configured to identify one or more arteries at
block 204
and/or one or more regions of plaque at block 206. In addition, in some
embodiments, the
system can be configured to determine one or more vascular morphology and/or
quantified
plaque parameters at block 208. For example, in some embodiments, the system
can be
configured to determine a geometry and/or volume of a region of plaque and/or
a vessel, a
ratio or function of volume to surface area of a region of plaque, a
heterogeneity or
homogeneity index of a region of plaque, radiodensity of a region of plaque
and/or a
composition thereof by ranges of radiodensity values, a ratio of radiodensity
to volume of
a region of plaque, and/or a diffusivity of a region of plaque. in addition,
in some
embodiments, at block 210, the system can be configured to classify one or
more regions
of plaque as stable v. unstable or good v. bad based on the one or more
vascular morphology
parameters and/or quantified plaque parameters determined and/or derived from
raw
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medical images. Additional detail regarding the processes and techniques
represented in
blocks 202, 204, 206, 208, and 210 can be found in the description above in
relation to
Figure 2A.
[0323]
In some embodiments, the system at block 412 is configured to generate
a ratio of bad plaque to the vessel on which the bad plaque appears. More
specifically, in
some embodiments, the system can be configured to determine a total surface
area of a
vessel identified on a medical image and a surface area of all regions of bad
or unstable
plaque within that vessel. Based on the foregoing, in some embodiments, the
system can
be configured to generate a ratio of surface area of all bad plaque within a
particular vessel
to the surface area of the entire vessel or a portion thereof shown in a
medical image.
Similarly, in some embodiments, the system can be configured to determine a
total volume
of a vessel identified on a medical image and a volume of all regions of bad
or unstable
plaque within that vessel. Based on the foregoing, in some embodiments, the
system can
be configured to generate a ratio of volume of all bad plaque within a
particular vessel to
the volume of the entire vessel or a portion thereof shown in a medical image.
[0324]
In some embodiments, at block 414, the system is further configured to
determine a total absolute volume and/or surface area of all bad or unstable
plaque
identified in a medical image. Also, in some embodiments, at block 416, the
system is
configured to determine a total absolute volume of all plaque, including good
plaque and
bad plaque, identified in a medical image. Further, in some embodiments, at
block 418,
the system can be configured to access or retrieve results from a blood
chemistry and/or
biomarker test of the patient and/or other non-imaging test results.
Furthermore, in some
embodiments, at block 422, the system can be configured to access and/or
analyze one or
more non-coronary cardiovascular system medical images.
[0325]
In some embodiments, at block 420, the system can be configured to
analyze one or more of the generated ratio of bad plaque to a vessel, whether
by surface
area or volume, total absolute volume of bad plaque, total absolute volume of
plaque, blood
chemistry and/or biomarker test results, and/or analysis results of one or
more non-coronary
cardiovascular system medical images to determine whether one or more of these
parameters, either individually and/or combined, is above a predetermined
threshold. For
example, in some embodiments, the system can be configured to analyze one or
more of
the foregoing parameters individually by comparing them to one or more
reference values
of healthy subjects and/or subjects at risk of a cardiovascular event. In some
embodiments,
the system can be configured to analyze a combination, such as a weighted
measure, of one
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or more of the foregoing parameters by comparing the combined or weighted
measure
thereof to one or more reference values of healthy subjects and/or subjects at
risk of a
cardiovascular event. In some embodiments, the system can be configured to
weight one
or more of these parameters equally. In some embodiments, the system can be
configured
to weight one or more of these parameters differently. In some embodiments,
the system
can be configured to weight one or more of these parameters logarithmically,
algebraically,
and/or utilizing another mathematical transform. In some embodiments, the
system can be
configured to utilize only some of the aforementioned parameters, either
individually,
combined, and/or as part of a weighted measure.
103261
In some embodiments, at block 424, the system is configured to generate
a prognosis for a cardiovascular event for the subject. In particular, in some
embodiments,
the system is configured to generate a prognosis for cardiovascular event
based on one or
more of the analysis results of the generated ratio of bad plaque to a vessel,
whether by
surface area or volume, total absolute volume of bad plaque, total absolute
volume of
plaque, blood chemistry and/or biomarker test results, and/or analysis results
of one or more
non-coronary cardiovascular system medical images. In some embodiments, the
system is
configured to generate the prognosis utilizing an AT, ML, and/or other
algorithm. In some
embodiments, the generated prognosis comprises a risk score or risk assessment
of a
cardiovascular event for the subject. In some embodiments, the cardiovascular
event can
include one or more of atherosclerosis, stenosis, ischemia, heart attack,
and/or the like.
[0327]
In some embodiments, at block 426, the system can be configured to
generate a proposed treatment plan for the subject. For example, in some
embodiments,
the system can be configured to generate a proposed treatment plan for the
subject based
on the change in calcium score and/or characterization thereof for the
subject. In some
embodiments, the generated treatment plan can include use of statins,
lifestyle changes,
and/or surgery.
[0328]
In some embodiments, one or more processes described herein in
connection with Figure 4A can be repeated. For example, one or more processes
described
herein can be repeated and the analytical results thereof can be used for
continued prognosis
of a cardiovascular event for a subject and/or other purposes.
Patient-Specific Stent Determination
[0329]
In some embodiments, the systems, methods, and devices described
herein can be used to determine and/or generate one or more parameters for a
patient-
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specific stent and/or selection or guidance for implantation thereof In
particular, in some
embodiments, the systems disclosed herein can be used to dynamically and
automatically
determine the necessary stent type, length, diameter, gauge, strength, and/or
any other stent
parameter for a particular patient based on processing of the medical image
data, for
example using AT, ML, and/or other algorithms.
[0330]
In some embodiments, by determining one or more patient-specific stent
parameters that are best suited for a particular artery area, the system can
reduce the risk of
patient complications and/or insurance risks because if too large of a stent
is implanted,
then the artery wall can be stretched too thin resulting in a possible
rupture, or undesirable
high flow, or other issues. On the other hand, if too small of a stent is
implanted, then the
artery wall might not be stretched open enough resulting in too little blood
flow or other
issues.
[0331]
In some embodiments, the system is configured to dynamically identify
an area of stenosis within an artery, dynamically determine a proper diameter
of the
identified area of the artery, and/or automatically select a stent from a
plurality of available
stent options. In some embodiments, the selected stent can be configured to
prop open the
artery area after implantation to the determined proper artery diameter. In
some
embodiments, the proper artery diameter is determined to be equivalent or
substantially
equivalent to what the diameter would naturally be without stenosis. In some
embodiments,
the system can be configured to dynamically generate a patient-specific
surgical plan for
implanting the selected stent in the identified artery area. For example, the
system can be
configured to determine whether a bifurcation of the artery is near the
identified artery area
and generate a patient-specific surgical plan for inserting two guidewires for
handling the
bifurcation and/or determining the position for jailing and inserting a second
stent into the
bifurcation.
[0332]
Figure 4B is a flowchart illustrating an overview of an example
embodiment(s) of a method for determination of patient-specific stent
parameters based on
medical image analysis.
[0333]
As illustrated in Figure 4B, in some embodiments, the system can be
configured to access a medical image at block 202, such as a CT scan of a
coronary region
of a subject. Further, in some embodiments, the system can be configured to
identify one
or more arteries at block 204 and/or one or more regions of plaque at block
206. In addition,
in some embodiments, the system can be configured to determine one or more
vascular
morphology and/or quantified plaque parameters at block 208. For example, in
some
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embodiments, the system can be configured to determine a geometry and/or
volume of a
region of plaque and/or a vessel at block 201, a ratio or function of volume
to surface area
of a region of plaque at block 203, a heterogeneity or homogeneity index of a
region of
plaque at block 205, radiodensity of a region of plaque and/or a composition
thereof by
ranges of radiodensity values at block 207, a ratio of radiodensity to volume
of a region of
plaque at block 209, and/or a diffusivity of a region of plaque at block 211.
Additional
detail regarding the processes and techniques represented in blocks 202, 204,
206, 208,
201, 203, 205, 207, 209, and 211 can be found in the description above in
relation to Figure
2A.
103341
In some embodiments, at block 440, the system can be configured to
analyze the medical image to determine one or more vessel parameters, such as
the
diameter, curvature, vascular morphology, vessel wall, lumen wall, and/or the
like. In some
embodiments, the system can be configured to determine or derive from the
medical image
one or more vessel parameters as shown in the medical image, for example with
stenosis at
certain regions along the vessel. In some embodiments, the system can be
configured to
determine one or more vessel parameters without stenosis. For example, in some
embodiments, the system can be configured to graphically and/or hypothetically
remove
stenosis or plaque from a vessel to determine the diameter, curvature, and/or
the like of the
vessel if stenosis did not exist.
[0335]
In some embodiments, at block 442, the system can be configured to
determine whether a stent is recommended for the subject and, if so, one or
more
recommended parameters of a stent specific for that patient based on the
medical analysis.
For example, in some embodiments, the system can be configured to analyze one
or more
of the identified vascular morphology parameters, quantified plaque
parameters, and/or
vessel parameters. In some embodiments, the system can be configured to
utilize an Al,
ML, and/or other algorithm. In some embodiments, the system is configured to
analyze
one or more of the aforementioned parameters individually, combined, and/or as
a weighted
measure. In some embodiments, one or more of these parameters derived from a
medical
image, either individually or combined, can be compared to one or more
reference values
derived or collected from other subjects, including those who had a stent
implanted and
those who did not. in some embodiments, based on the determined parameters of
a patient-
specific stent, the system can be configured to determine a selection of a
preexisting stent
that matches those parameters and/or generate manufacturing instructions to
manufacture
a patient-specific stent with stent parameters derived from a medical image.
In some
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embodiments, the system can be configured to recommend a diameter of a stent
that is less
than or substantially equal to the diameter of an artery if stenosis did not
exist.
[0336]
In some embodiments, at block 444, the system can be configured to
generate a recommended surgical plan for stent implantation based on the
analyzed medical
image. For example, in some embodiments, the system can be configured to
determine
whether a bifurcation exists based on the medical image and/or generate
guidelines for the
positioning of guidewires and/or stent for the patient prior to surgery. As
such, in some
embodiments, the system can be configured to generate a detailed surgical plan
that is
specific to a particular patient based on medical image analysis of plaque
and/or other
parameters.
[0337]
In some embodiments, at block 446, the system is configured to access
or retrieve one or more medical images after stent implantation. In some
embodiments, at
block 448, the system can be configured to analyze the accessed medical image
to perform
post-implantation analysis. For example, in some embodiments, the system can
be
configured to derive one or more vascular morphology and/or plaque parameters,
including
any of those discussed herein in relation to block 208, after stent
implantation. Based on
analysis of the foregoing, in some embodiments, the system can generate
further proposed
treatment in some embodiments, such as for example recommended use of statins
or other
medications, lifestyle change, further surgery or stent implantation, and/or
the like.
[0338]
In some embodiments, one or more processes described herein in
connection with Figure 4B can be repeated. For example, one or more processes
described
herein can be repeated and the analytical results thereof can be used to
determine the need
for and/or parameters of an additional patient-specific stent for a patient
and/or other
purposes.
Patient-Specific Report
[0339]
In some embodiments, the system is configured to dynamically generate
a patient-specific report based on the analysis of the processed data
generated from the raw
CT scan data. In some embodiments, the patient specific report is dynamically
generated
based on the processed data. In some embodiments, the written report is
dynamically
generated based on selecting and/or combining certain phrases from a database,
wherein
certain words, terms, and/or phrases are altered to be specific to the patient
and the
identified medical issues of the patient. In some embodiments, the system is
configured to
dynamically select one or more images from the image scanning data and/or the
system
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generated image views described herein, wherein the selected one or more
images are
dynamically inserted into the written report in order to generate a patient-
specific report
based on the analysis of the processed data.
[0340]
In some embodiments, the system is configured to dynamically annotate
the selected one or more images for insertion into the patient specific
report, wherein the
annotations are specific to patient and/or are annotations based on the data
processing
performed by the devices, methods, and systems disclosed herein, for example,
annotating
the one or more images to include markings or other indicators to show where
along the
artery there exists bad plaque buildup that is significant.
103411
In some embodiments, the system is configured to dynamically generate
a report based on past and/or present medical data. For example, in some
embodiments,
the system can be configured to show how a patient's cardiovascular health has
changed
over a period. In some embodiments, the system is configured to dynamically
generate
phrases and/or select phrases from a database to specifically describe the
cardiovascular
health of the patient and/or how the cardiovascular disease has changed within
a patient.
[0342]
In some embodiments, the system is configured to dynamically select
one or more medical images from prior medical scanning and/or current medical
scanning
for insertion into the medical report in order to show how the cardiovascular
disease has
changed over time in a patient, for example, showing past and present images
juxtaposed
to each other, or for example, showing past images that are superimposed on
present images
thereby allowing a user to move or fade or toggle between past and present
images.
[0343]
In some embodiments, the patient-specific report is an interactive report
that allows a user to interact with certain images, videos, animations,
augmented reality
(AR), virtual reality (VR), and/or features of the report. In some
embodiments, the system
is configured to insert into the patient-specific report dynamically generated
illustrations or
images of patient artery vessels in order to highlight specific vessels and/or
portions of
vessels that contain or are likely to contain vascular disease that require
review or further
analysis. In some embodiments, the dynamically generated patient-specific
report is
configured to show a user the vessel walls using AR and/or VR.
[0344]
In some embodiments, the system is configured to insert into the
dynamically generated report any ratios and/or dynamically generated data
using the
methods, systems, and devices disclosed herein. In some embodiments, the
dynamically
generated report comprises a radiology report. In some embodiments, the
dynamically
generated report is in an editable document, such as Microsoft Word , in order
to allow
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the physician to make edits to the report. In some embodiments, the
dynamically generated
report is saved into a PACS (Picture Archiving and Communication System) or
other EMR
(electronic medical records) system.
[0345]
In some embodiments, the system is configured to transform and/or
translate data from the imaging into drawings or infographics in a video
format, with or
without audio, in order to transmit accurately the information in a way that
is better
understandable to any patient to improve literacy. In some embodiments, this
method of
improving literacy is coupled to a risk stratification tool that defines a
lower risk with higher
literacy, and a higher risk with lower literacy. In some embodiments, these
report outputs
may be patient-derived and/or patient-specific. In some embodiments, real
patient imaging
data (for example, from their CT) can be coupled to graphics from their CT
and/or drawings
from the CT to explain the findings further. In some embodiments, real patient
imaging
data, graphics data and/or drawings data can be coupled to an explaining
graphic that is not
from the patient but that can help the patient better understand (for example,
a video about
lipid-rich plaque).
[0346]
In some embodiments, these patient reports can be imported into an
application that allows for following disease over time in relation to control
of heart disease
risk factors, such as diabetes or hypertension. In some embodiments, an app
and/or user
interface can allow for following of blood glucose and blood pressure over
time and/or
relate the changes of the image over time in a way that augments risk
prediction.
[0347]
In some embodiments, the system can be configured to generate a video
report that is specific to the patient based on the processed data generated
from the raw CT
data. In some embodiments, the system is configured to generate and/or provide
a
personalized cinematic viewing experience for a user, which can be programmed
to
automatically and dynamically change content based upon imaging findings,
associated
auto-calculated diagnoses, and/or prognosis algorithms. In some embodiments,
the method
of viewing, unlike traditional reporting, is through a movie experience which
can be in the
form of a regular 2D movie and/or through a mixed reality movie experience
through AR
or VR. In some embodiments, in the case of both 2D and mixed reality, the
personalized
cinematic experience can be interactive with the patient to predict their
prognosis, such as
risk of heart attack, rate of disease progression, and/or i scherni a.
[0348]
In some embodiments, the system can be configured to dynamically
generate a video report that comprises both cartoon images and/or animation
along with
audio content in combination with actual CT image data from the patient. In
some
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embodiments, the dynamically generated video medical report is dynamically
narrated
based on selecting phrases, terms and/or other content from a database such
that a voice
synthesizer or pre-made voice content can be used for playback during the
video report. In
some embodiments, the dynamically generated video medical report is configured
to
comprise any of the images disclosed herein. In some embodiments, the
dynamically
generated video medical report can be configured to dynamically select one or
more
medical images from prior medical scanning and/or current medical scanning for
insertion
into the video medical report in order to show how the cardiovascular disease
has changed
over time in a patient. For example, in some embodiments, the report can show
past and
present images juxtaposed next to each other. In some embodiments, the report
can show
past images that are superimposed on present images thereby allowing a user to
toggle or
move or fade between past and present images. In some embodiments, the
dynamically
generated video medical report can be configured to show actual medical
images, such as
a CT medical image, in the video report and then transition to an illustrative
view or cartoon
view (partial or entirely an illustrative or cartoon view) of the actual
medical images,
thereby highlighting certain features of the patient's arteries. In some
embodiments, the
dynamically generated video medical report is configured to show a user the
vessel walls
using AR and/or VR.
[0349]
Figure 5A is a flowchart illustrating an overview of an example
embodiment(s) of a method for generation of a patient-specific medical report
based on
medical image analysis. As illustrated in Figure 5A, in some embodiments, the
system can
be configured to access a medical image at block 202. In some embodiments, the
medical
image can be stored in a medical image database 100. Additional detail
regarding the types
of medical images and other processes and techniques represented in block 202
can be
found in the description above in relation to Figure 2A.
[0350]
In some embodiments, at block 354, the system is configured to identify
one or more arteries, plaque, and/or fat in the medical image, for example
using AT, ML,
and/or other algorithms. Additional detail regarding the types of medical
images and other
processes and techniques represented in block 354 can be found in the
description above in
relation to Figure 3C.
[0351]
In some embodiments, at block 208, the system can be configured to
determine one or more vascular morphology and/or quantified plaque parameters.
For
example, in some embodiments, the system can be configured to determine a
geometry
and/or volume of a region of plaque and/or a vessel at block 201, a ratio or
function of
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volume to surface area of a region of plaque at block 203, a heterogeneity or
homogeneity
index of a region of plaque at block 205, radiodensity of a region of plaque
and/or a
composition thereof by ranges of radiodensity values at block 207, a ratio of
radiodensity
to volume of a region of plaque at block 209, and/or a diffusivity of a region
of plaque at
block 211. Additional detail regarding the processes and techniques
represented in blocks
208, 201, 203, 205, 207, 209, and 211 can be found in the description above in
relation to
Figure 2A.
[0352]
In some embodiments, at block 508, the system can be configured to
determine and/or quantify stenosis, atherosclerosis, risk of ischemia, risk of
cardiovascular
event or disease, and/or the like. The system can be configured to utilize any
techniques
and/or algorithms described herein, including but not limited to those
described above in
connection with block 358 and block 366 of Figure 3C.
[0353]
In some embodiments, at block 510, the system can be configured to
generate an annotated medical image and/or quantized color map using the
analysis results
derived from the medical image. For example, in some embodiments, the system
can be
configured to generate a quantized map showing one or more arteries, plaque,
fat, good
plaque, bad plaque, vascular morphologies, and/or the like.
[0354]
In some embodiments, at block 512, the system can be configured to
determine a progression of plaque and/or disease of the patient, for example
based on
analysis of previously obtained medical images of the subject. In some
embodiments, the
system can be configured to utilize any algorithms or techniques described
herein in
relation to disease tracking, including but not limited to those described in
connection with
block 380 and/or Figure 3D generally.
103551
In some embodiments, at block 514, the system can be configured to
generate a proposed treatment plan for the patient based on the determined
progression of
plaque and/or disease. In some embodiments, the system can be configured to
utilize any
algorithms or techniques described herein in relation to disease tracking and
treatment
generation, including but not limited to those described in connection with
block 382 and/or
Figure 3D generally.
[0356]
In some embodiments, at block 516, the system can be configured to
generate a patient-specific report. The patient-specific report can include
one or more
medical images of the patient and/or derived graphics thereof For example, in
some
embodiments, the patient report can include one or more annotated medical
images and/or
quantized color maps. In some embodiments, the patient-specific report can
include one
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or more vascular morphology and/or quantified plaque parameters derived from
the
medical image. In some embodiments, the patient-specific report can include
quantified
stenosis, atherosclerosis, ischemia, risk of cardiovascular event or disease,
CAD-RADS
score, and/or progression or tracking of any of the foregoing. In some
embodiments, the
patient-specific report can include a proposed treatment, such as statins,
lifestyle changes,
and/or surgery.
[0357]
In some embodiments, the system can be configured to access and/or
retrieve from a patient report database 500 one or more phrases,
characterizations, graphics,
videos, audio files, and/or the like that are applicable and/or can be used to
generate the
patient-specific report. In generating the patient-specific report, in some
embodiments, the
system can be configured to compare one or more parameters, such as those
mentioned
above and/or derived from a medical image of the patient, with one or more
parameters
previously derived from other patients. For example, in some embodiments, the
system
can be configured to compare one or more quantified plaque parameters derived
from the
medical image of the patient with one or more quantified plaque parameters
derived from
medical images of other patients in the similar or same age group. Based on
the
comparison, in some embodiments, the system can be configured to determine
which
phrases, characterizations, graphics, videos, audio files, and/or the like to
include in the
patient-specific report, for example by identifying similar previous cases. In
some
embodiments, the system can be configured to utilize an AT and/or ML algorithm
to
generate the patient-specific report. In sonic embodiments, the patient-
specific report can
include a document, AR experience, VR experience, video, and/or audio
component.
[0358]
Figures 5B-5I illustrate example embodiment(s) of a patient-specific
medical report generated based on medical image analysis. In particular,
Figure 5B
illustrates an example cover page of a patient-specific report.
[0359]
Figures 5C-51 illustrate portions of an example patient-specific
report(s). In some embodiments, a patient-specific report generated by the
system may
include only some or all of these illustrated portions. As illustrated in
Figures 5C-5I, in
some embodiments, the patient-specific report includes a visualization of one
or more
arteries and/or portions thereof, such as for example, the Right Coronary
Artery (RCA), R-
Posteri or Descending Artery (R-PDA), R-Posterolateral Branch (R-PLB), Left
Main (LM)
and Left Anterior Descending (LAD) Artery, 1st Diagonal (D1) Artery, 2nd
Diagonal (D2)
Artery, Circumflex (Cx) Artery, 1st Obtuse Marginal Branch (0M1), 2nd Obtuse
Marginal
Branch (0M2), Ramus Intermedius (RI), and/or the like. In some embodiments,
for each
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of the arteries included in the report, the system is configured to generate a
straightened
view for easy tracking along the length of the vessel, such as for example at
the proximal,
mid, and/or distal portions of an artery.
[0360]
In some embodiments, a patient-specific report generated by the system
includes a quantified measure of various plaque and/or vascular morphology-
related
parameters shown within the vessel. In some embodiments, for each or some of
the arteries
included in the report, the system is configured to generate and/or derive
from a medical
image of the patient and include in a patient-specific report a quantified
measure of the total
plaque volume, total low-density or non-calcified plaque volume, total non-
calcified plaque
value, and/or total calcified plaque volume. Further, in some embodiments, for
each or
some of the arteries included in the report, the system is configured to
generate and/or
derive from a medical image of the patient and include in a patient-specific
report a
quantified measure of stenosis severity, such as for example a percentage of
the greatest
diameter stenosis within the artery. In some embodiments, for each or some of
the arteries
included in the patient-specific report, the system is configured to generate
and/or derive
from a medical image of the patient and include in a patient-specific report a
quantified
measure of vascular remodeling, such as for example the highest remodeling
index.
Visualization / GUI
[0361]
Atherosclerosis, the buildup of fats, cholesterol and other substances in
and on your artery walls (e.g., plaque), which can restrict blood flow. The
plaque can burst,
triggering a blood clot. Although atherosclerosis is often considered a heart
problem, it can
affect arteries anywhere in the body. However, determining information about
plaque in
coronary arteries can be difficult due in part to imperfect imaging data,
aberrations that can
be present in coronary artery images (e.g., due to movement of the patient),
and differences
in the manifestation of plaque in different patients. Accordingly, neither
calculated
information derived from CT images, or visual inspection of the CT images,
alone provide
sufficient information to determine conditions that exist in the patient's
coronary arteries.
Portions of this disclosure describe information they can be determined from
CT images
using automatic or semiautomatic processes. For example, using a machine
learning
process has been trained on thousands of CT scans determine information
depicted in the
CT images, and/or utilizing analyst to review and enhance the results of the
machine
learning process, and the example user interfaces described herein can provide
the
determined information to another analyst or a medical practitioner. While the
information
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determined from the CT images is invaluable in assessing the condition of a
patient's
coronary arteries, visual analysis of the coronary arteries by skilled medical
practitioner,
with the information determined from the CT images in-hand, allows a more
comprehensive assessment of the patient's coronary arteries. As indicated
herein,
embodiments of the system facilitate the analysis and visualization of vessel
lumens, vessel
walls, plaque and stenosis in and around coronary vessels. This system can
display vessels
in multi-planar formats, cross-sectional views, 3D coronary artery tree view,
axial, sagittal,
and coronal views based on a set of computerized tomography (CT) images, e.g.,
generated
by a CT scan of a patient's vessels. The CT images can be Digital Imaging and
Communications in Medicine (DICOM) images, a standard for the communication
and
management of medical imaging information and related data. CT images, or CT
scans, as
used herein, is a broad term that refers to pictures of structures within the
body created by
computer controlled scanner. For example, by a scanner that uses an X-ray
beam. However,
it is appreciated that other radiation sources and/or imaging systems may
produce a set of
CT-like images. Accordingly, the use of the term "CT images- herein may refer
to any type
of imaging system having any type of imaging source that produces a set of
images
depicting "slices" of structures within a body, unless otherwise indicated.
One key aspect
of the user interface described herein is the precise correlation of the views
and information
that is displayed of the CT images. Locations in the CT images displayed on
portions (or
"panels-) of the user interface are correlated precisely by the system such
that the same
locations are displayed concurrently in a different views. By simultaneously
displaying a
portion of the coronary vessel in, for example, two, three, four, five or six
views
simultaneously, and allowing a practitioner to explore particular locations of
a coronary
vessel in one view while the other 2-6 views correspondingly show the exact
same location
provides an enormous amount of insight into the condition of the vessel and
allows the
practitioner/analyst to quickly and easily visually integrate the presented
information to
gain a comprehensive and accurate understanding of the condition of the
coronary vessel
being examined.
[0362]
Advantageously, the present disclosure allows CT images and data to be
analyzed in a more useful and accurate way, for users to interact and analyze
images and
data in a more analytically useful way and/or for computation analysis to be
performed in
a more useful way, for example to detect conditions requiring attention. The
graphical user
interfaces in the processing described herein allow a user to visualize
otherwise difficult to
define relationships between different information and views of coronary
arteries. In an
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example, displaying a portion of a coronary artery simultaneously in a CMPR
view, a
SMPR view, and a cross-sectional view can provide insight to an analyst of
plaque or
stenosis associated with the coronary artery that may not otherwise be
perceivable using a
fewer number of views. Similarly, displaying the portion of the coronary
artery in an axial
view, a sagittal view, and a coronal view, in addition to the CMPR view, the
SMPR view,
and the cross-sectional view can provide further information to the analyst
that would not
otherwise be perceivable with a fewer number of views of the coronary artery.
In various
embodiments, any of the information described or illustrated herein,
determined by the
system or an analyst interacting with the system, and other information (for
example, from
another outside source, e.g., an analyst) that relates to coronary
arteries/vessels associated
with the set of CT images ("artery information") including information
indicative of
stenosis and plaque of segments of the coronary vessels in the set of CT
images, and
information indicative of identification and location of the coronary vessels
in the set of
CT images, can be stored on the system and presented in various panels of the
user interface
and in reports. The present disclosure allows for easier and quicker analysis
of a patient's
coronary arteries and features associate with coronary arteries. The present
disclosure also
allows faster analysis of coronary artery data by allowing quick and accurate
access to
selected portions of coronary artery data. Without using the present system
and methods of
the disclosure, quickly selecting, displaying, and analyzing CT images and
coronary artery
information, can be cumbersome and inefficient, and may lead to analyst
missing critical
information in their analysis of a patient's coronary arteries, which may lead
to inaccurate
evaluation of a patient's condition.
[0363]
In various embodiments, the system can identify a patient's coronary
arteries either automatically (e.g., using a machine learning algorithm during
the
preprocessing step of set of CT images associated with a patient), or
interactively (e.g., by
receiving at least some input form a user) by an analyst or practitioner using
the system. As
described herein, in some embodiments, the processing of the raw CT scan data
can
comprise analysis of the CT data in order to determine and/or identify the
existence and/or
nonexistence of certain artery vessels in a patient. As a natural occurring
phenomenon,
certain arteries may be present in certain patients whereas such certain
arteries may not
exist in other patients. in some embodiments, the system can be configured to
identify and
label the artery vessels detected in the scan data. In certain embodiments,
the system can
be configured to allow a user to click upon a label of an identified artery
within the patient,
and thereby allowing that artery to be highlighted in an electronic
representation of a
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plurality of artery vessels existing in the patient. In some embodiments, the
system is
configured to analyze arteries present in the CT scan data and display various
views of the
arteries present in the patient, for example within 10-15 minutes or less. In
contrast, as an
example, conducting a visual assessment of a CT to identify stenosis alone,
without
consideration of good or bad plaque or any other factor, can take anywhere
between 15
minutes to more than an hour depending on the skill level, and can also have
substantial
variability across radiologists and/or cardiac imagers.
[0364]
Although some systems may allow an analyst to view the CT images
associated with a patient, they lack the ability to display all of the
necessary views, in real
or near real-time, with correspondence between 3-D artery tree views of
coronary arteries
specific to a patient, multiple SMPR views, and a cross-sectional, as well as
an axial view,
a sagittal view, and/or the coronal view. Embodiments of the system can be
configured
this display one or more of the use, or all of the use, which provides
unparalleled visibility
of a patient's coronary arteries, and allows an analyst or practitioner to
perceive features
and information that is simply may not be perceivable without these views.
That is, a user
interface configured to show all of these views, as well as information
related to the
displayed coronary vessel, allows an analyst or practitioner to use their own
experience in
conjunction with the information that the system is providing, to better
identify conditions
of the arteries which can help them make a determination on treatments for the
patient. In
addition, the information that is determined by the system and displayed by
the user
interface that cannot be perceived by an analyst or practitioner is presented
in such a manner
that is easy to understand and quick to assimilate. As an example, the
knowledge of actual
radiodensity values of plaque is not something that analyst and determine
simply by looking
at the CT image, but the system can and present a full analysis of all plaque
is found.
[0365]
In general, arteries vessels are curvilinear in nature. Accordingly, the
system can be configured to straighten out such curvilinear artery vessels
into a
substantially straight-line view of the artery, and in some embodiments, the
foregoing is
referred to as a straight multiplanar reformation (MPR) view. In some
embodiments, the
system is configured to show a dashboard view with a plurality of artery
vessels showing
in a straight multiplanar reformation view. In some embodiments, the linear
view of the
artery vessels shows a cross-sectional view along a longitudinal axis (or the
length of the
vessel or a long axis) of the artery vessel. In some embodiments, the system
can be
configured to allow the user to rotate in a 3600 fashion about the
longitudinal axis of the
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substantially linear artery vessels in order for the user to review the vessel
walls from
various views and angles. In some embodiments, the system is configured to not
only show
the narrowing of the inner vessel diameter but also characteristics of the
inner and/or outer
vessel wall itself In some embodiments, the system can be configured to
display the
plurality of artery vessels in a multiple linear views, e.g., in an SMPR view.
[0366]
In some embodiments, the system can be configured to display the
plurality of artery vessels in a perspective view in order to better show the
user the
curvatures of the artery vessels. In some embodiments, the perspective view is
referred to
as a curved multiplanar reformation view. In some embodiments, the perspective
view
comprises the CT image of the heart and the vessels, for example, in an artery
tree view. In
some embodiments, the perspective view comprises a modified CT image showing
the
artery vessels without the heart tissue displayed in order to better highlight
the vessels of
the heart. In some embodiments, the system can be configured to allow the user
to rotate
the perspective view in order to display the various arteries of the patient
from different
perspectives. In some embodiments, the system can be configured to show a
cross-
sectional view of an artery vessel along a latitudinal axis (or the width of
the vessel or short
axis). In contrast to the cross-sectional view along a longitudinal axis, in
some
embodiments, the system can allow a user to more clearly see the stenosis or
vessel wall
narrowing by viewing the artery vessel from a cross-sectional view across a
latitudinal axis.
[0367]
In some embodiments, the system is configured to display the plurality
of artery vessels in an illustrative view or cartoon view. In the illustrative
view of the artery
vessels, in some embodiments, the system can utilize solid coloring or grey
scaling of the
specific artery vessels or sections of specific artery vessels to indicate
varying degrees of
risk for a cardiovascular event to occur in a particular artery vessel or
section of artery
vessel. For example, the system can be configured to display a first artery
vessel in yellow
to indicate a medium risk of a cardiovascular event occurring in the first
artery vessel while
displaying a second artery vessel in red to indicate a high risk of a
cardiovascular event
occurring in the second artery vessel. In some embodiments, the system can be
configured
to allow the user to interact with the various artery vessels and/or sections
of artery vessels
in order to better understand the designated risk associated with the artery
vessel or section
of artery vessel. In some embodiments, the system can allow the user to switch
from the
illustrative view to a CT view of the arteries of the patient.
[0368]
In some embodiments, the system can be configured to display in a
single dashboard view all or some of the various views described herein. For
example, the
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system can be configured to display the linear view with the perspective view.
In another
example, the system can be configured to display the linear view with the
illustrative view.
[0369]
In some embodiments, the processed CT image data can result in
allowing the system to utilize such processed data to display to a user
various arteries of a
patient. As described above, the system can be configured to utilize the
processed CT data
in order to generate a linear view of the plurality of artery vessels of a
patient. In some
embodiments, the linear view displays the arteries of a patient as in a linear
fashion to
resemble a substantially straight line. In some embodiments, the generating of
the linear
view requires the stretching of the image of one or more naturally occurring
curvilinear
artery vessels. In some embodiments, the system can be configured to utilize
such
processed data to allow a user to rotate a displayed linear view of an artery
in a 360
rotatable fashion. In some embodiments, the processed CT image data can
visualize and
compare the artery morphologies over time, i.e., throughout the cardiac cycle.
The dilation
of the arteries, or lack thereof, may represent a healthy versus sick artery
that is not capable
of vasodilation. In some embodiments, a prediction algorithm can be made to
determine
the ability of the artery to dilate or not, by simply examining a single point
in time.
[0370]
As mentioned above, aspects of the system can help to visualize a
patient's coronary arteries. In some embodiments, the system can be configured
to utilize
the processed data from the raw CT scans in order to dynamically generate a
visualization
interface for a user to interact with and/or analyze the data for a particular
patient. The
visualization system can display multiple arteries associated with a patient's
heart. The
system can be configured to display multiple arteries in a substantially
linear fashion even
though the arteries are not linear within the body of the patient. In some
embodiments, the
system can be configured to allow the user to scroll up and down or left to
right along the
length of the artery in order to visualize different areas of the artery. In
some embodiments,
the system can be configured to allow a user to rotate in a 360 fashion an
artery in order
to allow the user to see different portions of the artery at different angles.
[0371]
Advantageously, the system can be configured to comprise or generate
markings in areas where there is an amount of plaque buildup that exceeds a
threshold level.
In some embodiments, the system can be configured to allow the user to target
a particular
area of the artery for further examination. The system can be configured to
allow the user
to click on one or more marked areas of the artery in order to display the
underlying data
associated with the artery at a particular point along the length of the
artery. In some
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embodiments, the system can be configured to generate a cartoon rendition of
the patient's
arteries. In some embodiments, the cartoon or computer-generated
representation of the
arteries can comprise a color-coded scheme for highlighting certain areas of
the patient's
arteries for the user to examine further. In some embodiments, the system can
be
configured to generate a cartoon or computer-generated image of the arteries
using a red
color, or any other graphical representation, to signify arteries that require
further analysis
by the user. In some embodiments, the system can label the cartoon
representation of the
arteries, and the 3D representation of the arteries described above, with
stored coronary
vessel labels according to the labeling scheme. If a user desires, the
labeling scheme can be
changed or refined and preferred labels may be stored and used label coronary
arteries.
[0372]
In some embodiments, the system can be configured to identify areas in
the artery where ischemia is likely to be found. In some embodiments, the
system can be
configured to identify the areas of plaque in which bad plaque exists. In some
embodiments, the system can be configured to identify bad plaque areas by
determining
whether the coloration and/or the gray scale level of the area within the
artery exceeds a
threshold level. In an example, the system can be configured to identify areas
of plaque
where the image of a plaque area is black or substantially black or dark gray.
In an example,
the system can be configured to identify areas of -good" plaque by the
designation of
whiteness or light grey in a plaque area within the artery.
[0373]
In some embodiments, the system is configured to identify portions of
an artery vessel where there is high risk for a cardiac event and/or draw an
outline following
the vessel wall or profiles of plaque build-up along the vessel wall. In some
embodiments,
the system is further configured to display this information to a user and/or
provide editing
tools for the user to change the identified portions or the outline
designations if the user
thinks that the Al algorithm incorrectly drew the outline designations. In
some
embodiments, the system comprises an editing tool referred to as "snap-to-
lumen," wherein
the user selects a region of interest by drawing a box around a particular
area of the vessel
and selecting the snap-to-lumen option and the system automatically redraws
the outline
designation to more closely track the boundaries of the vessel wall and/or the
plaque build-
up, wherein the system is using image processing techniques, such as but not
limited to
edge detection. in some embodiments, the Al algorithm does not process the
medical
image data with complete accuracy and therefore editing tools are necessary to
complete
the analysis of the medical image data. In some embodiments, the final user
editing of the
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medical image data allows for faster processing of the medical image data than
using solely
AT algorithms to process the medical image data.
[0374]
In some embodiments, the system is configured to replicate images from
higher resolution imaging. As an example, in CT, partial volume artifacts from
calcium are
a known artifact of CT that results in overestimation of the volume of calcium
and the
narrowing of an artery. By training and validating a CT artery appearance to
that of
intravascular ultrasound or optical coherence tomography or histopathology, in
some
embodiments, the CT artery appearance may be replicated to be similar to that
of IVUS or
OCT and, in this way, de-bloom the coronary calcium artifacts to improve the
accuracy of
the CT image.
[0375]
In some embodiments, the system is configured to provide a graphical
user interface for displaying a vessel from a beginning portion to an ending
portion and/or
the tapering of the vessel over the course of the vessel length. Many examples
of panels
that can be displayed in a graphical user interface are illustrated and
described in reference
to Figures 6A-9N. In some embodiments, portions of the user interface, panels,
buttons, or
information displayed on the user interface be arranged differently than what
is described
herein and illustrated in the Figures. For example, a user may have a
preference for
arranging different views of the arteries in different portions of the user
interface.
[0376]
In some embodiments, the graphical user interface is configured to
annotate the displayed vessel view with plaque build-up data obtained from the
AT
algorithm analysis in order to show the stenosis of the vessel or a stenosis
view In some
embodiments, the graphical user interface system is configured to annotate the
displayed
vessel view with colored markings or other markings to show areas of high risk
or further
analysis, areas of medium risk, and/or areas of low risk. For example, the
graphical user
interface system can be configured to annotate certain areas along the vessel
length in red
markings, or other graphical marking, to indicate that there is significant
bad fatty plaque
build-up and/or stenosis. In some embodiments, the annotated markings along
the vessel
length are based on one or more variable such as but not limited to stenosis,
biochemistry
tests, biomarker tests, AT algorithm analysis of the medical image data,
and/or the like. In
some embodiments, the graphical user interface system is configured to
annotate the vessel
view with an atherosclerosis view. In some embodiments, the graphical user
interface
system is configured to annotate the vessel view with an ischemia view. In
some
embodiments, the graphical user interface is configured to allow the user to
rotate the vessel
180 degrees or 360 degrees in order to display the vessel and the annotated
plaque build-
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up views from different angles. From this view, the user can manually
determine the stent
length and diameter for addressing the stenosis, and in some embodiments, the
system is
configured to analyze the medical image information to determine the
recommended stent
length and diameter, and display the proposed stent for implantation in the
graphical user
interface to illustrate to the user how the stent would address the stenosis
within the
identified area of the vessel. In some embodiments, the systems, methods, and
devices
disclosed herein can be applied to other areas of the body and/or other
vessels and/or organs
of a subject, whether the subject is human or other mammal.
Illustrative Example
[0377]
One of the main uses of such systems can be to determine the presence
of plaque in vessels, for example but not limited to coronary vessels. Plaque
type can be
visualized based on Hounsfield Unit density for enhanced readability for the
user.
Embodiments of the system also provide quantification of variables related to
stenosis and
plaque composition at both the vessel and lesion levels for the segmented
coronary artery.
[0378]
In some embodiments, the system is configured as a web-based software
application that is intended to be used by trained medical professionals as an
interactive
tool for viewing and analyzing cardiac CT data for determining the presence
and extent of
coronary plaques (i.e., atherosclerosis) and stenosis in patients who
underwent Coronary
Computed Tomography Angiography (CCTA) for evaluation of coronary artery
disease
(CAD), or suspected CAD. This system post processes CT images obtained using a
CT
scanner. The system is configured to generate a user interface that provides
tools and
functionality for the characterization, measurement, and visualization of
features of the
coronary arteries.
[0379]
Features of embodiments of the system can include, for example,
centerline and lumen/vessel extraction, plaque composition overlay, user
identification of
stenosis, vessel statistics calculated in real time, including vessel length,
lesion length,
vessel volume, lumen volume, plaque volume (non-calcified, calcified, low-
density¨non-
calcified plaque and total), maximum remodeling index, and area/diameter
stenosis (e.g., a
percentage), two dimensional (2D) visualization of multi-planar reformatted
vessel and
cross-sectional views, interactive three dimensional (3D) rendered coronary
artery tree,
visualization of a cartoon artery tree that corresponds to actual vessels that
appear in the
CT images, semi-automatic vessel segmentation that is user modifiable, and
user
identification of stents and Chronic Total Occlusion (CTO).
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[0380]
In an embodiment, the system uses 18 coronary segments within the
coronary vascular tree (e.g., in accordance with the guidelines of the Society
of
Cardiovascular Computed Tomography). The coronary segment labels include:
= pRCA - proximal right coronary artery
= mRCA - mid right coronary artery
= dRCA - distal right coronary artery
= R-PDA - right posterior descending artery
= LM - left main artery
= pLAD - proximal left anterior descending artery
= mLAD - mid left anterior descending artery
= dLAD - distal left anterior descending artery
= D1 - first diagonal
= D2 - second diagonal
= pCx - proximal left circumflex artery
= 0M1 - first obtuse marginal
= LCx - distal left circumflex
= 0M2 - second obtuse marginal
= L-PDA - left posterior descending artery
= R-PLB - right posterior lateral branch
= RI - ramus intermedius artery
= L-PLB - left posterior lateral branch
[0381]
Other embodiments can include more, or fewer, coronary segment
labels. The coronary segments present in an individual patient are dependent
on whether
they are right or left coronary dominant. Some segments are only present when
there is
right coronary dominance, and some only when there is a left coronary
dominance.
Therefore, in many, if not all instances, no single patient may have all 18
segments. The
system will account for most known variants.
[0382]
In one example of performance of the system, CT scans were processed
by the system, and the resulting data was compared to ground truth results
produced by
expert readers. Pearson Correlation Coefficients and Bland-Altman Agreements
between
the systems results and the expert reader results is shown in the table below:
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Output Pearson Correlation Bland-Altman
Agreement
Lumen Volume 0.91 96%
Vessel Volume 0.93 97%
Total Plaque Volume 0.85 95%
Calcified Plaque Volume 0.94 95%
Non-Calcified Plaque Volume 0.74 95%
Low-Density-Non-Calcified 0.53 97%
Plaque Volume
[0383]
Figures 6A - 9N illustrate an embodiment of the user interface of the
system, and show examples of panels, graphics, tools, representations of CT
images, and
characteristics, structure, and statistics related to coronary vessels found
in a set of CT
images. In various embodiments, the user interface is flexible and that it can
be configured
to show various arrangements of the panels, images, graphics representations
of CT images,
and characteristics, structure, and statistics. For example, based on an
analyst's preference.
The system has multiple menus and navigational tools to assist in visualizing
the coronary
arteries. Keyboard and mouse shortcuts can also be used to navigate through
the images
and information associated with a set of CT images for patient.
103841
Figure 6A illustrates an example of a user interface 600 that can be
generated and displayed on a CT image analysis system described herein, the
user interface
600 having multiple panels (views) that can show various corresponding views
of a
patient's arteries and information about the arteries. In an embodiment, the
user interface
600 shown in Figure 6A can be a starting point for analysis of the patient's
coronary
arteries, and is sometimes referred to herein as the "Study Page" (or the
Study Page 600).
In some embodiments, the Study Page can include a number of panels that can be
arranged
in different positions on the user interface 600, for example, based on the
preference the
analyst. In various instances of the user interface 600, certain panels of the
possible panels
that may be displayed can be selected to be displayed (e.g., based on a user
input).
[0385]
The example of the Study Page 600 shown in Figure 6A includes a first
panel 601 (also shown in the circled "2") including an artery tree 602
comprising a three-
dimensional (3D) representation of coronary vessels based on the CT images and
depicting
coronary vessels identified in the CT images, and further depicting respective
segment
labels. While processing the CT images, the system can determine the extent of
the
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coronary vessels are determined and the artery tree is generated. Structure
that is not part
of the coronary vessels (e.g., heart tissue and other tissue around the
coronary vessels) are
not included in the artery tree 602. Accordingly, the artery tree 602 in
Figure 6A does not
include any heart tissue between the branches (vessels) 603 of the artery tree
602 allowing
visualization of all portions of the artery tree 602 without them being
obscured by heart
tissue.
[0386]
This Study Page 600 example also includes a second panel 604 (also
shown in the circled "la-) illustrating at least a portion of the selected
coronary vessel in
at least one straightened multiplanar reformat (SMPR) vessel view. A SMPR view
is an
elevation view of a vessel at a certain rotational aspect. When multiple SMPR
views are
displayed in the second panel 604 each view can be at a different rotational
aspect. For
example, at any whole degree, or at a half degree, from 0 to 259.5 , where
360 is the
same view as 00. In this example, the second panel 604 includes four
straightened
multiplanar vessels 604a-d displayed in elevation views at a relative rotation
of 0', 22.5',
45 , and 67.5 , the rotation indicated that the upper portion of the
straightened multiplanar
vessel. In some embodiments, the rotation of each view can be selected by the
user, for
example, at the different relative rotation interval. The user interface
includes the rotation
tool 605 that is configured to receive an input from a user, and can be used
to adjust rotation
of a SMPR view (e.g., by one or more degrees). One or more graphics related to
the vessel
shown in the SMPR view can also be displayed. For example, a graphic
representing the
lumen of the vessel, a graphic representing the vessel wall, and/or a graphic
representing
plaque.
[0387]
This Study Page 600 example also includes the third panel 606 (also
indicated by the circled "lc"), which is configured to show a cross-sectional
view of a
vessel 606a generated based on a CT image in the set of CT images of the
patient. The
cross-sectional view corresponds to the vessel shown in the SMPR view. The
cross-
sectional view also corresponds to a location indicated by a user (e.g., with
a pointing
device) on a vessel in the SMPR view. The user interfaces configured such that
a selection
of a particular location along the coronary vessel in the second panel 604
displays the
associated CT image in a cross-sectional view in the third panel 606. In this
example, a
graphic 607 is displayed on the second panel 604 and the third panel 606
indicating the
extent of plaque in the vessel.
[0388]
This Study Page 600 example also includes a fourth panel 608 that
includes anatomical plane views of the selected coronary vessel. In this
embodiment, the
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Study Page 600 includes an axial plane view 608a (also indicated by the
circled "3a-), a
coronal plane view 608b (also indicated by the circled "3b"), and a sagittal
plane view 608c
(also indicated by the circled "3c"). The axial plane view is a transverse or
"top" view. The
coronal plane view is a front view. The sagittal plane view is a side view.
The user interface
is configured to display corresponding views of the selected coronary vessel.
For example,
views of the selected coronary vessel at a location on the coronary vessel
selected by the
user (e.g., on one of the SMPR views in the second panel 604.
[0389]
Figure 6B illustrates another example of the Study Page (user interface)
600 that can be generated and displayed on the system, the user interface 600
having
multiple panels that can show various corresponding views of a patient's
arteries. In this
example, the user interface 600 displays an 3D artery tree in the first panel
601, the cross-
sectional view in the third panel 606, and axial, coronal, and sagittal plane
views in the
fourth panel 608. Instead of the second panel 604 shown in Figure 6A, the user
interface
600 includes a fifth panel 609 showing curved multiplanar reformat (CMPR)
vessel views
of a selected coronary vessel. The fifth panel 609 can be configured to show
one or more
CMPR views. In this example, two CMPR views were generated and are displayed,
a first
CMPR view 609a at 00 and a second CMPR view 609b at 90 . The CMPR views can be
generated and displayed at various relative rotations, for example, from 00 to
259.5 . The
coronary vessel shown in the CMPR view corresponds to the selected vessel, and
corresponds to the vessel displayed in the other panels. When a location on
the vessel in
one panel is selected (e.g., the CMPR view), the views in the other panels
(e.g., the cross-
section, axial, sagittal, and coronal views) can be automatically updated to
also show the
vessel at that the selected location in the respective views, thus greatly
enhancing the
information presented to a user and increasing the efficiency of the analysis.
[0390]
Figures 6C, 6D, and 6E illustrate certain details of a multiplanar
reformat (MPR) vessel view in the second panel, and certain functionality
associated with
this view. After a user verifies the accuracy of the segmentation of the
coronary artery tree
in panel 602, they can proceed to interact with the MPR views where edits can
be made to
the individual vessel segments (e.g., the vessel walls, the lumen, etc.). In
the SMPR and
CMPR views, the vessel can be rotated in increments (e.g., 22.50) by using the
arrow icon
605, illustrated in Figures 6C and 6D. Alternatively, the vessel can be
rotated continuously
by 1 degree increments in 360 degrees by using the rotation command 610, as
illustrated in
Figure 6E. The vessels can also be rotated by pressing the COMMAND or CTRL
button
and left clicking + dragging the mouse on the user interface 600.
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103911
Figure 6F illustrates additional information of the three-dimensional
(3D) rendering of the coronary artery tree 602 on the first panel 601 that
allows a user to
view the vessels and modify the labels of a vessel. Figure 6G illustrates
shortcut commands
for the coronary artery tree 602, axial view 608a, sagittal view 608b, and
coronal view
608c. In panel 601 shown in Figure 6F, a user can rotate the artery tree as
well as zoom in
and out of the 3D rendering using commands selected in the user interface
illustrated in
Figure 6G. Clicking on a vessel will turn it yellow which indicates that is
the vessel that is
currently being reviewed. In this view, users can rename or delete a vessel by
right-clicking
on the vessel name which opens panel 611, which is configured to receive an
input from a
user to rename the vessel. Panel 601 also includes a control that can be
activated to turn the
displayed labels "on" or -off." Figure 6H further illustrates panel 608 of the
user interface
for viewing DICOM images in three anatomical planes: axial, coronal, and
sagittal. Figure
61 illustrates panel 606 showing a cross-sectional view of a vessel. The
scroll, zoom in/out,
and pan commands can also be used on these views.
[0392]
Figure 6J and 6K illustrate certain aspects of the toolbar 612 and menu
navigation functionality of the user interface 600. Figure 6J illustrates a
toolbar of the user
interface for navigating the vessels. The toolbar 612 includes a button 612a,
612b etc. for
each of the vessels displayed on the screen. The user interface 600 is
configured to display
the buttons 612a-n to indicate various information to the user. In an example,
when a vessel
is selected, the corresponding button is highlighted (e.g., displayed in
yellow), for example,
button 612c. In another example, a button being dark gray with white lettering
indicates
that a vessel is available for analysis. In an example, a button 612d that is
shaded black
means a vessel could not be analyzed by the software because they are either
not
anatomically present or there are too many artifacts. A button 612e that is
displayed as gray
with check mark indicates that the vessel has been reviewed.
[0393]
Figure 6K illustrates a view of the user interface 600 with an expanded
menu to view all the series (of images) that are available for review and
analysis. If the
system has provided more than one of the same vessel segment from different
series of
images for analysis, the user interface is configured to receive a user input
to selected the
desired series for analysis. In an example, an input can be received
indicating a series for
review by a selection on one of the radio buttons 613 from the series of
interest. The radio
buttons will change from gray to purple when it is selected for review. In an
embodiment,
the software, by default, selects the two series of highest diagnostic quality
for analysis
however, all series are available for review. The user can use clinical
judgment to determine
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if the series selected by the system is of diagnostic quality that is required
for the analysis,
and should select a different series for analysis if desired. The series
selected by the system
is intended to improve workflow by prioritizing diagnostic quality images. The
system is
not intended to replace the user's review of all series and selection of a
diagnostic quality
image within a study. Users can send any series illustrated in Figure 6K for
the system to
suggest vessel segmentations by hovering the mouse over the series and select
an
"Analyze- button 614 as illustrated in Figure 6L.
[0394]
Figure 6M illustrates a panel that can be displayed on the user interface
600 to add a new vessel on the image, according to one embodiment. To add a
new vessel
on the image, the user interface 600 can receive a user input via a "+Add
Vessel" button
on the toolbar 612. The user interface will display a "create Mode" 615 button
appear in
the fourth panel 608 on the axial, coronal and sagittal view. Then the vessel
can be added
on the image by scrolling and clicking the left mouse button to create
multiple dots (e.g.,
green dots). As the new vessel is being added, it will preview as a new vessel
in the MPR,
cross-section, and 3D artery tree view. The user interface is configured to
receive a "Done"
command to indicate adding the vessel has been completed. Then, to segment the
vessels
utilizing the system's semi-automatic segmentation tool, click "Analyze" on
the tool bar
and the user interface displays suggested segmentation for review and
modification. The
name of the vessel can be chosen by selecting "New- in the 3D artery tree view
in the first
panel 601, which activates the name panel 611 and the name of the vessel can
be selected
from panel 611, which then stores the new vessel and its name. In an
embodiment, if the
software is unable to identify the vessel which has been added by the user, it
will return
straight vessel lines connecting the user-added green dots, and the user can
adjust the
centerline. The pop-up menu 611 of the user interface allows new vessels to be
identified
and named according to a standard format quickly and consistently.
[0395]
Figure 7A illustrates an example of an editing toolbar 714 that includes
editing tools which allow users to modify and improve the accuracy of the
findings
resulting from processing CT scans with a machine learning algorithm, and then
processing
the CT scans, and information generated by the machine learning algorithm, by
an analyst.
In some embodiments, the user interface includes editing tools that can be
used to modify
and improve the accuracy of the findings. In some embodiments, the editing
tools are
located on the left-hand side of the user interface, as shown in Figure 7A.
The following is
a listing and description of the available editing tools. Hovering over each
button (icon)
will display the name of each tool. These tools can be activated and
deactivated by clicking
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on it. If the color of the tool is gray, it is deactivated. If the software
has identified any of
these characteristics in the vessel, the annotations will already be on the
image when the
tool is activated. The editing tools in the toolbar can include one or more of
the following
tools: Lumen Wall 701, Snap to Vessel Wall 702, Vessel Wall 703, Snap to Lumen
Wall
704, Segments 705, Stenosis 706, Plaque Overlay 707, Centerline 708, Chronic
Total
Occlusion (CTO) 709, Stent 710, Exclude By 711, Tracker 712, and Distance 713.
The user
interface 600 is configured to activate each of these tools by receiving a
user selection on
the respective toll icon (shown in the table below and in Figure 7A) and are
configured to
provide functionality described in the Editing Tools Description Table below:
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WO 2023/023286
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coronal, sagittal, and the 3D artery tree views. To activate, the tracker icon
is selected on
the editing toolbar. When the Tracker tool 712 is activated, the user
interface generates and
displays a line 616 (e.g., a red line) on the SMPR or CMPR view. The system
generates on
the user interface a corresponding (red) disc 617 which is displayed on the 3D
artery tree
in the first panel 601 in a corresponding location as the line 616. The system
generates on
the user interface a corresponding (red) dot which his displayed on the axial,
sagittal and
coronal views in the fourth panel 608 in a corresponding location as the line
616. The line
616, disc 617, and dots 618 are location indicators all referencing the same
location in the
different views, such that scrolling any of the trackers up and down will also
result in the
same movement of the location indicator in other views. Also, the user
interface 600
displays the cross-sectional image in panel 606 corresponding to the location
indicated by
the location indicators.
[0397]
Figures 7D and 7E illustrate certain functionality of the vessel and
lumen wall tools, which are used to modify the lumen and vessel wall contours.
The Lumen
Wall tool 701 and the Vessel Wall tool 703 are configured to modify the lumen
and vessel
walls (also referred to herein as contours, boundaries, or features) that were
previously
determined for a vessel (e.g., determined by processing the CT images using a
machine
learning process. These tool are used by the system for determining
measurements that are
output or displayed. By interacting with the contours generated by the system
with these
tools, a user can refine the accuracy of the location of the contours, and any
measurements
that are derived from those contours. These tools can be used in the SMPR and
cross-
section view. The tools are activated by selecting the vessel and lumen icons
701, 703 on
the editing toolbar. The vessel wall 619 will be displayed in the MPR view and
the cross-
section view in a graphical "trace" overlay in a color (e.g., yellow). The
lumen wall 629
will be displayed in a graphical -trace" overly in a different color (e.g.,
purple). In an
embodiment, the user interface is configured to refine the contours through
interactions
with a user. For example, to refine the contours, the user can hover above the
contour with
a pointing device (e.g., mouse, stylus, finger) so it highlights the contour,
click on the
contour for the desired vessel or lumen wall and drag the displayed trace to a
different
location setting a new boundary. The user interface 600 is configured to
automatically save
any changes to these tracings. The system re-calculates any measurements
derived from the
changes contours in real time, or near real time. Also, the changes made in
one panel on
one view are displayed correspondingly in the other views / panels.
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[0398]
Figure 7F illustrates the lumen wall button 701 and the snap to vessel
wall button 702 (left) and the vessel wall button 703 and the snap to lumen
wall button 704
(right) of the user interface 600 which can be used to activate the Lumen
Wall/Snap to
Vessel tools 701, 702, and the Vessel Wall/Snap to Lumen Wall 703, 704 tools,
respectively. The user interface provides these tools to modify lumen and
vessel wall
contours that were previously determined. The Snap to Vessel/Lumen Wall tools
are used
to easily and quickly close the gap between lumen and vessel wall contours,
that is, move
a trace of the lumen contour and a trace of the vessel contour to be the same,
or substantially
the same, saving interactive editing time. The user interface 600 is
configured to activate
these tools when a user hovers of the tools with a pointing device, which
reveals the snap
to buttons. For example, hovering over the Lumen Wall button 701 reveals the
Snap to
Vessel button 702 to the right-side of the Lumen wall button, and hovering
over the Vessel
Wall button 703 reveals the Snap to Lumen Wall button 704 beside the Vessel
Wall button
703. A button is selected to activate the desired tool. In reference to Figure
7G, a pointing
device can be used to click at a first point 620 and drag along the intended
part of the vessel
to edit to a second point 621, and an area 622 will appear indicating where
the tool will run.
Once the end of the desired area 622 is drawn, releasing the selection will
snap the lumen
and vessel walls together.
[0399]
Figure 7H illustrates an example of the second panel 602 that can be
displayed while using the Segment tool 705 which allows for marking the
boundaries
between individual coronary segments on the MPR. The user interface 600 is
configured
such that when the Segment tool 705 is selected, lines (e.g., lines 623, 624)
appear on the
vessel image in the second panel 602 on the vessels in the SMPR view. The
lines indicate
segment boundaries that were determined by the system. The names are displayed
in icons
625, 626 adjacent to the respective line 623, 624. To edit the name of the
segment, click on
an icon 625, 626 and label appropriately using the name panel 611, illustrated
in Figure 71.
A segment can also be deleted, for example, by selecting a trashcan icon. The
lines 623,
624 can be moved up and down to define the segment of interest. If a segment
is missing,
the user can add a new segment using a segment addition button, and labeled
using the
labeling feature in the segment labeling pop-up menu 611.
[0400]
Figures 7J ¨ 7M illustrate an example of using the stenosis tool 706 on
the user interface 600. For example, Figure 7L illustrates a stenosis button
which can be
used to drop stenosis markers based on the user edited lumen and vessel wall
contours.
Figure 7M illustrates the stenosis markers on segments on a curved multiplanar
vessel
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(CMPR) view. The second panel 604 can be displayed while using the stenosis
tool 706
which allows a user to indicate markers to mark areas of stenosis on a vessel.
In an
embodiment, the stenosis tool contains a set of five markers that are used to
mark areas of
stenosis on the vessel. These markers are defined as:
= RI: Nearest proximal normal slice to the stenosis/lesion
= P: Most proximal abnormal slice of the stenosis/lesion
= 0: Slice with the maximum occlusion
= D: Most distal abnormal slice of the stenosis/lesion
= R2: Nearest distal normal slice to the stenosis/lesion
104011
In an embodiment, there are two ways to add stenosis markers to the
multiplanar view (straightened and curved). After selecting the stenosis tool
706, a stenosis
can be added by activating the stenosis button shown in Figure 7K or Figure
7L: to drop 5
evenly spaced stenosis markers (i) click on the Stenosis "+" button (Figure
7K); (ii) a series
of 5 evenly spaced yellow lines will appear on the vessel; the user must edit
these markers
to the applicable position; (iii) move all 5 markers at the same time by
clicking inside the
highlighted area encompassed by the markers and dragging them up/down; (iv)
move the
individual markers by clicking on the individual yellow lines or tags and move
up and
down; (v) to delete a stenosis, click on the red trashcan icon. To drop
stenosis markers
based on the user-edited lumen and vessel wall contours, click on the stenosis
C button
(see Figure 7L). A series of 5 yellow lines will appear on the vessel. The
positions are based
on the user-edited contours. The user interface 600 provides functionality for
a user to edit
the stenosis markers, e.g., can move the stenosis markers Figure 7J
illustrates the stenosis
markers R1, P. 0, D, and R2 placed on vessels in a SMPR view. Figure 7M
illustrates the
markers RI, P. 0, D, and R2 placed on vessels in a CMPR view.
104021
Figure 7N illustrates an example of a panel that can be displayed while
using the Plaque Overlay tool 707 of the user interface. In an embodiment and
in reference
to Figure 7N, "Plaque" is categorized as: low-density-non-calcified plaque (LD-
NCP) 701,
non-calcified plaque (NCP) 632, or calcified plaque (CP) 633. Selecting the
Plaque Overlay
tool 707 on the editing toolbar activates the tool. When activated, the Plaque
Overlay tool
707 overlays different colors on vessels in the SMPR view in the second panel
604, and in
the cross-section the SMPR, and cross-section view in the third panel 606 (see
for example,
Figure 7R) with areas of plaque based on Hounsfield Unit (HU) density. In
addition, a
legend opens in the cross-section view corresponding to plaque type to plaque
overlay color
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as illustrated in Figures 70 and 7Q. Users can select different HU ranges for
the three
different types of plaque by clicking on the "Edit Thresholds" button located
in the top right
comer of the cross-section view as illustrated in Figure 7P. In one
embodiment, plaque
thresholds default to the values shown in the table below:
Plaque Type Hounsfield Unit (HU)
LD-NCO -189 to 30
NCP -189 to 350
CP 350 to 2500
[0403]
The default values can be revised, if desired, for example, using the
Plaque Threshold interface shown in Figure 7Q. Although default values are
provided,
users can select different plaque thresholds based on their clinical judgment.
Users can use
the cross-section view of the third panel 606, illustrated in Figure 7R, to
further examine
areas of interest. Users can also view the selected plaque thresholds in a
vessel statistics
panel of the user interface 600, illustrated in Figure 7S.
[0404]
The Centerline tool 708 allows users to adjust the center of the lumen.
Changing a center point (of the centerline) may change the lumen and vessel
wall and the
plaque quantification, if present. The Centerline tool 708 is activated by
selecting it on the
user interface 600. A line 635 (e.g., a yellow line) will appear on the CMPR
view 609 and
a point 634 (e.g., a yellow point) will appear in the cross-section view on
the third panel
606. The centerline can be adjusted as necessary by clicking and dragging the
line/point.
Any changes made in the CMPR view will be reflected in the cross-section view,
and vice-
versa. The user interface 600 provides for several ways to extend the
centerline of an
existing vessel. For example, a user can extend the centerline by: (1) right-
clicking on the
dot 634 delineated vessel on the axial, coronal, or sagittal view (see Figure
7U); (2) select
-Extend from Start" or -Extend from End" (see Figure 7U), the view will jump
to the start
or end of the vessel; (3) add (green) dots to extend the vessel (see Figure
7V); (4) when
finished, select the (blue) check mark button, to cancel the extension, select
the (red) "x"
button (see for example, Figure 7V). The user interface then extends the
vessel according
to the changes made by the user. A user can then manually edit the lumen and
vessel walls
on the SMPR or cross-section views (see for example, Figure 7W). If the user
interface is
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unable to identify the vessel section which has been added by the user, it
will return straight
vessel lines connecting the user-added dots. The user can then adjust the
centerline.
[0405]
The user interface 600 also provides a Chronic Total Occlusion (CTO)
tool 709 to identify portions of an artery with a chronic total occlusion
(CTO), that is, a
portion of artery with 100% stenosis and no detectable blood flow. Since it is
likely to
contain a large amount of thrombus, the plaque within the CTO is not included
in overall
plaque quantification. To activate, click on the CTO tool 709 on the editing
toolbar 612.
To add a CTO, click on the CTO "+" button on the user interface. Two lines
(markers) 636,
637 will appear on the MPR view in the second panel 604, as illustrated in
Figure 7X
indicating a portion of the vessel of the CTO. The markers 636, 637 can be
moved to adjust
the extent of the CTO. If more than one CTO is present, additional CTO's can
be added by
again activating the CTO "+" button on the user interface. A CTO can also be
deleted, if
necessary. The location of the CTO is stored. In addition, portions of the
vessel that are
within the designated CTO are not included in the overall plaque calculation,
and the plaque
quantification determination is re-calculated as necessary after CTO's are
identified.
[0406]
The user interface 600 also provides a Stent tool 710 to indicate where
in vessel a stent exists. The Stent tool is activated by a user selection of
the Stent tool 710
on the toolbar 612. To add a stent, click on the Stent -+" button provided on
the user
interface. Two lines 638, 639 (e.g., purple lines) will appear on of the MPR
view as
illustrated in Figure 7Y, and the lines 638, 639 can be moved to indicate the
extend of the
stent by clicking on the individual lines 638, 639 and moving them up and down
along the
vessel to the ends of the stent. Overlapping with the stent (or the
CTO/Exclusion/Stenosis)
markers is not permitted by the user interface 600. A stent can also be
deleted.
104071
The user interface 600 also provides an Exclude tool 711 that is
configured to indicate a portion of a vessel to exclude from the analysis due
to blurring
caused by motion, contrast, misalignment, or other reasons. Excluding poor
quality images
will improve the overall quality of the results of the analysis for the non-
excluded portions
of the vessels. To exclude the top or bottom portion of a vessel, activate the
segment tool
705 and the exclude tool 711 in the editing toolbar 612. Figure 7Z illustrates
the use of the
exclusion tool to exclude a portion from the top of the vessel. Figure 7AA
illustrates the
use of the exclusion tool to exclude a bottom portion of the vessel. A first
segment marker
acts as the exclusion marker for the top portion of the vessel. The area
enclosed by
exclusion markers is excluded from all vessel statistic calculations. An area
can be excluded
by dragging the top segment marker to the bottom of the desired area of
exclusion. The
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excluded area will be highlighted. Or the "End- marker can be dragged to the
top of the
desired area of exclusion. The excluded area will be highlighted, and a user
can enter the
reason for an exclusion in the user interface (see Figure 7AC). To add a new
exclusion to
the center of the vessel, activate the exclude tool 711 on the editing toolbar
612. Click on
the Exclusion "+" button. A pop-up window on the user interface will appear
for the reason
of the exclusion (Figure 7AC), and the reason can be entered and it is stored
in reference
to the indicated excluded area. Two markers 640, 641 will appear on the MPR as
shown in
Figure 7AB. Move both markers at the same time by clicking inside the
highlighted area.
The user can move the individual markers by clicking and dragging the lines
640, 641. The
user interface 600 tracks the locations of the exclusion marker lines 640, 641
(and
previously defined features) and prohibits overlap of the area defined by the
exclusion lines
640, 641 with any previously indicated portions of the vessel having a CTO,
stent or
stenosis. The user interface 600 also is configured to delete a designated
exclusion.
[0408]
Now referring to Figures 7AD-7AG, the user interface 600 also provides
a Distance tool 713, which is used to measure the distance between two points
on an image.
It is a drag and drop ruler that captures precise measurements. The Distance
tool works in
the MPR, cross-section, axial, corona', and sagittal views. To activate, click
on the distance
tool 713 on the editing toolbar 612. Then, click and drag between the desired
two points.
A line 642 and measurement 643 will appear on the image displayed on the user
interface
600. Delete the measurement by right-clicking on the distance line 642 or
measurement
643 and selecting "Remove the Distance" button 644 on the user interface 600
(see Figure
7AF). Figure 7AD illustrates an example of measuring a distance of a
straightened
multiplanar vessel (SMPR). Figure 7AE illustrates an example of measuring the
distance
642 of a curved multiplanar vessel (CMPR). Figure 7AF illustrates an example
of
measuring a distance 642 of a cross-section of the vessel. Figure 7AG
illustrates an example
of measuring the distance 642 on an Axial View of a patient's anatomy.
[0409]
An example of a vessel statistics panel of the user interface 600 is
described in reference to Figures 7AH - 7AK. Figure 7AH illustrates a -vessel
statistics"
portion 645 of the user interface 600 (e.g., a button) of a panel which can be
selected to
display the vessel statistics panel 646 (or "tab"), illustrated in Figure 7AI.
Figure 7AJ
illustrates certain functionality on the vessel statistics tab that allows a
user to click through
the details of multiple lesions. Figure 7AK further illustrates the vessel
panel which the
user can use to toggle between vessels. For example, users can hide the panel
by clicking
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on the "X- on the top right hand side of the panel, illustrated in Figure 7A1.
Statistics are
shown at the per-vessel and per-lesion (if present) level, as indicated in
Figure 7AJ.
[0410]
If more than one lesion is marked by the user, the user can click through
each lesion's details. To view the statistics for each vessel, the users can
toggle between
vessels on the vessel panel illustrated in Figure 7AK.
[0411]
General information pertaining to the length and volume are presented
for the vessel and lesion (if present) in the vessel statistics panel 646,
along with the plaque
and stenosis information on a per-vessel and per-lesion level. Users may
exclude artifacts
from the image they do not want to be considered in the calculations by using
the exclusion
tool. The following tables indicate certain statistics that are available for
vessels, lesions,
plaque, and stenosis.
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VESSEL
Term Definition
Vessel Length (mm) Length of a linear coronary vessel.
Total Vessel Volume (mm3) The volume of consecutive slices of
vessel contours.
Total Lumen Volume (mm3) The volume of consecutive slices of lumen contours.
LESION
Term Definition
Lesion Length (mm) Linear distance from the start of a
coronary
lesion to the end of a coronary lesion.
Vessel Volume (mm3) The volume of consecutive slices of
vessel
contours.
Lumen Volume (mm3) The volume of consecutive slices of
lumen
contours.
PLAQUE
Term Definition
Total Calcified Plaque Volume (mm3) Calcified plaque is defined
as plaque in
between the lumen and vessel wall with an
attenuation of greater than 350 HU, or as
defined by the user, and is reported in
absolute measures by plaque volume.
Calcified plaques are identified in each
coronary artery >1.5 mm in mean vessel
diameter.
Total Non-Calcified Plaque Volume Non-calcified plaque is defined as plaque
(mm3) in between the lumen and
vessel wall with
an attenuation of less than or equal to 350,
or as defined by the user, HU and is
reported in absolute measures by plaque
volume. The total non-calcified plaque
volume is the sum total of all non-calcified
plaques identified in each coronary artery
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Term Definition
>1.5 mm in mean vessel diameter. Non-
calci tied plaque data reported is further
broken down into low-density plaque,
based on HU density thresholds.
Low-Density Non-Calcified Plaque Low-Density--Non-Calcified Plaque is
Volume (mm3) defined as plaque in between
the lumen and
vessel wall with an attenuation of less than
or equal to 30 HU or as defined by the user
and is reported in absolute measures by
plaque volume.
Total Plaque Volume (mm3) Plaque volume is defined as
plaque in
between the lumen and vessel wall reported
in absolute measures. The total plaque
volume is the sum total of all plaque
identified in each coronary artery >1.5 mm
in mean vessel diameter or wherever the
user places the "End" marker.
STENOS1S
Term Definition
Remodeling Index Remodeling Index is defined
as the mean
vessel diameter at a denoted slice divided
by the mean vessel diameter at a reference
slice.
Greatest Diameter Stenosis (%) The deviation of the mean
lumen diameter
at the denoted slice from a reference slice,
expressed in percentage.
Greatest Area Stenosis (/o) The deviation of the lumen
area at the
denoted slice to a reference area, expressed
in percentage
[0412]
A quantitative variable that is used in the system and displayed on
various portions of the user interface 600, for example, in reference to low-
density non-
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calcified plaque, non-calcified plaque, and calcified plaque, is the
Hounsfield unit (HU).
As is known, a Hounsfield Unit scale is a quantitative scale for describing
radiation, and is
frequently used in reference to CT scans as a way to characterize radiation
attenuation and
thus making it easier to define what a given finding may represent. A
Hounsfield Unit
measurement is presented in reference to a quantitative scale. Examples of
Hounsfield Unit
measurements of certain materials are shown in the following table:
Material HU
Air -1000
Fat -50
Distilled Water 0
Soft Tissue +40
Blood +40 to 80
Calcified Plaques 350-1000+
Bone +1000
[0413]
In an embodiment, information that the system determines relating to
stenosis, atherosclerosis, and CAD-RADS details are included on panel 800 of
the user
interface 600, as illustrated in Figure 8A. By default, the CAD-RADS score may
be
unselected and requires the user to manually select the score on the CAD-RADS
page.
Hovering over the -#" icons causes the user interface 600 to provide more
information
about the selected output. To view more details about the stenosis,
atherosclerosis, and
CAD-RADS outputs, click the "View Details" button in the upper right of panel
800 - this
will navigate to the applicable details page. In an embodiment, in the center
of a centerpiece
page view of the user interface 600 there is a non-patient specific rendition
of a coronary
artery tree 805 (a "cartoon artery tree- 805) broken into segments 805a-805r
based on the
SCCT coronary segmentation, as illustrated in panel 802 in Figure 8C. All
analyzed vessels
are displayed in color according to the legend 806 based on the highest
diameter stenosis
within that vessel. Greyed out segments/vessels in the cartoon artery tree
805, for example,
segment 805q and 805r, were not anatomically available or not analyzed in the
system (all
segments may not exist in all patients). Per-territory and per-segment
information can be
viewed by clicking the territory above the tree (RCA, LM+LAD, etc.) using, for
example,
the user interface 600 selection buttons in panel 801, as illustrated in
Figure 8B and 8C. Or
my selecting a segment 805a-805r within the cartoon coronary tree 805.
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[0414]
Stenosis and atherosclerosis data displayed on the user interface in panel
807 will update accordingly as various segments are selected, as illustrated
in Figure 8D.
Figure 8E illustrates an example of a portion of the per-territory summary
panel 807 of the
user interface. Figure 8F also illustrates an example of portion of panel 807
showing the
SMPR of a selected vessel and its associated statistics along the vessel at
indicated locations
(e.g., at locations indicated by a pointing device as it is moved along the
SMPR
visualization). That is, the user interface 600 is configured to provide
plaque details and
stenosis details in an SMPR visualization in panel 809 and a pop-up panel 810
that displays
information as the user interface receives location information long the
displayed vessel
from the user, e.g., via a pointing device. The presence of a chronic total
occlusion (CT)
and/or a stent are indicated at the vessel segment level. For example, Figure
8G illustrates
the presence of a stent in the D1 segment. Figure 8H indicates the presence of
a CTO in the
mRCA segment. Coronary dominance and any anomalies can be displayed below the
coronary artery tree as illustrated in Figure 81. The anomalies that were
selected in the
analysis can be displayed, for example, by "hovering- with a pointing device
over the
"details" button. If plaque thresholds were changed in the analysis, an alert
can be displayed
on the user interface, or on a generated report, that indicates the plaque
thresholds were
changed. When anomalies are present, the coronary vessel segment 805
associated with
each anomaly will appear detached from the aorta as illustrated in Figure 8J.
In an
embodiment, a textual summary of the analysis can also be displayed below the
coronary
tree, for example, as illustrated in the panel 811 in Figure 8K.
[0415]
Figure 9A illustrates an atherosclerosis panel 900 that can be displayed
on the user interface, which displays a summary of atherosclerosis information
based on
the analysis. Figure 9B illustrates the vessel selection panel which can be
used to select a
vessel such that the summary of atherosclerosis information is displayed on a
per segment
basis. The top section of the atherosclerosis panel 900 contains per-patient
data, as
illustrated in Figure 9A. When a user "hovers" over the "Segments with
Calcified Plaque"
on panel 901, or hovers over the "Segments with Non-Calcified Plaque- in panel
902, the
segment names with the applicable plaque are displayed. Below the patient
specific data,
users may access per-vessel and per-segment atherosclerosis data by clicking
on one of the
vessel buttons, illustrated in Figure 9B.
[0416]
Figure 9C illustrates a panel 903, that can be generated and displayed on
the user interface, which shows atherosclerosis information determined by the
system on a
per segment basis. The presence of positive remodeling, the highest remodeling
index, and
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the presence of Low-Density¨Non-Calcified Plaque are reported for each segment
in the
panel 903 illustrated in Figure 9C. For example, plaque data can be displayed
below on a
per-segment basis, and plaque composition volumes can be displayed on a per-
segment in
the panel 903 illustrated in Figure 9C.
104171 Figure 9D illustrates a panel 904 that can be
displayed on the user
interface that contains stenosis per patient data. The top section of the
stenosis panel 904
contains per-patient data Further details about each count can be displayed by
hovering
with a pointing device over the numbers, as illustrated in Figure 9E. Vessels
included in
each territory are shown in the table below:
Vessel Territory Segment N ame
LM (Left Main Artery) LM
LAD (Left Anterior Descending) pLAD
mLAD
dLAD
D1
D2
RI
LCN (Left Circumflex Artery) pCx
LCx
0M1
0M2
L-PLB
L-PDA
RCA (Right Coronary Artery) pRCA
mRCA
dRCA
R-PLB
R-PDA
104181 In an embodiment, a percentage Diameter Stenosis
bar graph 906 can be
generated and displayed in a panel 905 of the user interface, as illustrated
in Figure 9F. The
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percentage Diameter Stenosis bar graph 906 displays the greatest diameter
stenosis in each
segment. If a CTO has been marked on the segment, it will display as a 100%
diameter
stenosis. If more than one stenosis has been marked on a segment, the highest
value outputs
are displayed by default and the user can click into each stenosis bar to view
stenosis details
and interrogate smaller stenosis (if present) within that segment. The user
can also scroll
through each cross-section by dragging the grey button in the center of a SMPR
view of
the vessel, and view the lumen diameter and % diameter stenosis at each cross-
section at
any selected location, as illustrated in Figure 9G.
[0419]
Figure 9H illustrates a panel showing categories of the one or more
stenosis marked on the SMPR based on the analysis. Color can be used to
enhance the
displayed information. In an example, stenosis in the LM >= 50% diameter
stenosis are
marked in red. As illustrated in a panel 907 of the user interface in Figure
91, for each
segment's greatest percentage diameter stenosis the minimum luminal diameter
and lumen
diameter at the reference can be displayed when a pointing device is -hovered"
above the
graphical vessel cross-section representation, as illustrated in Figure 9J. If
a segment was
not analyzed or is not anatomically present, the segment will be greyed out
and will display
"Not Analyzed". If a segment was analyzed but did not have any stenosis
marked, the value
will display -N/A".
[0420]
Figure 9K illustrates a panel 908 of the user interface that indicates
CADS-RADS score selection. The CAD-RADS panel displays the definitions of CAD-
RADS as defined by "Coronary Artery Disease - Reporting and Data System (CAD-
RADS)
An Expert Consensus Document of SCCT, ACR and NASCI: Endorsed by the ACC". The
user is in full control of selecting the CAD-RADS score. In an embodiment, no
score will
be suggested by the system. In another embodiment, a CAD-RADS score can be
suggested.
Once a CAD-RADS score is selected on this page, the score will display in both
certain
user interface panels and full text report pages. Once a CAD-RADS score is
selected, the
user has the option of selecting modifiers and the presentation of symptoms.
Once a
presentation is selected, the interpretation, further cardiac investigation
and management
guidelines can be displayed to the user on the user interface, for example, as
illustrated in
the panel 909 illustrated in Figure 9L. These guidelines reproduce the
guidelines found in
-Coronary Artery Disease - Reporting and Data System (CAD-RADS) An Expert
Consensus Document of SCCT, ACR and NASC1: Endorsed by the ACC."
[0421]
Figures 9M and 9N illustrate tables that can be generated and displayed
on a panel of the user interface, and/or included in a report. Figure 9M
illustrates
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quantitative stenosis and vessel outputs. Figure 9N illustrates quantitative
plaque outputs.
In these quantitative tables, a user can view quantitative per-segment
stenosis and
atherosclerosis outputs from the system analysis. The quantitative stenosis
and vessel
outputs table (Figure 9M) includes information for the evaluated arteries and
segments.
Totals are given for each vessel territory. Information can include, for
example, length,
vessel volume, lumen volume, total plaque volume, maximum diameter stenosis,
maximum
area stenosis, and highest remodeling index. The quantitative plaque outputs
table (Figure
9N) includes information for the evaluated arteries and segments. Information
can include,
for example, total plaque volume, total calcified plaque volume, non-calcified
plaque
volume, low-density non-calcified plaque volume, and total non-calcified
plaque volume.
The user is also able to download a PDF or CSV file of the quantitative
outputs is a full
text Report. The full text Report presents a textual summary of the
atherosclerosis, stenosis,
and CAD-RADS measures. The user can edit the report, as desired. Once the user
chooses
to edit the report, the report will not update the CAD-RADS selection
automatically.
[0422]
Figure 10 is a flowchart illustrating a process 1000 for analyzing and
displaying CT images and corresponding information. At block 1005, the process
1000
stores computer-executable instructions, a set of CT images of a patient's
coronary vessels,
vessel labels, and artery information associated with the set of CT images
including
information of stenosis, plaque, and locations of segments of the coronary
vessels. All of
the steps of the process can be performed by embodiments of the system
described herein,
for example, on embodiments of the systems described in Figure 13. For
example, by one
or more computer hardware processors in communication with the one or more non-
transitory computer storage mediums, executing the computer-executable
instructions
stored on one or more non-transitory computer storage mediums. In various
embodiments,
the user interface can include one or more portions, or panels, that are
configured to display
one or more of images, in various views (e.g., SMPR, CMPR, cross-sectional,
axial,
sagittal, coronal, etc.) related to the CT images of a patient's coronary
arteries, a graphical
representation of coronary arteries, features (e.g., a vessel wall, the lumen,
the centerline,
the stenosis, plaque, etc.) that have been extracted or revised by machine
learning algorithm
or by an analyst, and information relating to the CT images that has been
determined by
the system, by an analyst, or by an analyst interacting with the system (e.g.,
measurements
of features in the CT images. In various embodiments, panels of the user
interface can be
arranged differently than what is described herein and what is illustrated in
the
corresponding figures. A user can make an input to the user interface using a
pointing
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device or a user's finger on a touchscreen. In an embodiment, the user
interface can receive
input by determining the selection of a button/icon/portion of the user
interface. In an
embodiment, the user interface can receive an input in a defined field of the
user interface.
[0423]
At block 1010, the process 1000 can generate and display in a user
interface a first panel including an artery tree comprising a three-
dimensional (3D)
representation of coronary vessels based on the CT images and depicting
coronary vessels
identified in the CT images, and depicting segment labels, the artery tree not
including heart
tissue between branches of the artery tree. An example of such an artery tree
602 is shown
in panel 601 in Figure 6A. In various embodiments, panel 601 can be positioned
in locations
of the user interface 600 other than what is shown in Figure 6A.
[0424]
At block 1015, the process 1000 can receive a first input indicating a
selection of a coronary vessel in the artery tree in the first panel. For
example, the first input
can be received by the user interface 600 of a vessel in the artery tree 602
in panel 601. At
block 1020, in response to the first input, the process 1000 can generate and
display on the
user interface a second panel illustrating at least a portion of the selected
coronary vessel
in at least one straightened multiplanar vessel (SMPR) view. In an example,
the SMPR
view is displayed in panel 604 of Figure 6A.
[0425]
At block 1025, the process 1000 can generate and display on the user
interface a third panel showing a cross-sectional view of the selected
coronary vessel, the
cross-sectional view generated using one of the set of CT images of the
selected coronary
vessel. Locations along the at least one SMPR view are each associated with
one of the
CT images in the set of CT images such that a selection of a particular
location along the
coronary vessel in the at least one SMPR view displays the associated CT image
in the
cross-sectional view in the third panel. In an example, the cross-sectional
view can be
displayed in panel 606 as illustrated in Figure 6A. At block 1035, the process
1000 can
receive a second input on the user interface indicating a first location along
the selected
coronary artery in the at least one SMPR view. In an example, user may use a
pointing
device to select a different portion of the vessel shown in the SMPR view in
panel 604. At
block 1030, the process 1000, in response to the second input, displays the
associated CT
scan associated in the cross-sectional view in the third panel, panel 606.
That is, the cross-
sectional view that correspond to the first input is replaced by the cross-
sectional view that
corresponds to the second input on the SMPR view.
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Normalization Device
[0426]
In some instances, medical images processed and/or analyzed as
described throughout this application can be normalized using a normalization
device. As
will be described in more detail in this section, the normalization device may
comprise a
device including a plurality of samples of known substances that can be placed
in the
medical image field of view so as to provide images of the known substances,
which can
serve as the basis for normalizing the medical images. In some instances, the
normalization
device allows for direct within image comparisons between patient tissue
and/or other
substances (e.g., plaque) within the image and known substances within the
normalization
device.
[0427]
As mentioned briefly above, in some instances, medical imaging
scanners may produce images with different scalable radiodensities for the
same object.
This, for example, can depend not only on the type of medical imaging scanner
or
equipment used but also on the scan parameters and/or environment of the
particular day
and/or time when the scan was taken. As a result, even if two different scans
were taken
of the same subject, the brightness and/or darkness of the resulting medical
image may be
different, which can result in less than accurate analysis results processed
from that image.
To account for such differences, in some embodiments, the normalization device
comprising one or more known samples of known materials can be scanned
together with
the subject, and the resulting image of the one or more known elements can be
used as a
basis for translating, converting, and/or normalizing the resulting image_
[0428]
Normalizing the medical images that will be analyzed can be beneficial
for several reasons. For example, medical images can be captured under a wide
variety of
conditions, all of which can affect the resulting medical images. In instances
where the
medical imager comprises a CT scanner, a number of different variables can
affect the
resulting image. Variable image acquisition parameters, for example, can
affect the
resulting image. Variable image acquisition parameters can comprise one or
more of a
kilovoltage (kV), kilovoltage peak (kVp), a milliamperage (mA), or a method of
gating,
among others. In some embodiments, methods of gating can include prospective
axial
triggering, retrospective ECG helical gating, and fast pitch helical, among
others. Varying
any of these parameters, may produce slight differences in the resulting
medical images,
even if the same subject is scanned.
[0429]
Additionally, the type of reconstruction used to prepare the image after
the scan may provide differences in medical images. Example types of
reconstruction can
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include iterative reconstruction, non-iterative reconstruction, machine
learning-based
reconstruction, and other types of physics-based reconstruction among others.
Figures
11A-11D illustrate different images reconstructed using different
reconstruction
techniques. In particular, Figure 11A illustrates a CT image reconstructed
using filtered
back projection, while Figure 11B illustrates the same CT image reconstructed
using
iterative reconstruction. As shown, the two images appear slightly different.
The
normalization device described below can be used to help account for these
differences by
providing a method for normalizing between the two. Figure 11C illustrates a
CT image
reconstructed by using iterative reconstruction, while Figure 11D illustrates
the same image
reconstructed using machine learning. Again, one can see that the images
include slight
differences, and the normalization device described herein can advantageously
be useful in
normalizing the images to account for the two differences.
[0430]
As another example, various types of image capture technologies can be
used to capture the medical images. In instances where the medical imager
comprises a
CT scanner, such image capture technologies may include a dual source scanner,
a single
source scanner, dual energy, monochromatic energy, spectral CT, photon
counting, and
different detector materials, among others. As before, images captured using
difference
parameters may appear slightly different, even if the same subject is scanned.
In addition
to CT scanners, other types of medical imagers can also be used to capture
medical images.
These can include, for example, x-ray, ultrasound, echocardiography,
intravascular
ultrasound (IVUS), MR imaging, optical coherence tomography (OCT), nuclear
medicine
imaging, positron-emission tomography (PET), single photon emission computed
tomography (SPECT), or near-field infrared spectroscopy (NIRS).
Use of the
normalization device can facilitate normalization of images such that images
captured on
these different imaging devices can be used in the methods and systems
described herein.
[0431]
Additionally, new types of medical imaging technologies are currently
being developed. Use of the normalization device can allow the methods and
systems
described herein to be used even with medical imaging technologies that are
currently being
developed or that will be developed in the future. Use of different or
emerging medical
imaging technologies can also cause slight differences between images.
[0432]
Another factor that can cause differences in medical images that can be
accounted for using the normalization device can be use of different contrast
agents during
medical imaging. Various contrast agents currently exist, and still others are
under
development. Use of the normalization device can facilitate normalization of
medical
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images regardless of the type of contrast agent used and even in instances
where no contrast
agent is used.
[0433]
These slight differences can, in some instances, negatively impact
analysis of the image, especially where analysis of the image is performed by
artificial
intelligence or machine learning algorithms that were trained or developed
using medical
images captured under different conditions. In some embodiments, the methods
and
systems described throughout this application for analyzing medical images can
include the
use of artificial intelligence and/or machine learning algorithms. Such
algorithms can be
trained using medical images. In some embodiments, the medical images that are
used to
train these algorithms can include the normalization device such that the
algorithms are
trained based on normalized images. Then, by normalizing subsequent images by
also
including the normalization device in those images, the machine learning
algorithms can
be used to analyze medical images captured under a wide variety of parameters,
such as
those described above.
[0434]
In some embodiments, the normalization device described herein is
distinguishable from a conventional phantom. In some instances, conventional
phantoms
can be used to verify if a CT machine is operating in a correct manner. These
conventional
phantoms can be used periodically to verify the calibration of the CT machine.
For
example, in some instances, conventional phantoms can be used prior to each
scan, weekly,
monthly, yearly, or after maintenance on the CT machine to ensure proper
functioning and
calibration. Notably, however, the conventional phantoms do not provide a
normalization
function that allows for normalization of the resulting medical images across
different
machines, different parameters, different patients, etc.
104351
In some embodiments, the normalization device described herein can
provide this functionality. The normalization device can allow for the
normalization of CT
data or other medical imaging data generated by various machine types and/or
for
normalization across different patients. For example, different CT devices
manufactured
by various manufacturers, can produce different coloration and/or different
gray scale
images. In another example, some CT scanning devices can produce different
coloration
and/or different gray scale images as the CT scanning device ages or as the CT
scanning
device is used or based on the environmental conditions surrounding the device
during the
scanning. In another example, patient tissue types or the like can cause
different coloration
and/or gray scale levels to appear differently in medical image scan data.
Normalization
of CT scan data can be important in order to ensure that processing of the CT
scan data or
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other medical imaging data is consistent across various data sets generated by
various
machines or the same machines used at different times and/or across different
patients. In
some embodiments, the normalization device needs to be used each time a
medical image
scan is performed because scanning equipment can change over time and/or
patients are
different with each scan. In some embodiments, the normalization device is
used in
performing each and every scan of patient in order to normalize the medical
image data of
each patient for the Al algorithm(s) used to analyze the medical image data of
the patient.
In other words, in some embodiments, the normalization device is used to
normalize to
each patient as opposed to each scanner. In some embodiments, the
normalization device
may have different known materials with different densities adjacent to each
other (e.g., as
described with reference to Figure 12F). This configuration may address an
issue present
in some CT images where the density of a pixel influences the density of the
adjacent pixels
and that influence changes with the density of each of the individual pixel.
One example of
such an embodiment can include different contrast densities in the coronary
lumen
influencing the density of the plaque pixels. The normalization device can
address this issue
by having known volumes of known substances to help to correctly evaluate
volumes of
materials/lesions within the image correcting in some way the influence of the
blooming
artifact on quantitative CT image analysis/measures. In some instances, the
normalization
device might have moving known materials with known volume and known and
controllable motion. This may allow to exclude or reduce the effect of motion
on
quantitative CT image analysis/measures.
[0436]
Accordingly, the normalization device, in some embodiments, is not a
phantom in the traditional sense because the normalization device is not just
calibrating to
a particular scanner but is also normalizing for a specific patient at a
particular time in a
particular environment for a particular scan, for particular scan image
acquisition
parameters, and/or for specific contrast protocols. Accordingly, in some
embodiments, the
normalization device can be considered a reverse phantom. This can be because,
rather
than providing a mechanism for validating a particular medical imager as a
conventional
phantom would, the normalization device can provide a mechanism for
normalizing or
validating a resulting medical image such that it can be compared with other
medical
images taken under different conditions. In some embodiments, the
normalization device
is configured to normalize the medical image data being examined with the
medical image
data used to train, test, and/or validate the Al algorithms used for analyzing
the to be
examined medical image data.
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[0437]
In some embodiments, the normalization of medical scanning data can
be necessary for the AT processing methods disclosed herein because in some
instances AT
processing methods can only properly process medical scanning data when the
medical
scanning data is consistent across all medical scanning data being processed.
For example,
in situations where a first medical scanner produces medical images showing
fatty material
as dark gray or black, whereas a second medical scanner produces medical image
showing
the same fatty material as medium or light gray, then the AT processing
methodologies of
the systems, methods, and devices disclosed herein may misidentify and/or not
fully
identify the fatty materials in one set or both sets of the medical images
produced by the
first and second medical scanners. This can be even more problematic as the
relationship
of specific material densities may not be constant, and even may change in an
non linear
way depending on the material and on the scanning parameters. In some
embodiments, the
normalization device enables the use of AT algorithms trained on certain
medical scanner
devices to be used on medical images generated by next-generation medical
scanner
devices that may have not yet even been developed.
[0438]
Figure 12A is a block diagram representative of an embodiment of a
normalization device 1200 that can be configured to normalize medical images
for use with
the methods and systems described herein.
In the illustrated embodiment, the
normalization device 1200 can include a substrate 1202. The substrate 1202 can
provide
the body or structure for the normalization device 1200. In some embodiments,
the
normalization device 1200 can comprise a square or rectangular or cube shape,
although
other shapes are possible. In some embodiments, the normalization device 1200
is
configured to be bendable and/or be self-supporting. For example, the
substrate 1202 can
be bendable and/or self-supporting. A bendable substrate 1202 can allow the
normalization
device to fit to the contours of a patient's body. In some embodiments, the
substrate 1202
can comprise one or more fiducia1s 1203. The fiducials 1203 can be configured
to facilitate
determination of the alignment of the normalization device 1200 in an image of
the
normalization device such that the position in the image of each of the one or
more
compartments holding samples of known materials can be determined.
[0439]
The substrate 1202 can also include a plurality of compartments (not
shown in Figure 12A, but see, for example, compartments 1216 of Figures 12C-
12F). The
compartments 1216 can be configured to hold samples of known materials, such
as contrast
samples 1204, studied variable samples 1206, and phantom samples 1208. In some
embodiments, the contrast samples 1204 comprise samples of contrast materials
used
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during capture of the medical image. In some embodiments, the samples of the
contrast
materials 1204 comprise one or more of iodine, Gad, Tantalum, Tungsten, Gold,
Bismuth,
or Ytterbium. These samples can be provided within the compartments 1216 of
the
normalization device 1200 at various concentrations. The studied variable
samples 1206
can includes samples of materials representative of materials to be analyzed
systems and
methods described herein. In some examples, the studied variable samples 1206
comprise
one or more of calcium 1000HU, calcium 220HU, calcium 150HU, calcium 130HU,
and a
low attenuation (e.g., 30 HU) material. Other studied variable samples 1206
provided at
different concentrations can also be included. In general, the studied
variable samples 1206
can correspond to the materials for which the medical image is being analyzed.
The
phantom samples 1208 can comprise samples of one or more phantom materials. In
some
examples, the phantom samples 1208 comprise one or more of water, fat,
calcium, uric
acid, air, iron, or blood. Other phantom samples 1208 can also be used.
104401
In some embodiments, the more materials contained in the
normalization device 1200, or the more compartments 1216 with different
materials in the
normalization device 1200, the better the normalization of the data produced
by the medical
scanner. In some embodiments, the normalization device 1200 or the substrate
1202
thereof is manufactured from flexible and/or bendable plastic. In some
embodiments, the
normalization device 1200 is adapted to be positioned within or under the
coils of an MR
scanning device. In some embodiments, the normalization device 1200 or the
substrate
1202 thereof is manufactured from rigid plastic.
104411
In the illustrated embodiment of Figure 12A, the normalization device
1200 also includes an attachment mechanism 1210. The attachment mechanism 1210
can
be used to attach the normalization device 1200 to the patient. For example,
in some
embodiments, the normalization device 1200 is attached to the patient near the
coronary
region to be imaged prior to image acquisition. In some embodiments, the
normalization
device 1200 can be adhered to the skin of a patient using an adhesive or
Velcro or some
other fastener or glue. In some embodiments, the normalization device 1200 can
be applied
to a patient like a bandage. For example, in some embodiments, a removable
Band-Aid or
sticker is applied to the skin of the patient, wherein the Band-Aid can
comprise a Velcro
outward facing portion that allows the normali zati on device having a
corresponding Velcro
mating portion to adhere to the Band-Aid or sticker that is affixed to the
skin of the patient
(see, for example, the normalization device of Figure 12G, described below).
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[0442]
In some embodiments, the attachment mechanism 1210 can be omitted,
such that the normalization device 1200 need not be affixed to the patient.
Rather, in some
embodiments, the normalization device can be placed in a medical scanner with
or without
a patient. In some embodiments, the normalization device can be configured to
be placed
alongside a patient within a medical scanner.
[0443]
In some embodiments, the normalization device 1200 can be a reusable
device or be a disposable one-time use device. In some embodiments, the
normalization
device 1200 comprises an expiration date, for example, the device can comprise
a material
that changes color to indicate expiration of the device, wherein the color
changes over time
and/or after a certain number of scans or an amount of radiation exposure
(see, for example,
Figures 12H and 121, described below). In some embodiments, the normalization
device
1200 requires refrigeration between uses, for example, to preserve one or more
of the
samples contained therein. In some embodiments, the normalization device 1200
can
comprise an indicator, such as a color change indicator, that notifies the
user that the device
has expired due to heat exposure or failure to refrigerate.
[0444]
In certain embodiments, the normalization device 1200 comprises a
material that allows for heat transfer from the skin of the patient in order
for the materials
within the normalization device 1200 to reach the same or substantially the
same
temperature of the skin of the patient because in some cases the temperature
of the materials
can affect the resulting coloration or gray-scale of the materials produced by
the image
scanning device_ For example, the substrate 1202 can comprise a material with
a relatively
high heat transfer coefficient to facilitate heat transfer from the patient to
the samples within
the substrate 1202. In some embodiments, the normalization device 1200 can be
removably
coupled to a patient's skin by using an adhesive that can allow the device to
adhere to the
skin of a patient.
[0445]
In some embodiments, the normalization device 1200 can be used in the
imaging field of view or not in the imaging field of view. In some
embodiments, the
normalization device 1200 can be imaged simultaneously with the patient image
acquisition
or sequentially. Sequential use can comprise first imaging the normalization
device 1200
and the imaging the patient shortly thereafter using the same imaging
parameters (or vice
versa). In some embodiments, the normalization device 1200 can be static or
programmed
to be in motion or movement in sync with the image acquisition or the
patient's heart or
respiratory motion. In some embodiments, the normalization device 1200 can
utilize
comparison to image domain-based data or projection domain-based data. In some
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embodiments, the normalization device 1200 can be a 2D (area), or 3D (volume),
or 4D
(changes with time) device. In some embodiments, two or more normalization
devices
1200 can be affixed to and/or positioned alongside a patient during medical
image scanning
in order to account for changes in coloration and/or gray scale levels at
different depths
within the scanner and/or different locations within the scanner.
[0446]
In some embodiments, the normalization device 1200 can comprise one
or more layers, wherein each layer comprises compartments for holding the same
or
different materials as other layers of the device. Figure 12B, for example,
illustrates a
perspective view of an embodiment of a normalization device 1200 including a
multilayer
substrate 1202. In the illustrated embodiment, the substrate 1202 comprises a
first layer
1212 and a second layer 1214. The second layer 1214 can be positioned above
the first
layer 1212. In other embodiments, one or more additional layers may be
positioned above
the second layer 1214. Each of the layers 1212, 1214 can be configured with
compartments
for holding the various known samples, as shown in Figure 12C. In some
embodiments,
the various layers 1212, 1214 of the normalization device 1200 allow for
normalization at
various depth levels for various scanning machines that perform three-
dimensional
scanning, such as MR and ultrasound. In some embodiments, the system can be
configured
to normalize by averaging of coloration and/or gray scale level changes in
imaging
characteristics due to changes in depth.
[0447]
Figure 12C is a cross-sectional view of the normalization device 1200
of Figure 12B illustrating various compartments 1216 positioned therein for
holding
samples of known materials for use during normalization. The compartments 1216
can be
configured to hold, for example, the contrast samples 1204, the studied
variable samples
1206, and the phantom samples 1208 illustrated in Figure 12A. The compartments
1216
may comprise spaces, pouches, cubes, spheres, areas, or the like, and within
each
compartment 1216 there is contained one or more compounds, fluids, substances,
elements,
materials, and the like. In some embodiments, each of the compartments 1216
can
comprise a different substance or material. In some embodiments, each
compartment 1216
is air-tight and sealed to prevent the sample, which may be a liquid, from
leaking out.
[0448]
Within each layer 1212, 1214, or within the substrate 1202, the
normalization device 1200 may include different arrangements for the
compartments 1216.
Figure 12D illustrates a top down view of an example arrangement of a
plurality of
compartments 1216 within the normalization device 1200. In the illustrated
embodiment,
the plurality of compartments 1216 are arranged in a rectangular or grid-like
pattern. Figure
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12E illustrates a top down view of another example arrangement of a plurality
of
compartments 1216 within a normalization device 1200. In the illustrated
embodiment, the
plurality of compartments 1216 are arranged in a circular pattern. Other
arrangements are
also possible.
[0449]
Figure 12F is a cross-sectional view of another embodiment of a
normalization device 1200 illustrating various features thereof, including
adjacently
arranged compartments 1216A, self-sealing fillable compartments 1216B, and
compartments of various sizes and shapes 1216C. As shown in Figure 12F, one or
more
of the compartments 1216A can be arranged so as to be adjacent to each other
so that
materials within the compartments 1216A can be in contact with and/or in close
proximity
to the materials within the adjacent compartments 1216A. In some embodiments,
the
normalization device 1200 comprises high density materials juxtaposed to low
density
materials in order to determine how a particular scanning device displays
certain materials,
thereby allowing normalization across multiple scanning devices. In some
embodiments,
certain materials are positioned adjacent or near other materials because
during scanning
certain materials can influence each other. Examples of materials that can be
placed in
adjacently positioned compartments 1216A can include iodine, air, fat
material, tissue,
radioactive contrast agent, gold, iron, other metals, distilled water, and/or
water, among
others.
[0450]
In some embodiments, the normalization device 1200 is configured
receive material and/or fluid such that the normalization device is self-
sealing.
Accordingly, Figure 12F illustrates compartments 1216B that are self-sealing.
These can
allow a material to be injected into the compat
_________________________________ intent 1216B and then sealed therein. For
example, a radioactive contrast agent can be injected in a self-sealing manner
into a
compartment 1216B of the normalization device 1200, such that the medical
image data
generated from the scanning device can be normalized over time as the
radioactive contrast
agent decays over time during the scanning procedure. In some embodiments, the
normalization device can be configured to contain materials specific for a
patient and/or a
type of tissue being analyzed and/or a disease type and/or a scanner machine
type.
[0451]
In some embodiments, the normalization device 1200 can be configured
measure scanner resolution and type of resolution by configuring the
normalization device
1200 with a plurality of shapes, such as a circle. Accordingly, the compai
______ intents 1216C
can be provided with different shapes and sizes. Figures 12F illustrates an
example wherein
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compartments 1216C are provided with different shapes (cubic and spherical)
and different
sizes. In some embodiments, all compartments 1216 can be the same shape and
size.
[0452]
In some embodiments, the size of one or more compartment 1216 of the
normalization device 1200 can be configured or selected to correspond to the
resolution of
the medical image scanner. For example, in some embodiments, if the spatial
resolution of
a medical image scanner is 0.5 mm X 0.5 mm X 0.5 mm, then the dimension of the
compartments of the normalization device can also be 0.5 mm X 0.5 mm X 0.5 mm.
In
some embodiments, the sizes of the compartments range from 0.5 mm to 0.75 mm.
In some
embodiments, the width of the compartments of the normalization device can be
about 0.1
mm, about 0.15 mm, about 0.2 mm, about 0.25 mm, about 0.3 mm, about 0.35 mm,
about
0.4 mm, about 0.45 mm, about 0.5 mm, about 0.55 mm, about 0.6 mm, about 0.65
mm,
about 0.7 mm, about 0.75 mm, about 0.8 mm, about 0.85 mm, about 0.9 mm, about
0.95
mm, about 1.0 mm, and/or within a range defined by two of the aforementioned
values. In
some embodiments, the length of the compartments of the normalization device
can be
about 0.1 mm, about 0.15 mm, about 0.2 mm, about 0.25 mm, about 0.3 mm, about
0.35
mm, about 0.4 mm, about 0.45 mm, about 0.5 mm, about 0.55 mm, about 0.6 mm,
about
0.65 mm, about 0.7 mm, about 0.75 mm, about 0.8 mm, about 0.85 mm, about 0.9
mm,
about 0.95 mm, about 1.0 mm, and/or within a range defined by two of the
aforementioned
values. In some embodiments, the height of the compartments of the
normalization device
can be about 0.1 mm, about 0.15 mm, about 0.2 mm, about 0.25 mm, about 0.3 mm,
about
0.35 mm, about 0.4 mm, about 0.45 mm, about 0.5 mm, about 0_55 mm, about 0.6
mm,
about 0.65 mm, about 0.7 mm, about 0.75 mm, about 0.8 mm, about 0.85 mm, about
0.9
mm, about 0.95 mm, about 1.0 mm, and/or within a range defined by two of the
aforementioned values.
[0453]
In some embodiments, the dimensions of each of the compartments
1216 in the normalization device 1200 are the same or substantially the same
for all of the
compartments 1216. In some embodiments, the dimensions of some or all of the
compartments 1216 in the normalization device 1200 can be different from each
other in
order for a single normalization device 1200 to have a plurality of
compartments having
different dimensions such that the normalization device 1200 can be used in
various
medical image scanning devices having different resolution capabilities (for
example, as
illustrated in Figure 12F). In some embodiments, a normalization device 1200
having a
plurality of compartments 1216 with differing dimensions enable the
normalization device
to be used to determine the actual resolution capability of the scanning
device. In some
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embodiments, the size of each compartment 1216 may extend up to 10 mm, and the
sizes
of each compartment may be variable depending upon the material contained
within.
[0454]
In the illustrated embodiment of Figures 12C and 12F, the normalization
device 1200 includes an attachment mechanism 1210 which includes an adhesive
surface
1218. The adhesive surface 1218 can be configured to affix (e.g., removably
affix) the
normalization device 1200 to the skin of the patient. Figure 12G is a
perspective view
illustrating an embodiment of an attachment mechanism 1210 for a normalization
device
1200 that uses hook and loop fasteners 1220 to secure a substrate of the
normalization
device to a fastener of the normalization device 1200. In the illustrated
embodiment, an
adhesive surface 1218 can be configured to be affixed to the patient. The
adhesive surface
1218 can include a first hook and loop fastener 1220. A corresponding hook and
loop
fastener 1220 can be provided on a lower surface of the substrate 1202 and
used to
removably attach the substrate 1202 to the adhesive surface 1218 via the hook
and loop
fasteners 1220.
[0455]
Figures 12H and 121 illustrate an embodiment of a normalization device
1200 that includes an indicator 1222 configured to indicate an expiration
status of the
normalization device 1200. The indicator 1222 can comprise a material that
changes color
or reveals a word to indicate expiration of the device, wherein the color or
text changes or
appears over time and/or after a certain number of scans or an amount of
radiation exposure.
Figure 12H illustrates the indicator 1222 in a first state representative of a
non-expired
state, and Figure 121 illustrates the indicator 1222 in a second state
representative of an
expired state. In some embodiments, the normalization device 1200 requires
refrigeration
between uses. In some embodiments, the indicator 1222, such as a color change
indicator,
can notify the user that the device has expired due to heat exposure or
failure to refrigerate.
[0456]
In some embodiments, the normalization device 1200 can be used with
a system configured to set distilled water to a gray scale value of zero, such
that if a
particular medical image scanning device registers the compartment of the
normalization
device 1200 comprising distilled water as having a gray scale value of some
value other
than zero, then the system can utilize an algorithm to transpose or transform
the registered
value to zero. In some embodiments, the system is configured to generate a
normalization
algorithm based on known values established for particular substances in the
compartments
of the normalization device 1200, and on the detected/generated values by a
medical image
scanning device for the same substances in the compartments 1216 of the
normalization
device 1200. In some embodiments, the normalization device 1200 can be
configured to
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generate a normalization algorithm based on a linear regression model to
normalize medical
image data to be analyzed. In some embodiments, the normalization device 1200
can be
configured to generate a normalization algorithm based on a non-linear
regression model
to normalize medical image data to be analyzed. In some embodiments, the
normalization
device 1200 can be configured to generate a normalization algorithm based on
any type of
model or models, such as an exponential, logarithmic, polynomial, power,
moving average,
and/or the like, to normalize medical image data to be analyzed. In some
embodiments,
the normalization algorithm can comprise a two-dimensional transformation. In
some
embodiments, the normalization algorithm can comprise a three-dimensional
transformation to account for other factors such as depth, time, and/or the
like.
[0457]
By using the normalization device 1200 to scan known substances using
different machines or the same machine at different times, the system can
normalize CT
scan data across various scanning machines and/or the same scanning machine at
different
times. In some embodiments, the normalization device 1200 disclosed herein can
be used
with any scanning modality including but not limited to x-ray, ultrasound,
echocardiogram,
magnetic resonance (MR), optical coherence tomography (OCT), intravascular
ultrasound
(IVUS) and/or nuclear medicine imaging, including positron-emission tomography
(PET)
and single photon emission computed tomography (SPECT).
[0458]
In some embodiments, the normalization device 1200 contains one or
more materials that form plaque (e.g., studied variable samples 1206) and one
or more
materials that are used in the contrast that is given to the patient through a
vein during
examination (e.g., contrast samples 1204). In some embodiments, the materials
within the
compartments 1216 include iodine of varying concentrations, calcium of varying
densities,
non-calcified plaque materials or equivalents of varying densities, water,
fat, blood or
equivalent density material, iron, uric acid, air, gadolinium, tantalum,
tungsten, gold,
bismuth, ytterbium, and/or other material. In some embodiments, the training
of the AT
algorithm can be based at least in part on data relating to the density in the
images of the
normalization device 1200. As such, in some embodiments, the system can have
access to
and/or have stored pre-existing data on how the normalization device 1200
behaved or was
shown in one or more images during the training of the Al algorithm. In some
embodiments, the system can use such prior data as a baseline to determine the
difference
with how the normalization device 1200 behaves in the new or current CT scan
to which
the AT algorithm is applied to. In some embodiments, the determined difference
can be
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used to calibrate, normalize, and/or map one or more densities in recently
acquired image(s)
to one or more images that were obtained and/or used during training of the Al
algorithm.
[0459]
As a non-limiting example, in some embodiments, the normalization
device 1200 comprises calcium. If, for example, the calcium in the CT or
normalization
device 1200 that was used to train the Al algorithm(s) showed a density of 300
Hounsfield
Units (HU), and if the same calcium showed a density of 600 HU in one or more
images of
a new scan, then the system, in some embodiments, may be configured to
automatically
divide all calcium densities in half to normalize or transform the new CT
image(s) to be
equivalent to the old CT image(s) used to train the Al algorithm.
104601
In some embodiments, as discussed above, the normalization device
1200 comprises a plurality of all materials that may be relevant, which can be
advantageous
as different materials can change densities in different amounts across scans.
For example,
if the density of calcium changes 2X across scans, the density of fat may
change around
10% across the same scans. As such, it can be advantageous for the
normalization device
1200 to comprise a plurality of materials, such as for example one or more
materials that
make up plaque, blood, contrast, and/or the like.
[0461]
As described above, in some embodiments, the system can be
configured to normalize, map, and/or calibrate density readings and/or CT
images obtained
from a particular scanner and/or subject proportionally according to changes
or differences
in density readings and/or CT images obtained from one or more materials of a
normalization device 1200 using a baseline scanner compared to density
readings and/or
CT images obtained from one or more same materials of a normalization device
1200 using
the particular scanner and/or subject. As a non-limiting example, for
embodiments in
which the normalization device 1200 comprises calcium, the system can be
configured to
apply the same change in density of known calcium between the baseline scan
and the new
scan, for example 2X, to all other calcium readings of the new scan to
calibrate and/or
normalize the readings.
[0462]
In some embodiments, the system can be configured to normalize, map,
and/or calibrate density readings and/or CT images obtained from a particular
scanner
and/or subject by averaging changes or differences between density readings
and/or CT
images obtained from one or more materials of a normalization device 1200
using a
baseline scanner compared to density readings and/or CT images obtained from
one or
more materials or areas of a subject using the same baseline scanner. As a non-
limiting
example, for embodiments in which the normalization device 1200 comprises
calcium, the
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system can be configured to determine a difference, or a ratio thereof, in
density readings
between calcium in the normalization device 1200 and other areas of calcium in
the subject
during the baseline scan. In some embodiments, the system can be configured to
similarly
determine a difference, or a ratio thereof, in density readings between
calcium in the
normalization device 1200 and other areas of calcium in the subject during the
new scan;
dividing the value of calcium from the device to the value of calcium anywhere
else in the
image can cancel out any change as the difference in conditions can affect the
same material
in the same manner.
[0463]
In some embodiments, the device will account for scan parameters (such
as mA or kVp), type and number of x-ray sources within a scanner (such as
single source
or dual source), temporal resolution of a scanner, spatial resolution of
scanner or image,
image reconstruction method (such as adaptive statistical iterative
reconstruction, model-
based iterative reconstruction, machine learning-based iterative
reconstruction or similar);
image reconstruction method (such as from different types of kernels,
overlapping slices
from retrospective ECG-helical studies, non-overlapping slices from
prospective axial
triggered studies, fast pitch helical studies, or half vs. full scan integral
reconstruction);
contrast density accounting for internal factors (such as oxygen, blood,
temperature, and
others); contrast density accounting for external factors (such as contrast
density,
concentration, osmolality and temporal change during the scan); detection
technology
(such as material, collimation and filtering); spectral imaging (such as
polychromatic,
monochromatic and spectral imaging along with material basis decomposition and
single
energy imaging); photon counting; and/or scanner brand and model.
[0464]
In some embodiments, the normalization device 1200 can be applied to
MM studies, and account for one or more of: type of coil; place of
positioning, number of
antennas; depth from coil elements; image acquisition type; pulse sequence
type and
characteristics; field strength, gradient strength, slew rate and other
hardware
characteristics; magnet vendor, brand and type; imaging characteristics
(thickness, matrix
size, field of view, acceleration factor, reconstruction methods and
characteristics, 2D, 3D,
4D [cine imaging, any change over time], temporal resolution, number of
acquisitions,
diffusion coefficients, method of populating k-space); contrast (intrinsic
[oxygen, blood,
temperature, etc.] and extrinsic types, volume, temporal change after
administration); static
or moving materials; quantitative imaging (including Ti T2 mapping, ADC,
diffusion,
phase contrast, and others); and/or administration of pharmaceuticals during
image
acquisition.
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[0465]
In some embodiments, the normalization device 1200 can be applied to
ultrasound studies, and account for one or more of: type and machine brands;
transducer
type and frequency; greyscale, color, and pulsed wave doppler; B- or M-mode
doppler type;
contrast agent; field of view; depth from transducer; pulsed wave deformity
(including
elastography), angle; imaging characteristics (thickness, matrix size, field
of view,
acceleration factor, reconstruction methods and characteristics, 2D, 3D, 4D
[cine imaging,
any change over time]; temporal resolution; number of acquisitions; gain,
and/or focus
number and places, amongst others.
[0466]
In some embodiments, the normalization device 1200 can be applied to
nuclear medicine studies, such as PET or SPECT and account for one or more of:
type and
machine brands; for PET/CT all CT applies; for PET/MR all MR applies; contrast
(radiopharmaceutical agent types, volume, temporal change after
administration); imaging
characteristics (thickness, matrix size, field of view, acceleration factor,
reconstruction
methods and characteristics, 2D, 3D, 4D [cine imaging, any change over time];
temporal
resolution; number of acquisitions; gain, and/or focus number and places,
amongst others.
[0467]
In some embodiments, the normalization device may have different
'mown materials with different densities adjacent to each other. This may
address any issue
present in some CT images where the density of a pixel influences the density
of the
adjacent pixels and that influence changes with the density of each of the
individual pixel.
One example of this embodiment being different contrast densities in the
coronary lumen
influencing the density of the plaque pixels. In some embodiments, the
normalization
device may include known volumes of known substances to help to correctly
evaluate
volumes of materials/lesions within the image in order to correct the
influence of the
blooming artifact on quantitative CT image analysis/measures. In some
embodiments, the
normalization device might have moving known materials with known volume and
known
and controllable motion. This would allow to exclude or reduce the effect of
motion on
quantitative CT image analysis/measures.
[0468]
In some embodiments, having a known material on the image in the
normalization device might also be helpful for material specific
reconstructions from the
same image. For example, it can be possible to use only one set of images to
display only
known materials, not needing multiple kV/spectral image hardware.
[0469]
Figure 12J is a flowchart illustrating an example method 1250 for
normalizing medical images for an algorithm-based medical imaging analysis
such as the
analyses described herein. Use of the normalization device can improve
accuracy of the
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algorithm-based medical imaging analysis. The method 1250 can be a computer-
implemented method, implemented on a system that comprises a processor and an
electronic storage medium. The method 1250 illustrates that the normalization
device can
be used to normalize medical images captured under different conditions. For
example, at
block 1252, a first medical image of a coronary region of a subject and the
normalization
device is accessed. The first medical image can be obtained non-invasively.
The
normalization device can comprise a substrate comprising a plurality of
compartments,
each of the plurality of compartments holding a sample of a known material,
for example
as described above. At block 1254, a second medical image of a coronary region
of a
subject and the normalization device is captured. The second medical image can
be
obtained non-invasively. Although the method 1250 is described with reference
to a
coronary region of a patient, the method is also applicable to all body parts
and not only
the vessels as the same principles apply to all body parts, all time points
and all imaging
devices. This can even include -live" type of images such as fluoroscopy or MR
real time
image.
[0470]
As illustrated by the portion within the dotted lines, the first medical
image and the second medical image can comprise at least one of the following:
(1) one or
more first variable acquisition parameters associated with capture of the
first medical image
differ from a corresponding one or more second variable acquisition parameters
associated
with capture of the second medical image, (2) a first image capture technology
used to
capture the first medical image differs from a second image capture technology
used to
capture the second medical image, and (3) a first contrast agent used during
the capture of
the first medical image differs from a second contrast agent used during the
capture of the
second medical image.
104711
In some embodiments, the first medical image and the second medical
image each comprise a CT image and the one or more first variable acquisition
parameters
and the one or more second variable acquisition parameters comprise one or
more of a
kilovoltage (kV), kilovoltage peak (kVp), a milliamperage (mA), or a method of
gating. In
some embodiments, the method of gating comprises one of prospective axial
triggering,
retrospective ECG helical gating, and fast pitch helical. In some embodiments,
the first
image capture technology and the second image capture technology each comprise
one of
a dual source scanner, a single source scanner, dual energy, monochromatic
energy,
spectral CT, photon counting, and different detector materials. In some
embodiments, the
first contrast agent and the second contrast agent each comprise one of an
iodine contrast
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of varying concentration or a non-iodine contrast agent. In some embodiments,
the first
image capture technology and the second image capture technology each comprise
one of
CT, x-ray, ultrasound, echocardiography, intravascular ultrasound (IVUS), MR
imaging,
optical coherence tomography (OCT), nuclear medicine imaging, positron-
emission
tomography (PET), single photon emission computed tomography (SPECT), or near-
field
infrared spectroscopy (NIRS).
[0472]
In some embodiments, a first medical imager that captures the first
medical imager is different than a second medical image that capture the
second medical
image. In some embodiments, the subject of the first medical image is
different than the
subject of the first medical image. In some embodiments, wherein the subject
of the first
medical image is the same as the subject of the second medical image. In some
embodiments, wherein the subject of the first medical image is different than
the subject of
the second medical image. In some embodiments, wherein the capture of the
first medical
image is separated from the capture of the second medical image by at least
one day. In
some embodiments, wherein the capture of the first medical image is separated
from the
capture of the second medical image by at least one day. In some embodiments,
wherein a
location of the capture of the first medical image is geographically separated
from a
location of the capture of the second medical image.
[0473]
Accordingly, it is apparent that the first and second medical images can
be acquired under different conditions that can cause differences between the
two images,
even if the subject of each image is the same. The normalization device can
help to
normalize and account for these differences.
[0474]
The method 1250 then moves to blocks 1262 and 1264, at which image
parameters of the normalization device within the first medical image and
which image
parameters of the normalization device within the second medical image are
identified,
respectively. Due to different circumstances under which the first and second
medical
images were captured, the normalization device may appear differently in each
image, even
though the normalization device includes the same known samples.
[0475]
Next, at blocks 1266 and 1268, the method generates a normalized first
medical image for the algorithm-based medical imaging analysis based in part
on the first
identified image parameters of the normalization device within the first
medical image and
generates a normalized second medical image for the algorithm-based medical
imaging
analysis based in part on the second identified image parameters of the
normalization
device within the second medical image, respectively. In these blocks, each
image is
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normalized based on the appearance or determined parameters of the
normalization device
in each image.
[0476]
In some embodiments, the algorithm-based medical imaging analysis
comprises an artificial intelligence or machine learning imaging analysis
algorithm, and the
artificial intelligence or machine learning imaging analysis algorithm was
trained using
images that included the normalization device.
System Overview
[0477]
In some embodiments, the systems, devices, and methods described
herein are implemented using a network of one or more computer systems, such
as the one
illustrated in Figure 13. Figure 13 is a block diagram depicting an
embodiment(s) of a
system for medical image analysis, visualization, risk assessment, disease
tracking,
treatment generation, and/or patient report generation.
[0478]
As illustrated in Figure 13, in some embodiments, a main server system
1302 is configured to perform one or more processes, analytics, and/or
techniques
described herein, some of which relating to medical image analysis,
visualization, risk
assessment, disease tracking, treatment generation, and/or patient report
generation. In
some embodiments, the main server system 1302 is connected via an electronic
communications network 1308 to one or more medical facility client systems
1304 and/or
one or more user access point systems 1306. For example, in some embodiments,
one or
more medical facility client systems 1304 can be configured to access a
medical image
taken at the medical facility of a subject, which can then be transmitted to
the main server
system 1302 via the network 1308 for further analysis. After analysis, in some
embodiments, the analysis results, such as for example quantified plaque
parameters,
assessed risk of a cardiovascular event, generated report, annotated and/or
derived medical
images, and/or the like, can be transmitted back to the medical facility
client system 1304
via the network 1308. In some embodiments, the analysis results, such as for
example
quantified plaque parameters, assessed risk of a cardiovascular event,
generated report,
annotated and/or derived medical images, and/or the like, can be transmitted
also to a user
access point system 1306, such as a smartphone or other computing device of
the patient
or subject. As such, in some embodiments, a patient can be allowed to view
and/or access
a patient-specific report and/or other analyses generated and/or derived by
the system from
the medical image on the patient's computing device.
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[0479]
In some embodiments, the main server system 1302 can comprise and/or
be configured to access one or more modules and/or databases for performing
the one or
more processes, analytics, and/or techniques described herein. For example, in
some
embodiments, the main server system 1302 can comprise an image analysis module
1310,
a plaque quantification module 1312, a fat quantification module 1314, an
atherosclerosis,
stenosis, and/or ischemia analysis module 1316, a visualization/GUI module
1318, a risk
assessment module 1320, a disease tracking module 1322, a normalization module
1324, a
medical image database 1326, a parameter database 1328, a treatment database
1330, a
patient report database 1332, a normalization device database 1334, and/or the
like.
104801
In some embodiments, the image analysis module 1310 can be
configured to perform one or more processes described herein relating to image
analysis,
such as for example vessel and/or plaque identification from a raw medical
image. In some
embodiments, the plaque quantification module 1312 can be configured to
perform one or
more processes described herein relating to deriving or generating quantified
plaque
parameters, such as for example radiodensity, volume, heterogeneity, and/or
the like of
plaque from a raw medical image. In some embodiments, the fat quantification
module
1314 can be configured to perform one or more processes described herein
relating to
deriving or generating quantified fat parameters, such as for example
radiodensity, volume,
heterogeneity. and/or the like of fat from a raw medical image. In some
embodiments, the
atherosclerosis, stenosis. and/or ischemia analysis module 1316 can be
configured to
perform one or more processes described herein relating to analyzing and/or
generating an
assessment or quantification of atherosclerosis, stenosis, and/or ischemia
from a raw
medical image. In some embodiments, the visualization / GUI module 1318 can be
configured to perform one or more processes described herein relating to
deriving or
generating one or more visualizations and/or GUIs, such as for example a
straightened view
of a vessel identifying areas of good and/or bad plaque from a raw medical
image. In some
embodiments, the risk assessment module 1320 can be configured to perform one
or more
processes described herein relating to deriving or generating risk assessment,
such as for
example of a cardiovascular event or disease from a raw medical image. In some
embodiments, the disease tracking module 1322 can be configured to perform one
or more
processes described herein relating to tracking a plaque-based disease, such
as for example
atherosclerosis, stenosis, ischemia, and/or the like from a raw medical image.
In some
embodiments, the normalization module 1324 can be configured to perform one or
more
processes described herein relating to normalizing and/or translating a
medical image, for
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example based on a medical image of a normalization device comprising known
materials,
for further processing and/or analysis.
104811
In some embodiments, the medical image database 1326 can comprise
one or more medical images that are used for one or more of the various
analysis techniques
and processes described herein. In some embodiments, the parameter database
1328 can
comprise one or more parameters derived from raw medical images by the system,
such as
for example one or more vessel morphology parameters, quantified plaque
parameters,
quantified fat parameters, and/or the like. In some embodiments, the treatment
database
1328 can comprise one or more recommended treatments derived from raw medical
images
by the system. In some embodiments, the patient report database 1332 can
comprise one
or more patient-specific reports derived from raw medical images by the system
and/or one
or more components thereof that can be used to generate a patient-specific
report based on
medical image analysis results. In some embodiments, the normalization
database 1334
can comprise one or more historical data points and/or datasets of normalizing
various
medical images and/or the specific types of medical imaging scanners and/or
specific scan
parameters used to obtain those images, as well as previously used
normalization variables
and/or translations for different medical images.
Computer System
[0482]
In some embodiments, the systems, processes, and methods described
herein are implemented using a computing system, such as the one illustrated
in Figure 14.
The example computer system 1402 is in communication with one or more
computing
systems 1420 and/or one or more data sources 1422 via one or more networks
1418. While
Figure 14 illustrates an embodiment of a computing system 1402, it is
recognized that the
functionality provided for in the components and modules of computer system
1402 may
be combined into fewer components and modules, or further separated into
additional
components and modules.
[0483]
The computer system 1402 can comprise a Medical Analysis, Risk
Assessment, and Tracking Module 1414 that carries out the functions, methods,
acts, and/or
processes described herein. The Medical Analysis, Risk Assessment, and
Tracking Module
1414 is executed on the computer system 1402 by a central processing unit 1406
discussed
further below.
[0484]
In general the word -module," as used herein, refers to logic embodied
in hardware or firmware or to a collection of software instructions, having
entry and exit
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points. Modules are written in a program language, such as JAVA, C or C++,
PYTHON
or the like. Software modules may be compiled or linked into an executable
program,
installed in a dynamic link library, or may be written in an interpreted
language such as
BASIC, PERL, LUA, or Python. Software modules may be called from other modules
or
from themselves, and/or may be invoked in response to detected events or
interruptions.
Modules implemented in hardware include connected logic units such as gates
and flip-
flops, and/or may include programmable units, such as programmable gate arrays
or
processors.
[0485]
Generally, the modules described herein refer to logical modules that
may be combined with other modules or divided into sub-modules despite their
physical
organization or storage. The modules are executed by one or more computing
systems, and
may be stored on or within any suitable computer readable medium, or
implemented in-
whole or in-part within special designed hardware or firmware. Not all
calculations,
analysis, and/or optimization require the use of computer systems, though any
of the above-
described methods, calculations, processes, or analyses may be facilitated
through the use
of computers. Further, in some embodiments, process blocks described herein
may be
altered, rearranged, combined, and/or omitted.
[0486]
The computer system 1402 includes one or more processing units (CPU)
1406, which may comprise a microprocessor. The computer system 1402 further
includes
a physical memory 1410, such as random access memory (RAM) for temporary
storage of
information, a read only memory (ROM) for permanent storage of information,
and a mass
storage device 1404, such as a backing store, hard drive, rotating magnetic
disks, solid state
disks (S SD), flash memory, phase-change memory (PCM), 3D XPoint memory,
diskette,
or optical media storage device. Alternatively, the mass storage device may be
implemented in an array of servers. Typically, the components of the computer
system
1402 are connected to the computer using a standards based bus system. The bus
system
can be implemented using various protocols, such as Peripheral Component
Interconnect
(PCI), Micro Channel, SCSI, Industrial Standard Architecture (ISA) and
Extended ISA
(EISA) architectures.
[0487]
The computer system 1402 includes one or more input/output (I/O)
devices and interfaces 1412, such as a keyboard, mouse, touch pad, and
printer. The I/O
devices and interfaces 1412 can include one or more display devices, such as a
monitor,
that allows the visual presentation of data to a user. More particularly, a
display device
provides for the presentation of GUIs as application software data, and multi-
media
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presentations, for example. The I/O devices and interfaces 1412 can also
provide a
communications interface to various external devices. The computer system 1402
may
comprise one or more multi-media devices 1408, such as speakers, video cards,
graphics
accelerators, and microphones, for example.
[0488]
The computer system 1402 may run on a variety of computing devices,
such as a server, a Windows server, a Structure Query Language server, a Unix
Server, a
personal computer, a laptop computer, and so forth. In other embodiments, the
computer
system 1402 may run on a cluster computer system, a mainframe computer system
and/or
other computing system suitable for controlling and/or communicating with
large
databases, performing high volume transaction processing, and generating
reports from
large databases. The computing system 1402 is generally controlled and
coordinated by an
operating system software, such as z/OS, Windows, Linux, UNIX, BSD, SunOS,
Solaris,
MacOS, or other compatible operating systems, including proprietary operating
systems.
Operating systems control and schedule computer processes for execution,
perform
memory management, provide file system, networking, and I/O services, and
provide a user
interface, such as a graphical user interface (GUI), among other things.
[0489]
The computer system 1402 illustrated in Figure 14 is coupled to a
network 1418, such as a LAN, WAN, or the Internet via a communication link
1416 (wired,
wireless, or a combination thereof). Network 1418 communicates with various
computing
devices and/or other electronic devices. Network 1418 is communicating with
one or more
computing systems 1420 and one or more data sources 1422. The Medical
Analysis, Risk
Assessment, and Tracking Module 1414 may access or may be accessed by
computing
systems 1420 and/or data sources 1422 through a web-enabled user access point.
Connections may be a direct physical connection, a virtual connection, and
other
connection type. The web-enabled user access point may comprise a browser
module that
uses text, graphics, audio, video, and other media to present data and to
allow interaction
with data via the network 1418.
[0490]
Access to the Medical Analysis, Risk Assessment, and Tracking Module
1414 of the computer system 1402 by computing systems 1420 and/or by data
sources 1422
may be through a web-enabled user access point such as the computing systems'
1420 or
data source's 1422 personal computer, cellular phone, smartph on e, laptop,
tablet computer,
e-reader device, audio player, or other device capable of connecting to the
network 1418.
Such a device may have a browser module that is implemented as a module that
uses text,
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graphics, audio, video, and other media to present data and to allow
interaction with data
via the network 1418.
104911
The output module may be implemented as a combination of an all-
points addressable display such as a cathode ray tube (CRT), a liquid crystal
display (LCD),
a plasma display, or other types and/or combinations of displays. The output
module may
be implemented to communicate with input devices 1412 and they also include
software
with the appropriate interfaces which allow a user to access data through the
use of stylized
screen elements, such as menus, windows, dialogue boxes, tool bars, and
controls (for
example, radio buttons, check boxes, sliding scales, and so forth).
Furthermore, the output
module may communicate with a set of input and output devices to receive
signals from
the user.
[0492]
The input device(s) may comprise a keyboard, roller ball, pen and stylus,
mouse, trackball, voice recognition system, or pre-designated switches or
buttons. The
output device(s) may comprise a speaker, a display screen, a printer, or a
voice synthesizer.
In addition a touch screen may act as a hybrid input/output device. In another
embodiment,
a user may interact with the system more directly such as through a system
terminal
connected to the score generator without communications over the Internet, a
WAN, or
LAN, or similar network.
[0493]
In some embodiments, the system 1402 may comprise a physical or
logical connection established between a remote microprocessor and a mainframe
host
computer for the express purpose of uploading, downloading, or viewing
interactive data
and databases on-line in real time. The remote microprocessor may be operated
by an entity
operating the computer system 1402, including the client server systems or the
main server
system, and/or may be operated by one or more of the data sources 1422 and/or
one or more
of the computing systems 1420. In some embodiments, terminal emulation
software may
be used on the microprocessor for participating in the micro-mainframe link.
[0494]
In some embodiments, computing systems 1420 who are internal to an
entity operating the computer system 1402 may access the Medical Analysis,
Risk
Assessment, and Tracking Module 1414 internally as an application or process
run by the
CPU 1406.
[0495]
The computing system 1402 may include one or more internal and/or
external data sources (for example, data sources 1422). In some embodiments,
one or more
of the data repositories and the data sources described above may be
implemented using a
relational database, such as DB2, Sybase, Oracle, CodeBase, and Microsoft SQL
Server
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as well as other types of databases such as a flat-file database, an entity
relationship
database, and object-oriented database, and/or a record-based database.
[0496]
The computer system 1402 may also access one or more databases 1422.
The databases 1422 may be stored in a database or data repository. The
computer system
1402 may access the one or more databases 1422 through a network 1418 or may
directly
access the database or data repository through I/O devices and interfaces
1412. The data
repository storing the one or more databases 1422 may reside within the
computer system
1402.
[0497]
In some embodiments, one or more features of the systems, methods,
and devices described herein can utilize a URL and/or cookies, for example for
storing
and/or transmitting data or user information. A Uniform Resource Locator (URL)
can
include a web address and/or a reference to a web resource that is stored on a
database
and/or a server. The URL can specify the location of the resource on a
computer and/or a
computer network. The URL can include a mechanism to retrieve the network
resource. The source of the network resource can receive a URL, identify the
location of
the web resource, and transmit the web resource back to the requestor. A URL
can be
converted to an IP address, and a Domain Name System (DNS) can look up the URL
and
its corresponding IP address. URLs can be references to web pages, file
transfers, emails,
database accesses, and other applications. The URLs can include a sequence of
characters
that identify a path, domain name, a file extension, a host name, a query, a
fragment,
scheme, a protocol identifier, a port number, a username, a password, a flag,
an object, a
resource name and/or the like. The systems disclosed herein can generate,
receive,
transmit, apply, parse, serialize, render, and/or perform an action on a URL.
104981
A cookie, also referred to as an HTTP cookie, a web cookie, an intemet
cookie, and a browser cookie, can include data sent from a website and/or
stored on a user's
computer. This data can be stored by a user's web browser while the user is
browsing. The
cookies can include useful information for websites to remember prior browsing
information, such as a shopping cart on an online store, clicking of buttons,
login
information, and/or records of web pages or network resources visited in the
past. Cookies
can also include information that the user enters, such as names, addresses,
passwords,
credit card information, etc. Cookies can also perform computer functions. For
example,
authentication cookies can be used by applications (for example, a web
browser) to identify
whether the user is already logged in (for example, to a web site). The cookie
data can be
encrypted to provide security for the consumer. Tracking cookies can be used
to compile
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historical browsing histories of individuals. Systems disclosed herein can
generate and use
cookies to access data of an individual. Systems can also generate and use
JSON web
tokens to store authenticity information, HTTP authentication as
authentication protocols,
IP addresses to track session or identity information, URLs, and the like.
Example Embodiments
104991
The following are non-limiting examples of certain embodiments of
systems and methods of characterizing coronary plaque. Other embodiments may
include
one or more other features, or different features, that are discussed herein.
[0500]
Embodiment 1: A computer-implemented method of quantifying and
classifying coronary plaque within a coronary region of a subject based on non-
invasive
medical image analysis, the method comprising: accessing, by a computer
system, a
medical image of a coronary region of a subject, wherein the medical image of
the coronary
region of the subject is obtained non-invasively; identifying, by the computer
system
utilizing a coronary artery identification algorithm, one or more coronary
arteries within
the medical image of the coronary region of the subject, wherein the coronary
artery
identification algorithm is configured to utilize raw medical images as input;
identifying,
by the computer system utilizing a plaque identification algorithm, one or
more regions of
plaque within the one or more coronary arteries identified from the medical
image of the
coronary region of the subject, wherein the plaque identification algorithm is
configured to
utilize raw medical images as input; determining, by the computer system, one
or more
vascular morphology parameters and a set of quantified plaque parameters of
the one or
more identified regions of plaque from the medical image of the coronary
region of the
subject, wherein the set of quantified plaque parameters comprises a ratio or
function of
volume to surface area, heterogeneity index, geometry, and radiodensity of the
one or more
regions of plaque within the medical image; generating, by the computer
system, a
weighted measure of the determined one or more vascular morphology parameters
and the
set of quantified plaque parameters of the one or more regions of plaque; and
classifying,
by the computer system, the one or more regions of plaque within the medical
image as
stable plaque or unstable plaque based at least in part on the generated
weighted measure
of the determined one or more vascular morphology parameters and the
determined set of
quantified plaque parameters, wherein the computer system comprises a computer
processor and an electronic storage medium.
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[0501]
Embodiment 2: The computer-implemented method of Embodiment 1,
wherein one or more of the coronary artery identification algorithm or the
plaque
identification algorithm comprises an artificial intelligence or machine
learning algorithm.
[0502]
Embodiment 3: The computer-implemented method of any one of
Embodiment 1 or 2, wherein the plaque identification algorithm is configured
to determine
the one or more regions of plaque by determining a vessel wall and lumen wall
of the one
or more coronary arteries and determining a volume between the vessel wall and
lumen
wall as the one or more regions of plaque.
[0503]
Embodiment 4: The computer-implemented method of any one of
Embodiments 1-3, wherein the one or more coronary arteries are identified by
size.
[0504]
Embodiment 5: The computer-implemented method of any one of
Embodiments 1-4, wherein a ratio of volume to surface area of the one or more
regions of
plaque below a predetermined threshold is indicative of stable plaque.
[0505]
Embodiment 6: The computer-implemented method of any one of
Embodiments 1-5, wherein a radiodensity of the one or more regions of plaque
above a
predetermined threshold is indicative of stable plaque.
[0506]
Embodiment 7: The computer-implemented method of any one of
Embodiments 1-6, wherein a heterogeneity of the one or more regions of plaque
below a
predetermined threshold is indicative of stable plaque.
[0507]
Embodiment 8: The computer-implemented method of any one of
Embodiments 1-7, wherein the set of quantified plaque parameters further
comprises
diffusivity of the one or more regions of plaque.
[0508]
Embodiment 9: The computer-implemented method of any one of
Embodiments 1-8, wherein the set of quantified plaque parameters further
comprises a ratio
of radiodensity to volume of the one or more regions of plaque.
[0509]
Embodiment 10: The computer-implemented method of any one of
Embodiments 1-9, further comprising generating, by the computer system, a
proposed
treatment for the subject based at least in part on the classified one or more
regions of
plaque.
[0510]
Embodiment 11: The computer-implemented method of any one of
Embodiments 1-10, further comprising generating, by the computer system, an
assessment
of the subject for one or more of atherosclerosis, stenosis, or ischemia based
at least in part
on the classified one or more regions of plaque.
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[0511]
Embodiment 12: The computer-implemented method of any one of
Embodiments 1-11, wherein the medical image comprises a Computed Tomography
(CT)
image.
[0512]
Embodiment 13: The computer-implemented method of Embodiment
12, wherein the medical image comprises a non-contrast CT image.
[0513]
Embodiment 14: The computer-implemented method of Embodiment
12, wherein the medical image comprises a contrast-enhanced CT image.
[0514]
Embodiment 15: The computer-implemented method of any one of
Embodiments 1-11, wherein the medical image comprises a Magnetic Resonance
(MR)
image.
[0515]
Embodiment 16: The computer-implemented method of any one of
Embodiments 1-11, wherein the medical image is obtained using an imaging
technique
comprising one or more of CT, x-ray, ultrasound, echocardiography,
intravascular
ultrasound (IV U S), MR imaging, optical coherence tomography (OCT), nuclear
medicine
imaging, positron-emission tomography (PET), single photon emission computed
tomography (SPECT), or near-field infrared spectroscopy (NIRS).
[0516]
Embodiment 17: The computer-implemented method of any one of
Embodiments 1-16, wherein the heterogeneity index of one or more regions of
plaque is
determined by generating a three-dimensional histogram of radiodensity values
across a
Geometric shape of the one or more regions of plaque.
[0517]
Embodiment 18: The computer-implemented method of any one of
Embodiments 1-17, wherein the heterogeneity index of one or more regions of
plaque is
determined by generating spatial mapping of radiodensity values across the one
or more
regions of plaque.
[0518]
Embodiment 19: The computer-implemented method of any one of
Embodiments 1-18, wherein the set of quantified plaque parameters comprises a
percentage
composition of plaque comprising different radiodensity values.
[0519]
Embodiment 20: The computer-implemented method of any one of
Embodiments 1-19, wherein the set of quantified plaque parameters comprises a
percentage
composition of plaque comprising different radiodensity values as a function
of volume of
plaque.
[0520]
Embodiment 21: The computer-implemented method of any one of
Embodiments 1-20, wherein the geometry of the one or more regions of plaque
comprises
a round or oblong shape.
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[0521]
Embodiment 22: The computer-implemented method of any one of
Embodiments 1-21, wherein the one or more vascular morphology parameters
comprises a
classification of arterial remodeling.
[0522]
Embodiment 23: The computer-implemented method of Embodiment
22, wherein the classification of arterial remodeling comprises positive
arterial remodeling,
negative arterial remodeling, and intermediate arterial remodeling.
[0523]
Embodiment 24: The computer-implemented method of Embodiment
22, wherein the classification of arterial remodeling is determined based at
least in part on
a ratio of a largest vessel diameter at the one or more regions of plaque to a
normal reference
vessel diameter.
[0524]
Embodiment 25: The computer-implemented method of Embodiment
23, wherein the classification of arterial remodeling comprises positive
arterial remodeling,
negative arterial remodeling, and intermediate arterial remodeling, and
wherein positive
arterial remodeling is determined when the ratio of the largest vessel
diameter at the one or
more regions of plaque to the normal reference vessel diameter is more than
1.1, wherein
negative arterial remodeling is determined when the ratio of the largest
vessel diameter at
the one or more regions of plaque to the normal reference vessel diameter is
less than 0.95,
and wherein intermediate arterial remodeling is determined when the ratio of
the largest
vessel diameter at the one or more regions of plaque to the normal reference
vessel diameter
is between 0.95 and 1.1.
[0525]
Embodiment 26: The computer-implemented method of any one of
Embodiments 1-25, wherein the function of volume to surface area of the one or
more
regions of plaque comprises one or more of a thickness or diameter of the one
or more
regions of plaque.
[0526]
Embodiment 27: The computer-implemented method of any one of
Embodiments 1-26, wherein the weighted measure is generated by weighting the
one or
more vascular morphology parameters and the set of quantified plaque
parameters of the
one or more regions of plaque equally.
[0527]
Embodiment 28: The computer-implemented method of any one of
Embodiments 1-26, wherein the weighted measure is generated by weighting the
one or
more vascular morphology parameters and the set of quantified plaque
parameters of the
one or more regions of plaque differently.
[0528]
Embodiment 29: The computer-implemented method of any one of
Embodiments 1-26, wherein the weighted measure is generated by weighting the
one or
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more vascular morphology parameters and the set of quantified plaque
parameters of the
one or more regions of plaque logarithmically, algebraically, or utilizing
another
mathematical transform.
[0529]
Embodiment 30: A computer-implemented method of quantifying and
classifying vascular plaque based on non-invasive medical image analysis, the
method
comprising: accessing, by a computer system, a medical image of a subject,
wherein the
medical image of the subject is obtained non-invasively; identifying, by the
computer
system utilizing an artery identification algorithm, one or more arteries
within the medical
image of the subject, wherein the artery identification algorithm is
configured to utilize raw
medical images as input; identifying, by the computer system utilizing a
plaque
identification algorithm, one or more regions of plaque within the one or more
arteries
identified from the medical image of the subject, wherein the plaque
identification
algorithm is configured to utilize raw medical images as input; determining,
by the
computer system, one or more vascular morphology parameters and a set of
quantified
plaque parameters of the one or more identified regions of plaque from the
medical image
of the subject, wherein the set of quantified plaque parameters comprises a
ratio or function
of volume to surface area, heterogeneity index, geometry, and radiodensity of
the one or
more regions of plaque from the medical image; generating, by the computer
system, a
weighted measure of the determined one or more vascular morphology parameters
and the
set of quantified plaque parameters of the one or more regions of plaque; and
classifying,
by the computer system, the one or more regions of plaque within the medical
image as
stable plaque or unstable plaque based at least in part on the generated
weighted measure
of the determined one or more vascular morphology and the determined set of
quantified
plaque parameters, wherein the computer system comprises a computer processor
and an
electronic storage medium.
[0530]
Embodiment 31: The computer-implemented method of Embodiment
30, wherein the identified one or more arteries comprise one or more of
carotid arteries,
aorta, renal artery, lower extremity artery, or cerebral artery.
[0531]
Embodiment 32: A computer-implemented method of determining non-
calcified plaque from a non-contrast Computed Tomography (CT) image, the
method
comprising: accessing, by a computer system, anon-contrast CT image of a
coronary region
of a subj ect; identifying, by the computer system, epicardial fat on the non-
contrast CT
image; segmenting, by the computer system, arteries on the non-contrast CT
image using
the identified epicardial fat as outer boundaries of the arteries;
identifying, by the computer
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system, a first set of pixels within the arteries on the non-contrast CT image
comprising a
Hounsfield unit radiodensity value below a predetermined radiodensity
threshold;
classifying, by the computer system, the first set of pixels as a first subset
of non-calcified
plaque; identifying, by the computer system, a second set of pixels within the
arteries on
the non-contrast CT image comprising a Hounsfield unit radiodensity value
within a
predetermined radiodensity range; determining, by the computer system, a
heterogeneity
index of the second set of pixels and identifying a subset of the second set
of pixels
comprising a heterogeneity index above a heterogeneity index threshold;
classifying, by
the computer system, the subset of the second set of pixels as a second subset
of non-
calcified plaque; and determining, by the computer system, non-calcified
plaque from the
non-contrast CT image by combining the first subset of non-calcified plaque
and the second
subset of non-calcified plaque, wherein the computer system comprises a
computer
processor and an electronic storage medium.
105321
Embodiment 33: The computer-implemented method of Embodiment
32, wherein the predetermined radiodensity threshold comprises a Hounsfield
unit
radiodensity value of 30.
[0533]
Embodiment 34: The computer-implemented method of any one of
Embodiments 32-33, wherein the predetermined radiodensity range comprises
Hounsfield
unit radiodensity values between 30 and 100.
[0534]
Embodiment 35: The computer-implemented method of any one of
Embodiments 32-34, wherein identifying epicardial fat on the non-contrast CT
image
further comprises: determining a Hounsfield unit radiodensity value of each
pixel within
the non-contrast CT image; and classifying as epicardial fat pixels within the
non-contrast
CT image with a Hounsfield unit radiodensity value within a predetermined
epicardial fat
radiodensity range, wherein the predetermined epicardial fat radiodensity
range comprises
a Hounsfield unit radiodensity value of -100.
[0535]
Embodiment 36: The computer-implemented method of any one of
Embodiments 32-35, wherein the heterogeneity index of the second set of pixels
is
determined by generating spatial mapping of radiodensity values of the second
set of pixels.
[0536]
Embodiment 37: The computer-implemented method of any one of
Embodiments 32-36, wherein the heterogeneity index of the second set of pixels
is
determined by generating a three-dimensional histogram of radiodensity values
across a
geometric region within the second set of pixels.
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[0537]
Embodiment 38: The computer-implemented method of any one of
Embodiments 32-37, further comprising classifying, by the computer system, a
subset of
the second set of pixels comprising a heterogeneity index below the
heterogeneity index
threshold as blood.
105381
Embodiment 39: The computer-implemented method of any one of
Embodiments 32-38, further comprising generating a quantized color map of the
coronary
region of the subject by assigning a first color to the identified epicardial
fat, assigning a
second color to the segmented arteries, and assigning a third color to the
determined non-
calcified plaque.
105391
Embodiment 40: The computer-implemented method of any one of
Embodiments 32-39, further comprising: identifying, by the computer system, a
third set
of pixels within the arteries on the non-contrast CT image comprising a
Hounsfield unit
radiodensity value above a predetermined calcified radiodensity threshold; and
classifying,
by the computer system, the third set of pixels as calcified plaque.
[0540]
Embodiment 41: The computer-implemented method of any one of
Embodiments 32-40, further comprising determining, by the computer system, a
proposed
treatment based at least in part on the determined non-calcified plaque.
[0541]
Embodiment 42: A computer-implemented method of determining low-
attenuated plaque from a medical image of a subject, the method comprising:
accessing, by
a computer system, a medical image of a subject; identifying, by the computer
system,
epicardial fat on the medical image of the subject by: determining a
radiodensity value of
each pixel within the medical image of the subject; and classifying as
epicardial fat pixels
within the medical image of the subject with a radiodensity value within a
predetermined
epicardial fat radiodensity range; segmenting, by the computer system,
arteries on the
medical image of the subject using the identified epicardial fat as outer
boundaries of the
arteries; identifying, by the computer system, a first set of pixels within
the arteries on the
medical image of the subject comprising a radiodensity value below a
predetermined
radiodensity threshold; classifying, by the computer system, the first set of
pixels as a first
subset of low-attenuated plaque; identifying, by the computer system, a second
set of pixels
within the arteries on the non-contrast CT image comprising a radiodensity
value within a
predetermined radiodensity range; determining, by the computer system, a
heterogeneity
index of the second set of pixels and identifying a subset of the second set
of pixels
comprising a heterogeneity index above a heterogeneity index threshold;
classifying, by
the computer system, the subset of the second set of pixels as a second subset
of low-
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attenuated plaque; and determining, by the computer system, low-attenuated
plaque from
the medical image of the subject by combining the first subset of low-
attenuated plaque
and the second subset of low-attenuated plaque, wherein the computer system
comprises a
computer processor and an electronic storage medium.
[0542]
Embodiment 43: The computer-implemented method of Embodiment
42, wherein the medical image comprises a Computed Tomography (CT) image.
[0543]
Embodiment 44: The computer-implemented method of Embodiment
42, wherein the medical image comprises a Magnetic Resonance (MR) image.
[0544]
Embodiment 45: The computer-implemented method of Embodiment
42, wherein the medical image comprises an ultrasound image.
[0545]
Embodiment 46: The computer-implemented method of any one of
Embodiments 42-45, wherein the medical image comprises an image of a coronary
region
of the subject.
[0546]
Embodiment 47: The computer-implemented method of any one of
Embodiments 42-46, further comprising determining, by the computer system, a
proposed
treatment for a disease based at least in part on the determined low-
attenuated plaque.
[0547]
Embodiment 48: The computer-implemented method of Embodiment
47, wherein the disease comprises one or more of arterial disease, renal
artery disease,
abdominal atherosclerosis, or carotid atherosclerosis.
[0548]
Embodiment 49: The computer-implemented method of any one of
Embodiments 42-48, wherein the heterogeneity index of the second set of pixels
is
determined by generating spatial mapping of radiodensity values of the second
set of pixels.
[0549]
Embodiment 50: A computer-implemented method of determining non-
calcified plaque from a Dual-Energy Computed Tomography (DECT) image or
spectral
Computed Tomography (CT) image, the method comprising: accessing, by a
computer
system, a DECT or spectral CT image of a coronary region of a subject;
identifying, by the
computer system, epicardial fat on the DECT image or spectral CT; segmenting,
by the
computer system, arteries on the DECT image or spectral CT; identifying, by
the computer
system, a first set of pixels within the arteries on the DECT or spectral CT
image
comprising a Hounsfield unit radiodensity value below a predetermined
radiodensity
threshold; classifying, by the computer system, the first set of pixels as a
first subset of non-
calcified plaque; identifying, by the computer system, a second set of pixels
within the
arteries on the DECT or spectral CT image comprising a Hounsfield unit
radiodensity value
within a predetermined radiodensity range; classifying, by the computer
system, a subset
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of the second set of pixels as a second subset of non-calcified plaque; and
determining, by
the computer system, non-calcified plaque from the DECT image or spectral CT
by
combining the first subset of non-calcified plaque and the second subset of
non-calcified
plaque, wherein the computer system comprises a computer processor and an
electronic
storage medium.
[0550]
Embodiment 51: The computer-implemented method of Embodiment
50, wherein the subset of the second set of pixels is identified by
determining, by the
computer system, a heterogeneity index of the second set of pixels and
identifying the
subset of the second set of pixels comprising a heterogeneity index above a
heterogeneity
index threshold.
[0551]
Embodiment 52: A computer-implemented method of assessing risk of
a cardiovascular event for a subject based on non-invasive medical image
analysis, the
method comprising: accessing, by a computer system, a medical image of a
coronary region
of a subject. wherein the medical image of the coronary region of the subject
is obtained
non-invasively; identifying, by the computer system utilizing a coronary
artery
identification algorithm, one or more coronary arteries within the medical
image of the
coronary region of the subject, wherein the coronary artery identification
algorithm is
configured to utilize raw medical images as input; identifying, by the
computer system
utilizing a plaque identification algorithm, one or more regions of plaque
within the one or
more coronary arteries identified from the medical image of the coronary
region of the
subject, wherein the plaque identification algorithm is configured to utilize
raw medical
images as input; determining, by the computer system, one or more vascular
morphology
parameters and a set of quantified plaque parameters of the one or more
identified regions
of plaque from the medical image of the coronary region of the subject,
wherein the set of
quantified plaque parameters comprises a ratio or function of volume to
surface area,
heterogeneity index, geometry, and radiodensity of the one or more regions of
plaque
within the medical image; generating, by the computer system, a weighted
measure of the
determined one or more vascular morphology parameters and the set of
quantified plaque
parameters of the one or more regions of plaque; classifying, by the computer
system, the
one or more regions of plaque within the medical image as stable plaque or
unstable plaque
based at least in part on the generated weighted measure of the determined one
or more
vascular morphology parameters and the determined set of quantified plaque
parameters;
generating, by the computer system, a risk of cardiovascular event for the
subject based at
least in part on the one or more regions of plaque classified as stable plaque
or unstable
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plaque; accessing, by the computer system, a coronary values database
comprising one or
more known datasets of coronary values derived from one or more other subjects
and
comparing the one or more regions of plaque classified as stable plaque or
unstable plaque
to the one or more known datasets of coronary values; updating, by the
computer system,
the generated risk of cardiovascular event for the subject based at least in
part on the
comparison of the one or more regions of plaque classified as stable plaque or
unstable
plaque to the one or more known datasets of coronary values; and generating,
by the
computer system, a proposed treatment for the subject based at least in part
on the
comparison of the one or more regions of plaque classified as stable plaque or
unstable
plaque to the one or more known datasets of coronary values, wherein the
computer system
comprises a computer processor and an electronic storage medium.
[0552]
Embodiment 53: The computer-implemented method of Embodiment
52, wherein the cardiovascular event comprises one or more of a Major Adverse
Cardiovascular Event (MACE), rapid plaque progression, or non-response to
medication.
[0553]
Embodiment 54: The computer-implemented method of any one of
Embodiments 52-53, wherein the one or more known datasets of coronary values
comprises
one or more parameters of stable plaque and unstable plaque derived from
medical images
of healthy subj ects.
[0554]
Embodiment 55: The computer-implemented method of any one of
Embodiments 52-54, wherein the one or more other subjects are healthy.
[0555]
Embodiment 56: The computer-implemented method of any one of
Embodiments 52-55, wherein the one or more other subjects have a heightened
risk of a
cardiovascular event.
105561
Embodiment 57: The computer-implemented method of any one of
Embodiments 52-57, further comprising: identifying, by the computer system,
one or more
additional cardiovascular structures within the medical image, wherein the one
or more
additional cardiovascular structures comprise one or more of the left
ventricle, right
ventricle, left atrium, right atrium, aortic valve, mitral valve, tricuspid
valve, pulmonic
valve, aorta, pulmonary artery, inferior and superior vena cava, epicardial
fat, or
pericardium; determining, by the computer system, one or more parameters
associated with
the identified one or more additional cardiovascular structures; classifying,
by the computer
system, the one or more additional cardiovascular structures based at least in
part on the
determined one or more parameters; accessing, by the computer system, a
cardiovascular
structures values database comprising one or more known datasets of
cardiovascular
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structures parameters derived from medical images of one or more other
subjects and
comparing the classified one or more additional cardiovascular structures to
the one or more
known datasets of cardiovascular structures parameters; and updating, by the
computer
system, the generated risk of cardiovascular event for the subj ect based at
least in part on
the comparison of the classified one or more additional cardiovascular
structures to the one
or more known datasets of cardiovascular structures parameters.
[0557]
Embodiment 58: The computer-implemented method of Embodiment
57, wherein the one or more additional cardiovascular structures are
classified as normal or
abnormal.
105581
Embodiment 59: The computer-implemented method of Embodiment
57, wherein the one or more additional cardiovascular structures are
classified as increased
or decreased.
[0559]
Embodiment 60: The computer-implemented method of Embodiment
57, wherein the one or more additional cardiovascular structures are
classified as static or
dynamic over time.
[0560]
Embodiment 61: The computer-implemented method of any one of
Embodiments 57-60, further comprising generating, by the computer system, a
quantized
color map for the additional cardiovascular structures.
[0561]
Embodiment 62: The computer-implemented method of any one of
Embodiments 57-61, further comprising updating, by the computer system, the
proposed
treatment for the subject based at least in part on the comparison of the
classified one or
more additional cardiovascular structures to the one or more known datasets of
cardiovascular structures parameters.
105621
Embodiment 63: The computer-implemented method of any one of
Embodiments 57-62, further comprising: identifying, by the computer system,
one or more
non-cardiovascular structures within the medical image, wherein the one or
more non-
cardiovascular structures comprise one or more of the lungs, bones, or liver;
determining,
by the computer system, one or more parameters associated with the identified
one or more
non-cardiovascular structures; classifying, by the computer system, the one or
more non-
cardiovascular structures based at least in part on the determined one or more
parameters;
accessing, by the computer system, a non-cardiovascular structures values
database
comprising one or more known datasets of non-cardiovascular structures
parameters
derived from medical images of one or more other subjects and comparing the
classified
one or more non-cardiovascular structures to the one or more known datasets of
non-
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cardiovascular structures parameters; and updating, by the computer system,
the generated
risk of cardiovascular event for the subject based at least in part on the
comparison of the
classified one or more non-cardiovascular structures to the one or more known
datasets of
non-cardiovascular structures parameters.
[0563]
Embodiment 64: The computer-implemented method of Embodiment
63, wherein the one or more non-cardiovascular structures are classified as
normal or
abnormal.
[0564]
Embodiment 65: The computer-implemented method of Embodiment
63, wherein the one or more non-cardiovascular structures are classified as
increased or
decreased.
[0565]
Embodiment 66: The computer-implemented method of Embodiment
63, wherein the one or more non-cardiovascular structures are classified as
static or
dynamic over time.
[0566]
Embodiment 67: The computer-implemented method of any one of
Embodiments 63-66, further comprising generating, by the computer system, a
quantized
color map for the non-cardiovascular structures.
[0567]
Embodiment 68: The computer-implemented method of any one of
Embodiments 63-67, further comprising updating, by the computer system, the
proposed
treatment for the subject based at least in part on the comparison of the
classified one or
more non-cardiovascular structures to the one or more known datasets of non-
cardiovascular structures parameters.
[0568]
Embodiment 69: The computer-implemented method of any one of
Embodiments 63-68, wherein the one or more parameters associated with the
identified one
or more non-cardiovascular structures comprises one or more of ratio of volume
to surface
area, heterogeneity, radiodensity, or geometry of the identified one or more
non-
cardiovascular structures.
[0569]
Embodiment 70: The computer-implemented method of any one of
Embodiments 52-69, wherein the medical image comprises a Computed Tomography
(CT)
image.
[0570]
Embodiment 71: The computer-implemented method of any one of
Embodiments 52-69, wherein the medical image comprises a Magnetic Resonance
(MR)
image.
[0571]
Embodiment 72: A computer-implemented method of quantifying and
classifying coronary atherosclerosis within a coronary region of a subject
based on non-
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invasive medical image analysis, the method comprising: accessing, by a
computer system,
a medical image of a coronary region of a subject, wherein the medical image
of the
coronary region of the subject is obtained non-invasively; identifying, by the
computer
system utilizing a coronary artery identification algorithm, one or more
coronary arteries
within the medical image of the coronary region of the subject, wherein the
coronary artery
identification algorithm is configured to utilize raw medical images as input;
identifying,
by the computer system utilizing a plaque identification algorithm, one or
more regions of
plaque within the one or more coronary arteries identified from the medical
image of the
coronary region of the subject, wherein the plaque identification algorithm is
configured to
utilize raw medical images as input; determining, by the computer system, one
or more
vascular morphology parameters and a set of quantified plaque parameters of
the one or
more identified regions of plaque from the medical image of the coronary
region of the
subject, wherein the set of quantified plaque parameters comprises a ratio or
function of
volume to surface area, heterogeneity index, geometry, and radiodensity of the
one or more
regions of plaque within the medical image; generating, by the computer
system, a
weighted measure of the determined one or more vascular morphology parameters
and the
set of quantified plaque parameters of the one or more regions of plaque;
quantifying, by
the computer system, coronary atherosclerosis of the subject based at least in
part on the
set of generated weighted measure of the determined one or more vascular
morphology
parameters and the determined quantified plaque parameters; and classifying,
by the
computer system, coronary atherosclerosis of the subject as one or more of
high risk,
medium risk, or low risk based at least in part on the quantified coronary
atherosclerosis of
the subject, wherein the computer system comprises a computer processor and an
electronic
storage medium.
[0572]
Embodiment 73: The computer-implemented method of Embodiment
72, wherein one or more of the coronary artery identification algorithm or the
plaque
identification algorithm comprises an artificial intelligence or machine
learning algorithm.
[0573]
Embodiment 74: The computer-implemented method of any one of
Embodiments 72 or 73, further comprising determining a numerical calculation
of coronary
stenosis of the subject based at least in part on the one or more vascular
morphology
parameters and/or set of quantified plaque parameters determined from the
medical image
of the coronary region of the subject.
[0574]
Embodiment 75: The computer-implemented method of any one of
Embodiment 72-74, further comprising assessing a risk of ischemia for the
subject based
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at least in part on the one or more vascular morphology parameters and/or set
of quantified
plaque parameters determined from the medical image of the coronary region of
the subject.
[0575]
Embodiment 76: The computer-implemented method of any one of
Embodiments 72-75, wherein the plaque identification algorithm is configured
to
determine the one or more regions of plaque by determining a vessel wall and
lumen wall
of the one or more coronary arteries and determining a volume between the
vessel wall and
lumen wall as the one or more regions of plaque.
[0576]
Embodiment 77: The computer-implemented method of any one of
Embodiments 72-76, wherein the one or more coronary arteries are identified by
size.
105771
Embodiment 78: The computer-implemented method of any one of
Embodiments 72-77, wherein a ratio of volume to surface area of the one or
more regions
of plaque below a predetermined threshold is indicative of low risk.
[0578]
Embodiment 79: The computer-implemented method of any one of
Embodiments 72-78, wherein a radiodensity of the one or more regions of plaque
above a
predetermined threshold is indicative of low risk.
[0579]
Embodiment 80: The computer-implemented method of any one of
Embodiments 72-79, wherein a heterogeneity of the one or more regions of
plaque below
a predetermined threshold is indicative of low risk.
[0580]
Embodiment 81: The computer-implemented method of any one of
Embodiments 72-80, wherein the set of quantified plaque parameters further
comprises
diffusivity of the one or more regions of plaque.
[0581]
Embodiment 82: The computer-implemented method of any one of
Embodiments 72-81, wherein the set of quantified plaque parameters further
comprises a
ratio of radiodensity to volume of the one or more regions of plaque.
[0582]
Embodiment 83: The computer-implemented method of any one of
Embodiments 72-82, further comprising generating, by the computer system, a
proposed
treatment for the subject based at least in part on the classified
atherosclerosis.
[0583]
Embodiment 84: The computer-implemented method of any one of
Embodiments 72-83, wherein the coronary atherosclerosis of the subject is
classified by
the computer system using a coronary atherosclerosis classification algorithm,
wherein the
coronary atherosclerosis classification algorithm is configured to utilize a
combination of
the ratio of volume of surface area, volume, heterogeneity index, and
radiodensity of the
one or more regions of plaque as input.
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[0584]
Embodiment 85: The computer-implemented method of any one of
Embodiments 72-84, wherein the medical image comprises a Computed Tomography
(CT)
image.
[0585]
Embodiment 86: The computer-implemented method of Embodiment
85, wherein the medical image comprises a non-contrast CT image.
[0586]
Embodiment 87: The computer-implemented method of Embodiment
85, wherein the medical image comprises a contrast CT image.
[0587]
Embodiment 88: The computer-implemented method of any one of
Embodiments 72-84, wherein the medical image is obtained using an imaging
technique
comprising one or more of CT, x-ray, ultrasound, echocardiography,
intravascular
ultrasound (IVUS), MR imaging, optical coherence tomography (OCT), nuclear
medicine
imaging, positron-emission tomography (PET), single photon emission computed
tomography (SPECT), or near-field infrared spectroscopy (NIRS).
[0588]
Embodiment 89: The computer-implemented method of any one of
Embodiments 72-88, wherein the heterogeneity index of one or more regions of
plaque is
determined by generating a three-dimensional histogram of radiodensity values
across a
geometric shape of the one or more regions of plaque.
[0589]
Embodiment 90: The computer-implemented method of any one of
Embodiments 72-89, wherein the heterogeneity index of one or more regions of
plaque is
determined by generating spatial mapping of radiodensity values across the one
or more
regions of plaque.
[0590]
Embodiment 91: The computer-implemented method of any one of
Embodiments 72-90, wherein the set of quantified plaque parameters comprises a
percentage composition of plaque comprising different radiodensity values.
[0591]
Embodiment 92: The computer-implemented method of any one of
Embodiments 72-91, wherein the set of quantified plaque parameters comprises a
percentage composition of plaque comprising different radiodensity values as a
function of
volume of plaque.
[0592]
Embodiment 93: The computer-implemented method of any one of
Embodiments 72-92, wherein the weighted measure of the determined one or more
vascular
morphology parameters and the set of quantified plaque parameters of the one
or more
regions of plaque is generated based at least in part by comparing the
determined set of
quantified plaque parameters to one or more predetermined sets of quantified
plaque
parameters.
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[0593]
Embodiment 94: The computer-implemented method of Embodiment
93, wherein the one or more predetermined sets of quantified plaque parameters
are derived
from one or more medical images of other subjects.
[0594]
Embodiment 95: The computer-implemented method of Embodiment
93, wherein the one or more predetermined sets of quantified plaque parameters
are derived
from one or more medical images of the subject.
[0595]
Embodiment 96: The computer-implemented method of any one of
Embodiments 72-95, wherein the geometry of the one or more regions of plaque
comprises
a round or oblong shape.
105961
Embodiment 97: The computer-implemented method of any one of
Embodiments 72-96, wherein the one or more vascular morphology parameters
comprises
a classification of arterial remodeling.
[0597]
Embodiment 98: The computer-implemented method of Embodiment
97, wherein the classification of arterial remodeling comprises positive
arterial remodeling,
negative arterial remodeling, and intermediate arterial remodeling.
105981
Embodiment 99: The computer-implemented method of Embodiment
97, wherein the classification of arterial remodeling is determined based at
least in part on
a ratio of a largest vessel diameter at the one or more regions of plaque to a
normal reference
vessel diameter.
[0599]
Embodiment 100: The computer-implemented method of Embodiment
99, wherein the classification of arterial remodeling comprises positive
arterial remodeling,
negative arterial remodeling, and intermediate arterial remodeling, and
wherein positive
arterial remodeling is determined when the ratio of the largest vessel
diameter at the one or
more regions of plaque to the normal reference vessel diameter is more than
1.1, wherein
negative arterial remodeling is determined when the ratio of the largest
vessel diameter at
the one or more regions of plaque to the normal reference vessel diameter is
less than 0.95,
and wherein intermediate arterial remodeling is determined when the ratio of
the largest
vessel diameter at the one or more regions of plaque to the normal reference
vessel diameter
is between 0.95 and 1.1.
[0600]
Embodiment 101: The computer-implemented method of any one of
Embodiments 72-100, wherein the function of volume to surface area of the one
or more
regions of plaque comprises one or more of a thickness or diameter of the one
or more
regions of plaque.
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[0601]
Embodiment 102: The computer-implemented method of any one of
Embodiments 72-101, wherein the weighted measure is generated by weighting the
one or
more vascular morphology parameters and the set of quantified plaque
parameters of the
one or more regions of plaque equally.
[0602]
Embodiment 103: The computer-implemented method of any one of
Embodiments 72-101, wherein the weighted measure is generated by weighting the
one or
more vascular morphology parameters and the set of quantified plaque
parameters of the
one or more regions of plaque differently.
[0603]
Embodiment 104: The computer-implemented method of any one of
Embodiments 72-101, wherein the weighted measure is generated by weighting the
one or
more vascular morphology parameters and the set of quantified plaque
parameters of the
one or more regions of plaque logarithmically, algebraically, or utilizing
another
mathematical transform.
[0604]
Embodiment 105: A computer-implemented method of quantifying a
state of coronary artery disease based on quantification of plaque, ischemia,
and fat
inflammation based on non-invasive medical image analysis, the method
comprising:
accessing, by a computer system, a medical image of a coronary region of a
subject,
wherein the medical image of the coronary region of the subject is obtained
non-invasively;
identifying, by the computer system utilizing a coronary artery identification
algorithm,
one or more coronary arteries within the medical image of the coronary region
of the
subject, wherein the coronary artery identification algorithm is configured to
utilize raw
medical images as input; identifying, by the computer system utilizing a
plaque
identification algorithm, one or more regions of plaque within the one or more
coronary
arteries identified from the medical image of the coronary region of the
subject, wherein
the plaque identification algorithm is configured to utilize raw medical
images as input;
identifying, by the computer system utilizing a fat identification algorithm,
one or more
regions of fat within the medical image of the coronary region of the subject,
wherein the
fat identification algorithm is configured to utilize raw medical images as
input;
determining, by the computer system, one or more vascular morphology
parameters and a
set of quantified plaque parameters of the one or more identified regions of
plaque from
the medical image of the coronary region of the subject, wherein the set of
quantified plaque
parameters comprises a ratio or function of volume to surface area,
heterogeneity index,
geometry, and radiodensity of the one or more regions of plaque within the
medical image;
quantifying, by the computer system, coronary stenosis based at least in part
on the set of
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quantified plaque parameters determined from the medical image of the coronary
region of
the subject; and determining, by the computer system, a presence or risk of
ischemia based
at least in part on the set of quantified plaque parameters determined from
the medical
image of the coronary region of the subject; determining, by the computer
system, a set of
quantified fat parameters of the one or more identified regions of fat within
the medical
image of the coronary region of the subject, wherein the set of quantified fat
parameters
comprises volume, geometry, and radiodensity of the one or more regions of fat
within the
medical image; generating, by the computer system, a weighted measure of the
determined
one or more vascular morphology parameters, the set of quantified plaque
parameters of
the one or more regions of plaque, the quantified coronary stenosis, the
determined
presence or risk of ischemia, and the determined set of quantified fat
parameters; and
generating, by the computer system, a risk assessment of coronary disease of
the subject
based at least in part on the generated weighted measure of the determined one
or more
vascular morphology parameters, the set of quantified plaque parameters of the
one or more
regions of plaque, the quantified coronary stenosis, the determined presence
or risk of
ischemia, and the determined set of quantified fat parameters, wherein the
computer system
comprises a computer processor and an electronic storage medium.
[0605]
Embodiment 106: The computer-implemented method of Embodiment
105, wherein one or more of the coronary artery identification algorithm,
plaque
identification algorithm, or fat identification algorithm comprises an
artificial intelligence
or machine learning algorithm.
[0606]
Embodiment 107: The computer-implemented method of any one of
Embodiment 105 or 106, further comprising automatically generating, by the
computer
system, a Coronary Artery Disease Reporting & Data System (CAD-RADS)
classification
score of the subject based at least in part on the quantified coronary
stenosis.
[0607]
Embodiment 108: The computer-implemented method of any one of
Embodiments 105-107, further comprising automatically generating, by the
computer
system, a CAD-RADS modifier of the subject based at least in part on one or
more of the
determined one or more vascular morphology parameters, the set of quantified
plaque
parameters of the one or more regions of plaque, the quantified coronary
stenosis, the
determined presence or risk of ischemia, and the determined set of quantified
fat
parameters, wherein the CAD-RADS modifier comprises one or more of
nondiagnostic
(N), stent (S), graft (G), or vulnerability (V).
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[0608]
Embodiment 109: The computer-implemented method of any one of
Embodiments 105-108, wherein the coronary stenosis is quantified on a vessel-
by-vessel
basis.
[0609]
Embodiment 110: The computer-implemented method of any one of
Embodiments 105-109, wherein the presence or risk of ischemia is determined on
a vessel-
by-vessel basis.
[0610]
Embodiment 111: The computer-implemented method of any one of
Embodiments 105-110, wherein the one or more regions of fat comprises
epicardial fat.
[0611]
Embodiment 112: The computer-implemented method of any one of
Embodiments 105-111, further comprising generating, by the computer system, a
proposed
treatment for the subject based at least in part on the generated risk
assessment of coronary
disease.
[0612]
Embodiment 113: The computer-implemented method of any one of
Embodiments 105-112, wherein the medical image comprises a Computed Tomography
(CT) image.
[0613]
Embodiment 114: The computer-implemented method of Embodiment
113, wherein the medical image comprises a non-contrast CT image.
[0614]
Embodiment 115: The computer-implemented method of Embodiment
113, wherein the medical image comprises a contrast CT image.
[0615]
Embodiment 116: The computer-implemented method of any one of
Embodiments 113-115, wherein the determined set of plaque parameters comprises
one or
more of a percentage of higher radiodensity calcium plaque or lower
radiodensity calcium
plaque within the one or more regions of plaque, wherein higher radiodensity
calcium
plaque comprises a Hounsfield radiodensity unit of above 1000, and wherein
lower
radiodensity calcium plaque comprises a Hounsfield radiodensity unit of below
1000.
[0616]
Embodiment 117: The computer-implemented method of any one of
Embodiments 105-112, wherein the medical image comprises a Magnetic Resonance
(MR)
image.
[0617]
Embodiment 118: The computer-implemented method of any one of
Embodiments 105-112, wherein the medical image comprises an ultrasound image.
[0618]
Embodiment 119: The computer-implemented method of any one of
Embodiments 105-112, wherein the medical image is obtained using an imaging
technique
comprising one or more of CT, x-ray, ultrasound, echocardiography,
intravascular
ultrasound (IVUS), MR imaging, optical coherence tomography (OCT), nuclear
medicine
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imaging, positron-emission tomography (PET), single photon emission computed
tomography (SPECT), or near-field infrared spectroscopy (NIRS).
[0619]
Embodiment 120: The computer-implemented method of any one of
Embodiments 105-119, wherein the heterogeneity index of one or more regions of
plaque
is determined by generating a three-dimensional histogram of radiodensity
values across a
geometric shape of the one or more regions of plaque.
[0620]
Embodiment 121: The computer-implemented method of any one of
Embodiments 105-119, wherein the heterogeneity index of one or more regions of
plaque
is determined by generating spatial mapping of radiodensity values across the
one or more
regions of plaque.
[0621]
Embodiment 122: The computer-implemented method of any one of
Embodiments 105-121, wherein the set of quantified plaque parameters comprises
a
percentage composition of plaque comprising different radiodensity values.
[0622]
Embodiment 123: The computer-implemented method of any one of
Embodiments 105-122, wherein the set of quantified plaque parameters further
comprises
diffusivity of the one or more regions of plaque.
[0623]
Embodiment 124: The computer-implemented method of any one of
Embodiments 105-123, wherein the set of quantified plaque parameters further
comprises
a ratio of radiodensity to volume of the one or more regions of plaque.
[0624]
Embodiment 125: The computer-implemented method of any one of
Embodiments 105-124, wherein the plaque identification algorithm is configured
to
determine the one or more regions of plaque by determining a vessel wall and
lumen wall
of the one or more coronary arteries and determining a volume between the
vessel wall and
lumen wall as the one or more regions of plaque.
[0625]
Embodiment 126: The computer-implemented method of any one of
Embodiments 105-125, wherein the one or more coronary arteries are identified
by size.
[0626]
Embodiment 127: The computer-implemented method of any one of
Embodiments 105-126, wherein the generated risk assessment of coronary disease
of the
subject comprises a risk score.
[0627]
Embodiment 128: The computer-implemented method of any one of
Embodiments 105-127, wherein the geometry of the one or more regions of plaque
comprises a round or oblong shape.
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[0628]
Embodiment 129: The computer-implemented method of any one of
Embodiments 105-128, wherein the one or more vascular morphology parameters
comprises a classification of arterial remodeling.
[0629]
Embodiment 130: The computer-implemented method of Embodiment
129, wherein the classification of arterial remodeling comprises positive
arterial
remodeling, negative arterial remodeling, and intermediate arterial
remodeling.
[0630]
Embodiment 131: The computer-implemented method of Embodiment
129, wherein the classification of arterial remodeling is determined based at
least in part on
a ratio of a largest vessel diameter at the one or more regions of plaque to a
normal reference
vessel diameter.
[0631]
Embodiment 132: The computer-implemented method of Embodiment
131, wherein the classification of arterial remodeling comprises positive
arterial
remodeling, negative arterial remodeling, and intermediate arterial
remodeling, and
wherein positive arterial remodeling is determined when the ratio of the
largest vessel
diameter at the one or more regions of plaque to the normal reference vessel
diameter is
more than 1.1, wherein negative arterial remodeling is determined when the
ratio of the
largest vessel diameter at the one or more regions of plaque to the normal
reference vessel
diameter is less than 0.95, and wherein intermediate arterial remodeling is
determined when
the ratio of the largest vessel diameter at the one or more regions of plaque
to the normal
reference vessel diameter is between 0.95 and 1.1.
[0632]
Embodiment 133: The computer-implemented method of any of
Embodiments 105-132, wherein the function of volume to surface area of the one
or more
regions of plaque comprises one or more of a thickness or diameter of the one
or more
regions of plaque.
[0633]
Embodiment 134: The computer-implemented method of any one of
Embodiments 105-133, wherein the weighted measure is generated by weighting
the one
or more vascular morphology parameters, the set of quantified plaque
parameters of the
one or more regions of plaque, the quantified coronary stenosis, the
determined presence
or risk of ischemia, and the determined set of quantified fat parameters
equally.
[0634]
Embodiment 135: The computer-implemented method of any one of
Embodiments 105-133, wherein the weighted measure is generated by weighting
the one
or more vascular morphology parameters, the set of quantified plaque
parameters of the
one or more regions of plaque, the quantified coronary stenosis, the
determined presence
or risk of ischemia, and the determined set of quantified fat parameters
differently.
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[0635]
Embodiment 136: The computer-implemented method of any one of
Embodiments 105-133, wherein the weighted measure is generated by weighting
the one
or more vascular morphology parameters, the set of quantified plaque
parameters of the
one or more regions of plaque, the quantified coronary stenosis, the
determined presence
or risk of ischemia, and the determined set of quantified fat parameters
logarithmically,
algebraically, or utilizing another mathematical transform.
[0636]
Embodiment 137: A computer-implemented method of tracking a
plaque-based disease based at least in part on determining a state of plaque
progression of
a subject using non-invasive medical image analysis, the method comprising:
accessing, by
a computer system, a first set of plaque parameters associated with a region
of a subject,
wherein the first set of plaque parameters are derived from a first medical
image of the
subject, wherein the first medical image of the subject is obtained non-
invasively at a first
point in time; accessing, by a computer system, a second medical image of the
subject,
wherein the second medical image of the subject is obtained non-invasively at
a second
point in time, the second point in time being later than the first point in
time; identifying,
by the computer system, one or more regions of plaque from the second medical
image;
determining, by the computer system, a second set of plaque parameters
associated with
the region of the subject by analyzing the second medical image and the
identified one or
more regions of plaque from the second medical image; analyzing, by the
computer system,
a change in one or more plaque parameters by comparing one or more of the
first set of
plaque parameters against one or more of the second set of plaque parameters;
determining,
by the computer system, a state of plaque progression associated with a plaque-
based
disease for the subject based at least in part on the analyzed change in the
one or more
plaque parameters, wherein the determined state of plaque progression
comprises one or
more of rapid plaque progression, non-rapid calcium dominant mixed response,
non-rapid
non-calcium dominant mixed response, or plaque regression; and tracking, by
the computer
system, progression of the plaque-based disease based at least in part on the
determined
state of plaque progression, wherein the computer system comprises a computer
processor
and an electronic storage medium.
[0637]
Embodiment 138: The computer-implemented method of Embodiment
137, wherein rapid plaque progression is determined when a percent atheroma
volume
increase of the subject is more than 1% per year, wherein non-rapid calcium
dominant
mixed response is determined when a percent atheroma volume increase of the
subject is
less than 1% per year and calcified plaque represents more than 50% of total
new plaque
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formation, wherein non-rapid non-calcium dominant mixed response is determined
when a
percent atheroma volume increase of the subject is less than 1% per year and
non-calcified
plaque represents more than 50% of total new plaque formation, and wherein
plaque
regression is determined when a decrease in total percent atheroma volume is
present.
[0638]
Embodiment 139: The computer-implemented method of any one of
Embodiments 137-138, further comprising generating, by the computer system, a
proposed
treatment for the subject based at least in part on the determined state of
plaque progression
of the plaque-based disease.
[0639]
Embodiment 140: The computer-implemented method of any one of
Embodiments 137-139, wherein the medical image comprises a Computed Tomography
(CT) image.
[0640]
Embodiment 141: The computer-implemented method of Embodiment
140, wherein the medical image comprises a non-contrast CT image.
[0641]
Embodiment 142: The computer-implemented method of Embodiment
140, wherein the medical image comprises a contrast CT image.
[0642]
Embodiment 143: The computer-implemented method of any one of
Embodiments 140-142, wherein the determined state of plaque progression
further
comprises one or more of a percentage of higher radiodensity plaques or lower
radiodensity
plaques, wherein higher radiodensity plaques comprise a Hounsfield unit of
above 1000,
and wherein lower radiodensity plaques comprise a Hounsfield unit of below
1000.
[0643]
Embodiment 144: The computer-implemented method of any one of
Embodiments 137-139, wherein the medical image comprises a Magnetic Resonance
(MR)
image.
106441
Embodiment 145: The computer-implemented method of any one of
Embodiments 137-139, wherein the medical image comprises an ultrasound image.
[0645]
Embodiment 146: The computer-implemented method of any one of
Embodiments 137-145, wherein the region of the subject comprises a coronary
region of
the subject.
[0646]
Embodiment 147: The computer-implemented method of any one of
Embodiments 137-145, wherein the region of the subject comprises one or more
of carotid
arteries, renal arteries, abdominal aorta, cerebral arteries, lower
extremities, or upper
extremities.
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[0647]
Embodiment 148: The computer-implemented method of any one of
Embodiments 137-147, wherein the plaque-based disease comprises one or more of
atherosclerosis, stenosis, or ischemia.
[0648]
Embodiment 149: The computer-implemented method of any one of
Embodiments 137-148, further comprising: determining, by the computer system,
a first
Coronary Artery Disease Reporting & Data System (CAD-RADS) classification
score of
the subject based at least in part on the first set of plaque parameters;
determining, by the
computer system, a second CAD-RADS classification score of the subject based
at least in
part on the second set of plaque parameters; and tracking, by the computer
system,
progression of a CAD-RADS classification score of the subject based on
comparing the
first CAD-RADS classification score and the second CAD-RADS classification
score.
[0649]
Embodiment 150: The computer-implemented method of any one of
Embodiments 137-149, wherein the plaque-based disease is further tracked by
the
computer system by analyzing one or more of serum biomarkers, genetics, omics,
transcriptomics, microbiomics, or metabolomics.
[0650]
Embodiment 151: The computer-implemented method of any one of
Embodiments 137-150, wherein the first set of plaque parameters comprises one
or more
of a volume, surface area, geometric shape, location, heterogeneity index, and
radiodensity
of one or more regions of plaque within the first medical image.
[0651]
Embodiment 152: The computer-implemented method of any one of
Embodiments 137-151, wherein the second set of plaque parameters comprises one
or more
of a volume, surface area, geometric shape, location, heterogeneity index, and
radiodensity
of one or more regions of plaque within the second medical image.
106521
Embodiment 153: The computer-implemented method of any one of
Embodiments 137-152, wherein the first set of plaque parameters and the second
set of
plaque parameters comprise a ratio of radiodensity to volume of one or more
regions of
plaque.
[0653]
Embodiment 154: The computer-implemented method of any one of
Embodiments 137-153, wherein the first set of plaque parameters and the second
set of
plaque parameters comprise a diffusivity of one or more regions of plaque.
[0654]
Embodiment 155: The computer-implemented method of any one of
Embodiments 137-154, wherein the first set of plaque parameters and the second
set of
plaque parameters comprise a volume to surface area ratio of one or more
regions of plaque.
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[0655]
Embodiment 156: The computer-implemented method of any one of
Embodiments 137-155, wherein the first set of plaque parameters and the second
set of
plaque parameters comprise a heterogeneity index of one or more regions of
plaque.
[0656]
Embodiment 157: The computer-implemented method of Embodiment
156, wherein the heterogeneity index of one or more regions of plaque is
determined by
generating a three-dimensional histogram of radiodensity values across a
geometric shape
of the one or more regions of plaque.
[0657]
Embodiment 158: The computer-implemented method of Embodiment
156, wherein the heterogeneity index of one or more regions of plaque is
determined by
generating spatial mapping of radiodensity values across the one or more
regions of plaque.
[0658]
Embodiment 159: The computer-implemented method of any one of
Embodiments 137-158, wherein the first set of plaque parameters and the second
set of
plaque parameters comprise a percentage composition of plaque comprising
different
radio density values.
[0659]
Embodiment 160: The computer-implemented method of any one of
Embodiments 137-159, wherein the first set of plaque parameters and the second
set of
plaque parameters comprise a percentage composition of plaque comprising
different
radiodensity values as a function of volume of plaque.
[0660]
Embodiment 161: A computer-implemented method of characterizing a
change in coronary calcium score of a subject, the method comprising:
accessing, by the
computer system, a first coronary calcium score of a subject and a first set
of plaque
parameters associated with a coronary region of a subject, the first coronary
calcium score
and the first set of parameters obtained at a first point in time, wherein the
first set of plaque
parameters comprises volume, surface area, geometric shape, location,
heterogeneity index,
and radiodensity for one or more regions of plaque within the coronary region
of the
subject; generating, by the computer system, a first weighted measure of the
accessed first
set of plaque parameters; accessing, by a computer system, a second coronary
calcium score
of the subj ect and one or more medical images of the coronary region of the
subject, the
second coronary calcium score and the one or more medical images obtained at a
second
point in time, the second point in time being later than the first point in
time, wherein the
one or more medical images of the coronary region of the subject comprises the
one or
more regions of plaque; determining, by the computer system, a change in
coronary calcium
score of the subj ect by comparing the first coronary calcium score and the
second coronary
calcium score; identifying, by the computer system, the one or more regions of
plaque from
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the one or more medical images; determining, by the computer system, a second
set of
plaque parameters associated with the coronary region of the subject by
analyzing the one
or more medical images, wherein the second set of plaque parameters comprises
volume,
surface area, geometric shape, location, heterogeneity index, and radiodensity
for the one
or more regions of plaque; generating, by the computer system, a second
weighted measure
of the determined second set of plaque parameters; analyzing, by the computer
system, a
change in the first weighted measure of the accessed first set of plaque
parameters and the
second weighted measure of the determined second set of plaque parameters; and
characterizing, by the computer system, the change in coronary calcium score
of the subject
based at least in part on the identified one or more regions of plaque and the
analyzed
change in the first weighted measure of the accessed first set of plaque
parameters and the
second weighted measure of the determined second set of plaque parameters,
wherein the
change in coronary calcium score is characterized as positive, neutral, or
negative, wherein
the computer system comprises a computer processor and an electronic storage
medium.
[0661]
Embodiment 162: The computer-implemented method of Embodiment
161, wherein radiodensity of the one or more regions of plaque is determined
from the one
or more medical images by analyzing a Hounsfield unit of the identified one or
more
regions of plaque.
[0662]
Embodiment 163: The computer-implemented method of any one of
Embodiments 161-162, further comprising determining a change in ratio between
volume
and radiodensity of the one or more regions of plaque within the coronary
region of the
subject, and wherein the change in coronary calcium score of the subject is
further
characterized based at least in part the determined change in ratio between
volume and
radiodensity of one or more regions of plaque within the coronary region of
the subject.
[0663]
Embodiment 164: The computer-implemented method of any one of
Embodiments 161-163, wherein the change in coronary calcium score of the
subject is
characterized for each vessel.
[0664]
Embodiment 165: The computer-implemented method of any one of
Embodiments 161-164, wherein the change in coronary calcium score of the
subject is
characterized for each segment.
[0665]
Embodiment 166: The computer-implemented method of any one of
Embodiments 161-165, wherein the change in coronary calcium score of the
subject is
characterized for each plaque.
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[0666]
Embodiment 167: The computer-implemented method of any one of
Embodiments 161-166, wherein the first set of plaque parameters and the second
set of
plaque parameters further comprise a diffusivity of the one or more regions of
plaque.
[0667]
Embodiment 168: The computer-implemented method of any one of
Embodiments 161-167, wherein the change in coronary calcium score of the
subject is
characterized as positive when the radiodensity of the one or more regions of
plaque is
increased.
[0668]
Embodiment 169: The computer-implemented method of any one of
Embodiments 161-168, wherein the change in coronary calcium score of the
subject is
characterized as negative when one or more new regions of plaque are
identified from the
one or more medical images.
[0669]
Embodiment 170: The computer-implemented method of any one of
Embodiments 161-169, wherein the change in coronary calcium score of the
subject is
characterized as positive when a volume to surface area ratio of the one or
more regions of
plaque is decreased.
[0670]
Embodiment 171: The computer-implemented method of any one of
Embodiments 161-170, wherein the heterogeneity index of the one or more
regions of
plaque is determined by generating a three-dimensional histogram of
radiodensity values
across a geometric shape of the one or more regions of plaque.
[0671]
Embodiment 172: The computer-implemented method of any one of
Embodiments 161-171, wherein the change in coronary calcium score of the
subject is
characterized as positive when the heterogeneity index of the one or more
regions of plaque
is decreased.
106721
Embodiment 173: The computer-implemented method of any one of
Embodiments 161-172, wherein the second coronary calcium score of the subject
is
determined by analyzing the one or more medical images of the coronary region
of the
subj ect.
[0673]
Embodiment 174: The computer-implemented method of any one of
Embodiments 161-172, wherein the second coronary calcium score of the subject
is
accessed from a database.
[0674]
Embodiment 175: The computer-implemented method of any one of
Embodiments 161-174, wherein the one or more medical images of the coronary
region of
the subject comprises an image obtained from a non-contrast Computed
Tomography (CT)
scan.
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[0675]
Embodiment 176: The computer-implemented method of any one of
Embodiments 161-174, wherein the one or more medical images of the coronary
region of
the subject comprises an image obtained from a contrast-enhanced CT scan.
[0676]
Embodiment 177: The computer-implemented method of Embodiment
176, wherein the one or more medical images of the coronary region of the
subject
comprises an image obtained from a contrast-enhanced CT angiogram.
[0677]
Embodiment 178: The computer-implemented method of any one of
Embodiments 161-177, wherein a positive characterization of the change in
coronary
calcium score is indicative of plaque stabilization.
106781
Embodiment 179: The computer-implemented method of any one of
Embodiments 161-178, wherein the first set of plaque parameters and the second
set of
plaque parameters further comprise radiodensity of a volume around plaque.
[0679]
Embodiment 180: The computer-implemented method of any one of
Embodiments 161-179, wherein the change in coronary calcium score of the
subject is
characterized by a machine learning algorithm utilized by the computer system.
[0680]
Embodiment 181: The computer-implemented method of any one of
Embodiments 161-180, wherein the first weighted measure is generated by
weighting the
accessed first set of plaque parameters equally.
[0681]
Embodiment 182: The computer-implemented method of any one of
Embodiments 161-180, wherein the first weighted measure is generated by
weighting the
accessed first set of plaque parameters differently_
[0682]
Embodiment 183: The computer-implemented method of any one of
Embodiments 161-180, wherein the first weighted measure is generated by
weighting the
accessed first set of plaque parameters logarithmically, algebraically, or
utilizing another
mathematical transform.
[0683]
Embodiment 184: A computer-implemented method of generating
prognosis of a cardiovascular event for a subject based on non-invasive
medical image
analysis. the method comprising: accessing, by a computer system, a medical
image of a
coronary region of a subject, wherein the medical image of the coronary region
of the
subject is obtained non-invasively; identifying, by the computer system
utilizing a coronary
artery identification algorithm, one or more coronary arteries within the
medical image of
the coronary region of the subject, wherein the coronary artery identification
algorithm is
configured to utilize raw medical images as input; identifying, by the
computer system
utilizing a plaque identification algorithm, one or more regions of plaque
within the one or
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more coronary arteries identified from the medical image of the coronary
region of the
subject, wherein the plaque identification algorithm is configured to utilize
raw medical
images as input; determining, by the computer system, a set of quantified
plaque parameters
of the one or more identified regions of plaque within the medical image of
the coronary
region of the subject, wherein the set of quantified plaque parameters
comprises volume,
surface area, ratio of volume to surface area, heterogeneity index, geometry,
and
radiodensity of the one or more regions of plaque within the medical image;
classifying, by
the computer system, the one or more regions of plaque within the medical
image as stable
plaque or unstable plaque based at least in part on the determined set of
quantified plaque
parameters; determining, by the computer system, a volume of unstable plaque
classified
within the medical image and a total volume of the one or more coronary
arteries within
the medical image; determining, by the computer system, a ratio of volume of
unstable
plaque to the total volume of the one or more coronary arteries; generating,
by the computer
system, a prognosis of a cardiovascular event for the subject based at least
in part on
analyzing the ratio of volume of unstable plaque to the total volume of the
one or more
coronary arteries, the volume of the one or more regions of plaque, and the
volume of
unstable plaque classified within the medical image, wherein the analyzing
comprises
conducting a comparison to a known dataset of one or more ratios of volume of
unstable
plaque to total volume of one or more coronary arteries, volume of one or more
regions of
plaque, and volume of unstable plaque, wherein the known dataset is collected
from other
subjects; and generating, by the computer system, treatment plan for the
subject based at
least in part on the generated prognosis of cardiovascular event for the
subject, wherein the
computer system comprises a computer processor and an electronic storage
medium.
106841
Embodiment 185: The computer-implemented method of Embodiment
184, further comprising generating, by the computer system, a weighted measure
of the
ratio of volume of unstable plaque to the total volume of the one or more
coronary arteries,
the volume of the one or more regions of plaque, and the volume of unstable
plaque
classified within the medical image, wherein the prognosis of cardiovascular
event is
further generated by comparing the weighted measure to one or more weighted
measures
derived from the known dataset.
[0685]
Embodiment 186: The computer-implemented method of Embodiment
185, wherein the weighted measure is generated by weighting the ratio of
volume of
unstable plaque to the total volume of the one or more coronary arteries, the
volume of the
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one or more regions of plaque, and the volume of unstable plaque classified
within the
medical image equally.
[0686]
Embodiment 187: The computer-implemented method of Embodiment
185, wherein the weighted measure is generated by weighting the ratio of
volume of
unstable plaque to the total volume of the one or more coronary arteries, the
volume of the
one or more regions of plaque, and the volume of unstable plaque classified
within the
medical image differently.
[0687]
Embodiment 188: The computer-implemented method of Embodiment
185, wherein the weighted measure is generated by weighting the ratio of
volume of
unstable plaque to the total volume of the one or more coronary arteries, the
volume of the
one or more regions of plaque, and the volume of unstable plaque classified
within the
medical image logarithmically, algebraically, or utilizing another
mathematical transform.
[0688]
Embodiment 189: The computer-implemented method of any one of
Embodiments 184-188, further comprising analyzing, by the computer system, a
medical
image of a non-coronary cardiovascular system of the subject, and wherein the
prognosis
of a cardiovascular event for the subject is further generated based at least
in part on the
analyzed medical image of the non-coronary cardiovascular system of the
subject.
[0689]
Embodiment 190: The computer-implemented method of any one of
Embodiments 184-189, further comprising accessing, by the computer system,
results of a
blood chemistry or biomarker test of the subject, and wherein the prognosis of
a
cardiovascular event for the subject is further generated based at least in
part on the results
of the blood chemistry or biomarker test of the subject.
[0690]
Embodiment 191: The computer-implemented method of any one of
Embodiments 184-190, wherein the generated prognosis of a cardiovascular event
for the
subject comprises a risk score of a cardiovascular event for the subject.
[0691]
Embodiment 192: The computer-implemented method of any one of
Embodiments 184-191, wherein the prognosis of a cardiovascular event is
generated by the
computer system utilizing an artificial intelligence or machine learning
algorithm.
[0692]
Embodiment 193: The computer-implemented method of any one of
Embodiments 184-192, wherein the cardiovascular event comprises one or more of
atherosclerosis, stenosis, or ischemia.
[0693]
Embodiment 194: The computer-implemented method of any one of
Embodiments 184-193, wherein the generated treatment plan comprises one or
more of use
of statins, lifestyle changes, or surgery.
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[0694]
Embodiment 195: The computer-implemented method of any one of
Embodiments 184-194, wherein one or more of the coronary artery identification
algorithm
or the plaque identification algorithm comprises an artificial intelligence or
machine
learning algorithm.
[0695]
Embodiment 196: The computer-implemented method of any one of
Embodiments 184-195, wherein the plaque identification algorithm is configured
to
determine the one or more regions of plaque by determining a vessel wall and
lumen wall
of the one or more coronary arteries and determining a volume between the
vessel wall and
lumen wall as the one or more regions of plaque.
106961
Embodiment 197: The computer-implemented method of any one of
Embodiments 184-196, wherein the medical image comprises a Computed Tomography
(CT) image.
[0697]
Embodiment 198: The computer-implemented method of Embodiment
197, wherein the medical image comprises a non-contrast CT image.
[0698]
Embodiment 199: The computer-implemented method of Embodiment
197, wherein the medical image comprises a contrast CT image.
[0699]
Embodiment 200: The computer-implemented method of any one of
Embodiments 184-196, wherein the medical image comprises a Magnetic Resonance
(MR)
image.
[0700]
Embodiment 201: The computer-implemented method of any one of
Embodiments 184-196, wherein the medical image is obtained using an imaging
technique
comprising one or more of CT, x-ray, ultrasound, echocardiography,
intravascular
ultrasound (IVUS), MR imaging, optical coherence tomography (OCT), nuclear
medicine
imaging, positron-emission tomography (PET), single photon emission computed
tomography (SPECT), or near-field infrared spectroscopy (NIRS).
[0701]
Embodiment 202: A computer-implemented method of determining
patient-specific stent parameters and guidance for implantation based on non-
invasive
medical image analysis, the method comprising: accessing, by a computer
system, a
medical image of a coronary region of a patient, wherein the medical image of
the coronary
region of the patient is obtained non-invasively; identifying, by the computer
system
utilizing a coronary artery identification algorithm, one or more coronary
arteries within
the medical image of the coronary region of the patient, wherein the coronary
artery
identification algorithm is configured to utilize raw medical images as input;
identifying,
by the computer system utilizing a plaque identification algorithm, one or
more regions of
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plaque within the one or more coronary arteries identified from the medical
image of the
coronary region of the patient, wherein the plaque identification algorithm is
configured to
utilize raw medical images as input; determining, by the computer system, a
set of
quantified plaque parameters of the one or more identified regions of plaque
from the
medical image of the coronary region of the patient, wherein the set of
quantified plaque
parameters comprises a ratio or function of volume to surface area,
heterogeneity index,
location, geometry, and radiodensity of the one or more regions of plaque
within the
medical image; determining, by the computer system, a set of stenosis vessel
parameters of
the one or more coronary arteries within the medical image of the coronary
region of the
patient, wherein the set of vessel parameters comprises volume, curvature,
vessel wall,
lumen wall, and diameter of the one or more coronary arteries within the
medical image in
the presence of stenosis; determining, by the computer system, a set of normal
vessel
parameters of the one or more coronary arteries within the medical image of
the coronary
region of the patient, wherein the set of vessel parameters comprises volume,
curvature,
vessel wall, lumen wall, and diameter of the one or more coronary arteries
within the
medical image without stenosis, wherein the set of normal vessel parameters
are determined
by graphically removing from the medical image of the coronary region of the
patient the
identified one or more regions of plaque; determining, by the computer system,
a predicted
effectiveness of stent implantation for the patient based at least in part on
the set of
quantified plaque parameters and the set of vessel parameters; generating, by
the computer
system, patient-specific stent parameters for the patient when the predicted
effectiveness of
stent implantation for the patient is above a predetermined threshold, wherein
the patient-
specific stent parameters are generated based at least in part on the set of
quantified plaque
parameters, the set of vessel parameters, and the set of normal vessel
parameters; and
generating, by the computer system, guidance for implantation of a patient-
specific stent
comprising the patient-specific stent parameters, wherein the guidance for
implantation of
the patient-specific stent is generated based at least in part on the set of
quantified plaque
parameters and the set of vessel parameters, wherein the generated guidance
for
implantation of the patient-specific stent comprises insertion of guidance
wires and
positioning of the patient-specific stent, wherein the computer system
comprises a
computer processor and an electronic storage medium.
[0702]
Embodiment 203: The computer-implemented method of Embodiment
202, further comprising accessing, by the computer system, a post-implantation
medical
image of the coronary region of the patient and performing post-implantation
analysis.
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[0703]
Embodiment 204: The computer-implemented method of Embodiment
203, further comprising generating, by the computer system, a treatment plan
for the patient
based at least in part on the post-implantation analysis.
[0704]
Embodiment 205: The computer-implemented method of Embodiment
204, wherein the generated treatment plan comprises one or more of use of
statins, lifestyle
changes, or surgery.
[0705]
Embodiment 206: The computer-implemented method of any one of
Embodiments 202-205, wherein the set of stenosis vessel parameters comprises a
location,
curvature, and diameter of bifurcation of the one or more coronary arteries.
107061
Embodiment 207: The computer-implemented method of any one of
Embodiments 202-206, wherein the patient-specific stent parameters comprise a
diameter
of the patient-specific stent.
[0707]
Embodiment 208: The computer-implemented method of Embodiment
207, wherein the diameter of the patient-specific stent is substantially equal
to the diameter
of the one or more coronary arteries without stenosis.
[0708]
Embodiment 209: The computer-implemented method of Embodiment
207, wherein the diameter of the patient-specific stent is less than the
diameter of the one
or more coronary arteries without stenosis.
[0709]
Embodiment 210: The computer-implemented method of any one of
Embodiments 202-209, wherein the predicted effectiveness of stent implantation
for the
patient is determined by the computer system utilizing an artificial
intelligence or machine
learning algorithm.
[0710]
Embodiment 211: The computer-implemented method of any one of
Embodiments 202-210, wherein the patient-specific stent parameters for the
patient are
generated by the computer system utilizing an artificial intelligence or
machine learning
algorithm.
[0711]
Embodiment 212: The computer-implemented method of any one of
Embodiments 202-211, wherein one or more of the coronary artery identification
algorithm
or the plaque identification algorithm comprises an artificial intelligence or
machine
learning algorithm.
[0712]
Embodiment 213: The computer-implemented method of any one of
Embodiments 202-212, wherein the plaque identification algorithm is configured
to
determine the one or more regions of plaque by determining a vessel wall and
lumen wall
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of the one or more coronary arteries and determining a volume between the
vessel wall and
lumen wall as the one or more regions of plaque.
[0713]
Embodiment 214: The computer-implemented method of any one of
Embodiments 202-213, wherein the medical image comprises a Computed Tomography
(CT) image.
[0714]
Embodiment 215: The computer-implemented method of Embodiment
214, wherein the medical image comprises a non-contrast CT image.
[0715]
Embodiment 216: The computer-implemented method of Embodiment
214, wherein the medical image comprises a contrast CT image.
107161
Embodiment 217: The computer-implemented method of any one of
Embodiments 202-213; wherein the medical image comprises a Magnetic Resonance
(MR)
image.
[0717]
Embodiment 218: The computer-implemented method of any one of
Embodiments 202-213, wherein the medical image is obtained using an imaging
technique
comprising one or more of CT, x-ray, ultrasound, echocardiography,
intravascular
ultrasound (IVUS), MR imaging, optical coherence tomography (OCT), nuclear
medicine
imaging, positron-emission tomography (PET), single photon emission computed
tomography (SPECT), or near-field infrared spectroscopy (N1RS).
[0718]
Embodiment 219: A computer-implemented method of generating a
patient-specific report on coronary artery disease for a patient based on non-
invasive
medical image analysis, the method comprising: accessing, by a computer
system, a
medical image of a coronary region of a patient, wherein the medical image of
the coronary
region of the patient is obtained non-invasively; identifying, by the computer
system
utilizing a coronary artery identification algorithm, one or more coronary
arteries within
the medical image of the coronary region of the patient, wherein the coronary
artery
identification algorithm is configured to utilize raw medical images as input;
identifying,
by the computer system utilizing a plaque identification algorithm, one or
more regions of
plaque within the one or more coronary arteries identified from the medical
image of the
coronary region of the patient, wherein the plaque identification algorithm is
configured to
utilize raw medical images as input; determining, by the computer system, one
or more
vascular morphology parameters and a set of quantified plaque parameters of
the one or
more identified regions of plaque from the medical image of the coronary
region of the
patient, wherein the set of quantified plaque parameters comprises a ratio or
function of
volume to surface area, volume, heterogeneity index, location, geometry, and
radiodensity
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of the one or more regions of plaque within the medical image; quantifying, by
the
computer system, stenosis and atherosclerosis of the patient based at least in
part on the set
of quantified plaque parameters determined from the medical image; generating,
by the
computer system, one or more annotated medical images based at least in part
on the
medical image, the quantified stenosis and atherosclerosis of the patient, and
the set of
quantified plaque parameters determined from the medical image; determining,
by the
computer system, a risk of coronary artery disease for the patient based at
least in part by
comparing the quantified stenosis and atherosclerosis of the patient and the
set of quantified
plaque parameters determined from the medical image to a known dataset of one
or more
quantified stenosis and atherosclerosis and one or more quantified plaque
parameters
derived from one or more medial images of healthy subjects within an age group
of the
patient; dynamically generating, by the computer system, a patient-specific
report on
coronary artery disease for the patient, wherein the generated patient-
specific report
comprises the one or more annotated medical images, one or more of the set of
quantified
plaque parameters, and determined risk of coronary artery disease, wherein the
computer
system comprises a computer processor and an electronic storage medium.
[0719]
Embodiment 220: The computer-implemented method of Embodiment
219, wherein the patient-specific report comprises a cinematic report.
[0720]
Embodiment 221: The computer-implemented method of Embodiment
220, wherein the patient-specific report comprises content configured to
provide an
Augmented Reality (AR) or Virtual Reality (VR) experience_
[0721]
Embodiment 222: The computer-implemented method of any one of
Embodiments 219-221, wherein the patient-specific report comprises audio
dynamically
generated for the patient based at least in part on the quantified stenosis
and atherosclerosis
of the patient, the set of quantified plaque parameters determined from the
medical image,
and determined risk of coronary artery disease.
[0722]
Embodiment 223: The computer-implemented method of any one of
Embodiments 219-222, wherein the patient-specific report comprises phrases
dynamically
generated for the patient based at least in part on the quantified stenosis
and atherosclerosis
of the patient, the set of quantified plaque parameters determined from the
medical image,
and determined risk of coronary artery disease.
[0723]
Embodiment 224: The computer-implemented method of any one of
Embodiments 219-223. further comprising generating, by the computer system, a
treatment
plan for the patient based at least in part on the quantified stenosis and
atherosclerosis of
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the patient, the set of quantified plaque parameters determined from the
medical image, and
determined risk of coronary artery disease, wherein the patient-specific
report comprises
the generated treatment plan.
[0724]
Embodiment 225: The computer-implemented method of Embodiment
224, wherein the generated treatment plan comprises one or more of use of
statins, lifestyle
changes, or surgery.
[0725]
Embodiment 226: The computer-implemented method of any one of
Embodiments 219-225, further comprising tracking, by the computer system,
progression
of coronary artery disease for the patient based at least in part on comparing
one or more
of the set of quantified plaque parameters determined from the medical image
against one
or more previous quantified plaque parameters derived from a previous medical
image of
the patient, wherein the patient-specific report comprises the tracked
progression of
coronary artery disease.
[0726]
Embodiment 227: The computer-implemented method of any one of
Embodiments 219-226, wherein one or more of the coronary artery identification
algorithm
or the plaque identification algorithm comprises an artificial intelligence or
machine
learning algorithm.
[0727]
Embodiment 228: The computer-implemented method of any one of
Embodiments 219-227, wherein the plaque identification algorithm is configured
to
determine the one or more regions of plaque by determining a vessel wall and
lumen wall
of the one or more coronary arteries and determining a volume between the
vessel wall and
lumen wall as the one or more regions of plaque.
[0728]
Embodiment 229: The computer-implemented method of any one of
Embodiments 219-228, wherein the medical image comprises a Computed Tomography
(CT) image.
[0729]
Embodiment 230: The computer-implemented method of Embodiment
229, wherein the medical image comprises a non-contrast CT image.
[0730]
Embodiment 231: The computer-implemented method of Embodiment
229, wherein the medical image comprises a contrast CT image.
[0731]
Embodiment 232: The computer-implemented method of any one of
Embodiments 219-228, wherein the medical image comprises a Magnetic Resonance
(MR)
image.
[0732]
Embodiment 233: The computer-implemented method of any one of
Embodiments 219-228, wherein the medical image is obtained using an imaging
technique
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comprising one or more of CT, x-ray, ultrasound, echocardiography,
intravascular
ultrasound (IVUS), MR imaging, optical coherence tomography (OCT), nuclear
medicine
imaging, positron-emission tomography (PET), single photon emission computed
tomography (SPECT), or near-field infrared spectroscopy (N1RS).
[0733]
Embodiment 234: A system comprising: at least one non-transitory
computer storage medium configured to at least store computer-executable
instructions, a
set of computed tomography (CT) images of a patient's coronary vessels, vessel
labels, and
artery information associated with the set of CT images including information
of stenosis,
plaque, and locations of segments of the coronary vessels; one or more
computer hardware
processors in communication with the at least one non-transitory computer
storage
medium, the one or more computer hardware processors configured to execute the
computer-executable instructions to at least: generate and display a user
interface a first
panel including an artery tree comprising a three-dimensional (3D)
representation of
coronary vessels depicting coronary vessels identified in the CT images, and
including
segment labels related to the artery tree, the artery tree not including heart
tissue between
branches of the artery tree; in response to an input on the user interface
indicating the
selection of a coronary vessel in the artery tree in the first panel, generate
and display on
the user interface a second panel illustrating at least a portion of the
selected coronary vessel
in at least one straightened multiplanar vessel (SMPR) view; generate and
display on the
user interface a third panel showing a cross-sectional view of the selected
coronary vessel,
the cross-sectional view generated using one of the set of CT images of the
selected
coronary vessel, wherein locations along the at least one SMPR view are each
associated
with one of the CT images in the set of CT images such that a selection of a
particular
location along the coronary vessel in the at least one SMPR view displays the
associated
CT image in the cross-sectional view in the third panel; and in response to an
input on the
third panel indicating a first location along the selected coronary artery in
the at least one
SMPR view, display a cross-sectional view associated with the selected
coronary artery at
the first location in the third panel.
[0734]
Embodiment 235: The system of embodiment 234, wherein the one or
more computer hardware processors are further configured to execute the
computer-
executable instructions to, in response to an input on the second panel of the
user interface
indicating a second location along the selected coronary artery in the at
least one SMPR
view, display the associated CT scan associated with the second location in a
cross-
sectional view in the third panel.
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[0735]
Embodiment 236: The system of embodiment 234, wherein the one or
more computer hardware processors are further configured to execute the
computer-
executable instructions to: in response to a second input on the user
interface indicating the
selection of a second coronary vessel in the artery tree displayed in the
first panel, generate
and display in the second panel at least a portion of the selected second
coronary vessel in
at least one straightened multiplanar vessel (SMPR) view, and generate and
display on the
third panel a cross-sectional view of the selected second coronary vessel, the
cross-sectional
view generated using one of the set of CT images of the selected second
coronary vessel,
wherein locations along the selected second coronary artery in the at least
one SMPR view
are each associated with one of the CT images in the set of CT images such
that a selection
of a particular location along the second coronary vessel in the at least one
SMPR view
displays the associated CT image in the cross-sectional view in the third
panel.
[0736]
Embodiment 237: The system of embodiment 234, wherein the one or
more computer hardware processors are further configured to identify the
vessel segments
using a machine learning algorithm that processes the CT images prior to
storing the artery
information on the at least one non-transitory computer storage medium.
[0737]
Embodiment 238: The system of embodiment 234, wherein the one or
more computer hardware processors are further configured to execute the
computer-
executable instructions to generate and display on the user interface in a
fourth panel a
cartoon artery tree, the cartoon artery tree comprising a non-patient specific
graphical
representation of a coronary artery tree, and wherein in response to a
selection of a vessel
segment in the cartoon artery tree, a view of the selected vessel segment is
displayed in a
panel of the user interface in a SMPR view, and upon selection of a location
of the vessel
segment displayed in the SMPR view, generate and display in the user interface
a panel
that displays information about the selected vessel at the selected location.
[0738]
Embodiment 239: The system of embodiment 238, wherein the
displayed information includes information relating to stenosis and plaque of
the selected
vessel.
[0739]
Embodiment 240: The system of embodiment 234, wherein the one or
more computer hardware processors are further configured to execute the
computer-
executable instructions to generate and segment name labels, proximal to a
respective
segment on the artery tree, indicative of the name of the segment.
[0740]
Embodiment 241: The system of embodiment 240, wherein the one or
more computer hardware processors are further configured to execute the
computer-
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executable instructions to, in response to an input selection of a first
segment name label
displayed on the user interface, generate and display on the user interface a
panel having a
list of vessel segment names and indicating the current name of the selected
vessel segment;
and in response to an input selection of a second segment name label on the
list, replace the
first segment name label with the second segment name label of the displayed
artery tree
in the user interface.
[0741]
Embodiment 242: The system of embodiment 234, wherein the at least
one SMPR view of the selected coronary vessel comprises at least two SMPR
views of the
selected coronary vessel displayed adjacently at a rotational interval.
107421
Embodiment 243: The system of embodiment 234, wherein the at least
one SMPR view include four SMPR views displayed at a relative rotation of 00,
22.5 , 45 ,
and 67.5'.
[0743]
Embodiment 244: The system of embodiment 234, wherein the one or
more computer hardware processors are further configured to execute the
computer-
executable instructions to, in response to a user input, rotate the at least
one SMPR view in
increments of 1 .
[0744]
Embodiment 245: The system of embodiment 234, wherein the artery
tree, the at least one SMPR view, and the cross-sectional view are displayed
concurrently
on the user interface.
[0745]
Embodiment 246: The system of embodiment 245, wherein the artery
tree is displayed in a center portion of the user panel, the cross-sectional
view is displayed
in a center portion of the user interface above or below the artery tree, and
the at least one
SMPR view are displayed on one side of the center portion of the user
interface.
107461
Embodiment 247: The system of embodiment 246, wherein the one or
more computer hardware processors are further configured to generate and
display, on one
side of the center portion of the user interface, one or more anatomical plane
views
corresponding to the selected coronary artery, the anatomical plane views of
the selected
coronary vessel based on the CT images.
[0747]
Embodiment 248: The system of embodiment 247, wherein the
anatomical plane views comprise three anatomical plane views.
[0748]
Embodiment 249: The system of embodiment 247, wherein the
anatomical plane views comprise at least one of an axial plane view, a coronal
plane view,
or a sagittal plane view.
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[0749]
Embodiment 250: The system of embodiment 234, wherein the one or
more computer hardware processors are further configured to receive a rotation
input on
the user interface, and rotate the at least one SMPR views incrementally based
on the
rotation input.
[0750]
Embodiment 251: The system of embodiment 234, wherein the at least
one non-transitory computer storage medium is further configured to at least
store vessel
wall information including information indicative of the lumen and the vessel
walls of the
coronary artery vessels, and wherein the one or more computer hardware
processors are
further configured to graphically display lumen and vessel wall information
corresponding
to the coronary vessel displayed in the cross-sectional view in the third
panel.
[0751]
Embodiment 252: The system of embodiment 251, wherein and one or
more computer hardware processors are further configured to display
information of the
lumen and the vessel wall on the user interface based on the selected portion
of the coronary
vessel in the at least one SMPR view.
[0752]
Embodiment 253: The system of embodiment 251, wherein and one or
more computer hardware processors are further configured to display
information of plaque
based on the selected portion of the coronary vessel in the at least one SMPR
view.
[0753]
Embodiment 254: The system of embodiment 251, wherein and one or
more computer hardware processors are further configured to display
information of
stenosis based on the selected portion of the coronary vessel in the at least
one SMPR view.
[0754]
Embodiment 255: The system of embodiment 234, wherein the one or
more computer hardware processors are further configured to execute the
computer-
executable instructions to generate and display on the user interface a
cartoon artery tree,
the cartoon artery tree being a non-patient specific graphical representation
of an artery
tree, wherein portions of the artery tree are displayed in a color that
corresponds to a risk
level.
[0755]
Embodiment 256: The system of embodiment 255, wherein the risk
level is based on stenosis.
[0756]
Embodiment 257: The system of embodiment 255, wherein the risk
level is based on a plaque.
[0757]
Embodiment 258: The system of embodiment 255, wherein the risk
level is based on ischemia.
[0758]
Embodiment 259: The system of embodiment 255, wherein the one or
more computer hardware processors are further configured to execute the
computer-
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executable instructions to, in response to selecting a portion of the cartoon
artery tree,
displaying on the second panel a SMPR view of the vessel corresponding to the
selected
portion of the cartoon artery tree, and displaying on the third panel a cross-
sectional view
of corresponding to the selected portion of the cartoon artery tree.
[0759]
Embodiment 260: A system comprising: means for storing computer-
executable instructions, a set of computed tomography (CT) images of a
patient's coronary
vessels, vessel labels, and artery information associated with the set of CT
images including
information of stenosis, plaque, and locations of segments of the coronary
vessels; and
means for executing the computer-executable instructions to at least: generate
and display
a user interface a first panel including an artery tree comprising a three-
dimensional (3D)
representation of coronary vessels based on the CT images and depicting
coronary vessels
identified in the CT images, and depicting segment labels, the artery tree not
including heart
tissue between branches of the artery tree; in response to an input on the
user interface
indicating the selection of a coronary vessel in the artery tree in the first
panel, generate
and display on the user interface a second panel illustrating at least a
portion of the selected
coronary vessel in at least one straightened multiplanar vessel (SMPR) view;
generate and
display on the user interface a third panel showing a cross-sectional view of
the selected
coronary vessel, the cross-sectional view generated using one of the set of CT
images of
the selected coronary vessel, wherein locations along the at least one SMPR
view are each
associated with one of the CT images in the set of CT images such that a
selection of a
particular location along the coronary vessel in the at least one SMPR view
displays the
associated CT image in the cross-sectional view in the third panel; and in
response to an
input on the user interface indicating a first location along the selected
coronary artery in
the at least one SMPR view, display the associated CT scan associated with the
in the cross-
sectional view in the third panel.
[0760]
Embodiment 261: A method for analyzing CT images and
corresponding information, the method comprising: storing computer-executable
instructions, a set of computed tomography (CT) images of a patient's coronary
vessels,
vessel labels, and artery information associated with the set of CT images
including
information of stenosis, plaque, and locations of segments of the coronary
vessels;
generating and displaying in a user interface a first panel including an
artery tree
comprising a three-dimensional (3D) representation of coronary vessels based
on the CT
images and depicting coronary vessels identified in the CT images, and
depicting segment
labels, the artery tree not including heart tissue between branches of the
artery tree;
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receiving a first input indicating a selection of a coronary vessel in the
artery tree in the
first panel; in response to the first input, generating and displaying on the
user interface a
second panel illustrating at least a portion of the selected coronary vessel
in at least one
straightened multiplanar vessel (SMPR) view; generating and displaying on the
user
interface a third panel showing a cross-sectional view of the selected
coronary vessel, the
cross-sectional view generated using one of the set of CT images of the
selected coronary
vessel, wherein locations along the at least one SMPR view are each associated
with one
of the CT images in the set of CT images such that a selection of a particular
location along
the coronary vessel in the at least one SMPR view displays the associated CT
image in the
cross-sectional view in the third panel; receiving a second input on the user
interface
indicating a first location along the selected coronary artery in the at least
one SMPR view;
and in response to the second input, displaying the associated CT scan
associated in the
cross-sectional view in the third panel, wherein the method is performed by
one or more
computer hardware processors executing computer-executable instructions in
communication stored on one or more non-transitory computer storage mediums.
[0761]
Embodiment 262: The method of embodiment 261, further comprising,
in response to an input on the second panel of the user interface indicating a
second location
along the selected coronary artery in the at least one SMPR view, display the
associated
CT scan associated with the second location in a cross-sectional view in the
third panel.
[0762]
Embodiment 263: The method of any one of embodiments 261 and 262,
further comprising. in response to a second input on the user interface
indicating the
selection of a second coronary vessel in the artery tree displayed in the
first panel,
generating and displaying in the second panel at least a portion of the
selected second
coronary vessel in at least one straightened multiplanar vessel (SMPR) view,
and
generating and displaying on the third panel a cross-sectional view of the
selected second
coronary vessel, the cross-sectional view generated using one of the set of CT
images of
the selected second coronary vessel, wherein locations along the selected
second coronary
artery in the at least one SMPR view are each associated with one of the CT
images in the
set of CT images such that a selection of a particular location along the
second coronary
vessel in the at least one SMPR view displays the associated CT image in the
cross-
sectional view in the third panel.
[0763]
Embodiment 264: The method of any one of embodiments 261-263,
further comprising generating and displaying on the user interface in a fourth
panel a
cartoon artery tree, the cartoon artery tree comprising a non-patient specific
graphical
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representation of a coronary artery tree, and wherein in response to a
selection of a vessel
segment in the cartoon artery tree, a view of the selected vessel segment is
displayed in a
panel of the user interface in a SMPR view, and upon selection of a location
of the vessel
segment displayed in the SMPR view, generating and displaying in the user
interface a
panel that displays information about the selected vessel at the selected
location.
[0764]
Embodiment 265: The method of embodiment 264, wherein the
displayed information includes information relating to stenosis and plaque of
the selected
vessel.
[0765]
Embodiment 266: The method of any one of embodiments 261-265,
further comprising generating and displaying segment name labels, proximal to
a respective
segment on the artery tree, indicative of the name of the segment, using the
stored artery
information.
[0766]
Embodiment 267: The method of any one of embodiments 261-266,
further comprising, in response to an input selection of a first segment name
label displayed
on the user interface, generating and displaying on the user interface a panel
having a list
of vessel segment names and indicating the current name of the selected vessel
segment,
and in response to an input selection of a second segment name label on the
list, replacing
the first segment name label with the second segment name label of the
displayed artery
tree in the user interface.
[0767]
Embodiment 268: The method of any one of embodiments 261-267,
further comprising generating and displaying a tool bar on a fourth panel of
the user
interface, the tool bar comprising tools to add, delete, or revise artery
information displayed
on the user interface.
107681
Embodiment 269: The method of embodiment 268, wherein the tools on
the toolbar include a lumen wall tool, a snap to vessel wall tool, a snap to
lumen wall tool,
vessel wall tool, a segment tool, a stenosis tool, a plaque overlay tool a
snap to centerline
tool, chronic total occlusion tool, stent tool, an exclude tool, a tracker
tool, or a distance
measurement tool.
[0769]
Embodiment 270: The method of embodiment 268, wherein the tools on
the toolbar include a lumen wall tool, a snap to vessel wall tool, a snap to
lumen wall tool,
vessel wall tool, a segment tool, a stenosis tool, a plaque overlay tool a
snap to centerline
tool, chronic total occlusion tool, stent tool, an exclude tool, a tracker
tool, and a distance
measurement tool.
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[0770]
Embodiment 271: A normalization device configured to facilitate
normalization of medical images of a coronary region of a subject for an
algorithm-based
medical imaging analysis, the normalization device comprising: a substrate
having a width,
a length, and a depth dimension, the substrate having a proximal surface and a
distal
surface, the proximal surface adapted to be placed adjacent to a surface of a
body portion
of a patient; a plurality of compartments positioned within the substrate,
each of the
plurality of compartments configured to hold a sample of a known material,
wherein: a first
subset of the plurality of compartments hold samples of a contrast material
with different
concentrations, a second subset of the plurality of compartments hold samples
of materials
representative of materials to be analyzed by the algorithm-based medical
imaging analysis,
and a third subset of the plurality of compartments hold samples of phantom
materials.
[0771]
Embodiment 272: The normalization device of Embodiment 271,
wherein the contrast material comprises one of iodine, Gad, Tantalum,
Tungsten, Gold,
Bismuth, or Ytterbium.
[0772]
Embodiment 273: The normalization device of any of Embodiments
271-272, wherein the samples of materials representative of materials to be
analyzed by
the algorithm-based medical imaging analysis comprise at least two of calcium
1000HU,
calcium 220HU, calcium 150HU, calcium 130HU, and a low attenuation (e.g., 30
HU)
material.
[0773]
Embodiment 274: The normalization device of any of Embodiments
271-273, wherein the samples of phantom materials comprise one or more of
water, fat,
calcium, uric acid, air, iron, or blood.
[0774]
Embodiment 275: The normalization device of any of Embodiments
271-274, further comprising one or more fiducials positioned on or in the
substrate for
determining the alignment of the normalization device in an image of the
normalization
device such that the position in the image of each of the one or more
compartments in the
first arrangement can be determined using the one or more fiducials.
[0775]
Embodiment 276: The normalization device of any of Embodiments
271-275, wherein the substrate comprises a first layer, and at least some of
the plurality of
compartments are positioned in the first layer in a first arrangement.
[0776]
Embodiment 277: The normalization device of Embodiment 276,
wherein the substrate further comprises a second layer positioned above the
first layer, and
at least some of the plurality of compartments are positioned in the second
layer including
in a second arrangement.
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[0777]
Embodiment 278: The normalization device of Embodiment 277,
further comprising one or more additional layers positioned above the second
layer, and at
least some of the plurality of compartments are positioned within the one or
more additional
layers.
[0778]
Embodiment 279: The normalization device of any one of Embodiments
271-278, wherein at least one of the compartments is configured to be self-
sealing such that
the material can be injected into the self-sealing compartment and the
compartment seals
to contain the injected material.
[0779]
Embodiment 280: The normalization device of any of Embodiments
271-279, further comprising an adhesive on the proximal surface of the
substrate and
configured to adhere the normalization device to the body portion patient.
[0780]
Embodiment 281: The normalization device of any of Embodiments
271-280, further comprising a heat transfer material designed to transfer heat
from the body
portion of the patient to the material in the one or more compartments.
[0781]
Embodiment 282: The normalization device of any of Embodiments
271-280, further comprising an adhesive strip having a proximal side and a
distal side, the
proximal side configured to adhere to the body portion, the adhesive strip
including a
fastener configured to removably attach to the proximal surface of the
substrate.
[0782]
Embodiment 283: The normalization device of Embodiment 282,
wherein the fastener comprises a first part of a hook-and-loop fastener, and
the first layer
comprises a corresponding second part of the hook-and-loop fastener.
[0783]
Embodiment 284: The normalization device of any of Embodiments
271-283, wherein substrate a flexible material to allow the substrate to
conform to the shape
of the body portion.
[0784]
Embodiment 285: The normalization device of any of Embodiments
271-284, wherein the first arrangement includes a circular-shaped arrangements
of the
compartments.
[0785]
Embodiment 286: The normalization device of any of Embodiments
271-284, wherein the first arrangement includes a rectangular-shaped
arrangements of the
compartments.
[0786]
Embodiment 287: The normalization device of any of Embodiments
271-286, wherein the material in at least two compartments is the same.
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[0787]
Embodiment 288: The normalization device of any of Embodiments
271-287, wherein at least one of a length, a width or a depth dimension of a
compartment
is less than 0.5 mm.
[0788]
Embodiment 289: The normalization device of any of Embodiments
271-287, wherein a width dimension of the compartments is between 0.1 mm and 1
mm.
[0789]
Embodiment 290: The normalization device of Embodiment 289,
wherein a length dimension of the compartments is between 0.1 mm and 1 mm.
[0790]
Embodiment 291: The normalization device of Embodiment 290,
wherein a depth dimension of the compartments is between 0.1 mm and 1 mm.
107911
Embodiment 292: The normalization device of any of Embodiments
271-287, wherein at least one of the length, width or depth dimension of a
compartment is
greater than 1.0 mm.
[0792]
Embodiment 293: The normalization device of any of Embodiments
271-287, wherein dimensions of some or all of the compartments in the
normalization
device are different from each other allowing a single normalization device to
have a
plurality of compartments having different dimensions such that the
normalization device
can be used in various medical image scanning devices having different
resolution
capabilities.
[0793]
Embodiment 294: The normalization device of any of Embodiments
271-287, wherein the normalization device includes a plurality of compartments
with
differing dimensions such that the normalization device can be used to
determine the actual
resolution capability of the scanning device.
[0794]
Embodiment 295: A normalization device, comprising: a first layer
having a width, length, and depth dimension, the first layer having a proximal
surface and
a distal surface, the proximal surface adapted to be placed adjacent to a
surface of a body
portion of a patient, the first layer including one or more compartments
positioned in the
first layer in a first arrangement, each of the one or more compartments
containing a known
material; and one or more fiducials for determining the alignment of the
normalization
device in an image of the normalization device such that the position in the
image of each
of the one or more compartments in the first arrangement be the determined
using the one
or more fi duci al s.
[0795]
Embodiment 296: The normalization device of Embodiment 295,
further comprising a second layer having a width, length, and depth dimension,
the second
layer having a proximal surface and a distal surface, the proximal surface
adjacent to the
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distal surface of the first layer, the second layer including one or more
compartments
positioned in the second layer in a second arrangement, each of the one or
more
compartments of the second layer containing a known material.
[0796]
Embodiment 297: The normalization device of Embodiment 296,
further comprising one or more additional layers each having a width, length,
and depth
dimension, the one or more additional layers having a proximal surface and a
distal surface,
the proximal surface facing the second layer and each of the one or more
layers positioned
such that the second layer is between the first layer and the one or more
additional layers,
each of the one or more additional layers respectively including one or more
compartments
positioned in each respective one or more additional layers layer in a second
arrangement,
each of the one or more compartments of the one or more additional layers
containing a
known material.
[0797]
Embodiment 298: The normalization device of any one of Embodiments
295-297, wherein at least one of the compartments is configured to be self-
sealing such that
the material can be injected into the self-sealing compartment and the
compartment seals
to contain the injected material.
[0798]
Embodiment 299: The normalization device of Embodiment 295,
further comprising an adhesive on the proximal surface of the first layer.
[0799]
Embodiment 300: The normalization device of Embodiment 295,
further comprising a heat transfer material designed to transfer heat from the
body portion
of the patient to the material in the one or more compartments.
[0800]
Embodiment 301: The normalization device of Embodiment 295,
further comprising an adhesive strip having a proximal side and a distal side,
the proximal
side configured to adhere to the body portion, the adhesive strip including a
fastener
configured to removably attach to the proximal surface of the first layer.
[0801]
Embodiment 302: The normalization device of Embodiment 301,
wherein the fastener comprises a first part of a hook-and-loop fastener, and
the first layer
comprises a corresponding second part of the hook-and-loop fastener.
[0802]
Embodiment 303: The normalization device of Embodiment 295,
wherein the normalization device comprises a flexible material to allow the
normalization
device to conform to the shape of the body portion.
[0803]
Embodiment 304: The normalization device of Embodiment 295,
wherein the first arrangement includes a circular-shaped arrangements of the
compartments.
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[0804]
Embodiment 305: The normalization device of Embodiment 295,
wherein the first arrangement includes a rectangular-shaped arrangements of
the
compartments.
[0805]
Embodiment 306: The normalization device of Embodiment 295,
wherein the material in at least two compartments of the first layer is the
same.
[0806]
Embodiment 307: The normalization device of any of Embodiment 296
or 297, wherein the material in at least two compartments of any of the layers
is the same.
[0807]
Embodiment 308: The normalization device of Embodiment 295,
wherein at least one of the one or more compartments include a contrast
material.
108081
Embodiment 309: The normalization device of Embodiment 308,
wherein the contrast material comprises one of iodine, Gad, Tantalum,
Tungsten, Gold,
Bismuth, or Ytterbium.
[0809]
Embodiment 310: The normalization device of Embodiment 295,
wherein at least one of the one or more compartments include a material
representative of
a studied variable.
[0810]
Embodiment 311: The normalization device of Embodiment 309,
wherein the studied variable is representative of calcium 1000HU, calcium
220HU, calcium
150HU, calcium 130HU, or a low attenuation (e.g., 30 HU) material.
[0811]
Embodiment 312: The normalization device of Embodiment 295,
wherein at least one of the one or more compartments include a phantom.
[0812]
Embodiment 313: The normalization device of Embodiment 312,
wherein the phantom comprises one of water, fat, calcium, uric acid, air,
iron, or blood.
[0813]
Embodiment 314: The normalization device of Embodiment 295,
wherein the first arrangement includes at least one compartment that contains
a contrast
agent, at least one compartment that includes a studied variable and at least
one
compartment that includes a phantom.
[0814]
Embodiment 315: The normalization device of Embodiment 295,
wherein the first arrangement includes at least one compartment that contains
a contrast
agent and at least one compartment that includes a studied variable.
[0815]
Embodiment 316: The normalization device of Embodiment 295,
wherein the first arrangement includes at least one compartment that contains
a contrast
agent and at least one compartment that includes a phantom.
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[0816]
Embodiment 317: The normalization device of Embodiment 295,
wherein the first arrangement includes at least one compartment that contains
a studied
variable and at least one compartment that includes a phantom.
[0817]
Embodiment 318: The normalization device of Embodiment 271,
wherein the first arrangement of the first layer includes at least one
compartment that
contains a contrast agent, at least one compartment that includes a studied
variable and at
least one compartment that includes a phantom, and the second arrangement of
the second
layer includes at least one compartment that contains a contrast agent, at
least one
compartment that includes a studied variable and at least one compartment that
includes a
phantom.
[0818]
Embodiment 319: The normalization device of Embodiment 295,
wherein at least one of the length, width or depth dimension of a compartment
is less than
0.5 mm.
[0819]
Embodiment 320: The normalization device of Embodiment 295,
wherein the width dimension of the compartments is between 0.1 mm and 1 mm.
[0820]
Embodiment 321: The normalization device of Embodiment 295,
wherein the length dimension of the compartments is between 0.1 mm and 1 mm.
[0821]
Embodiment 322: The normalization device of Embodiment 295,
wherein the depth (or height) dimension of the compartments is between 0.1 mm
and 1
mm.
[0822]
Embodiment 323: The normalization device of Embodiment 295,
wherein at least one of the length, width or depth dimension of a compartment
is greater
than 1.0 mm.
108231
Embodiment 324: The normalization device of any one of Embodiments
295-297, wherein the dimensions of some or all of the compartments in the
normalization
device are different from each other allowing a single normalization device to
have a
plurality of compartments having different dimension such that the
normalization device
can be used in various medical image scanning devices having different
resolution
capabilities.
[0824]
Embodiment 325: The normalization device of any one of Embodiments
295-297, wherein the normalization device includes a plurality of compartments
with
differing dimensions such that the normalization device can be used to
determine the actual
resolution capability of the scanning device.
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108251
Embodiment 326: A computer-implemented method for normalizing
medical images for an algorithm-based medical imaging analysis, wherein
normalization
of the medical images improves accuracy of the algorithm-based medical imaging
analysis,
the method comprising: accessing, by a computer system, a first medical image
of a region
of a subject and the normalization device, wherein the first medical image is
obtained non-
invasively, and wherein the normalization device comprises a substrate
comprising a
plurality of compartments, each of the plurality of compartments holding a
sample of a
known material; accessing, by the computer system, a second medical image of a
region of
a subject and the normalization device, wherein the second medical image is
obtained non-
invasively, and wherein the first medical image and the second medical image
comprise at
least one of the following: one or more first variable acquisition parameters
associated with
capture of the first medical image differ from a corresponding one or more
second variable
acquisition parameters associated with capture of the second medical image, a
first image
capture technology used to capture the first medical image differs from a
second image
capture technology used to capture the second medical image, and a first
contrast agent
used during the capture of the first medical image differs from a second
contrast agent used
during the capture of the second medical image; identifying, by the computer
system, image
parameters of the normalization device within the first medical image;
generating a
normalized first medical image for the algorithm-based medical imaging
analysis based in
part on the first identified image parameters of the normalization device
within the first
medical image; identifying, by the computer system, image parameters of the
normalization
device within the second medical image; and generating a normalized second
medical
image for the algorithm-based medical imaging analysis based in part on the
second
identified image parameters of the normalization device within the second
medical image,
wherein the computer system comprises a computer processor and an electronic
storage
medium.
[0826]
Embodiment 327: The computer-implemented method of Embodiment
326, wherein the algorithm-based medical imaging analysis comprises an
artificial
intelligence or machine learning imaging analysis algorithm, and wherein the
artificial
intelligence or machine learning imaging analysis algorithm was trained using
images that
included the normalization device.
[0827]
Embodiment 328: The computer-implemented method of any of
Embodiments 326-327, wherein the first medical image and the second medical
image each
comprise a CT image and the one or more first variable acquisition parameters
and the one
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or more second variable acquisition parameters comprise one or more of a
kilovoltage (kV),
kilov oltage peak (kVp), a milliamperage (mA), or a method of gating.
[0828]
Embodiment 329: The computer-implemented method of Embodiment
328, wherein the method of gating comprises one of prospective axial
triggering,
retrospective ECG helical gating, and fast pitch helical.
[0829]
Embodiment 330: The computer-implemented method of any of
Embodiments 326-329, wherein the first image capture technology and the second
image
capture technology each comprise one of a dual source scanner, a single source
scanner,
Dual source vs. single source scanners dual energy, monochromatic energy,
spectral CT,
photon counting, and different detector materials.
[0830]
Embodiment 331: The computer-implemented method of any of
Embodiments 326-330, wherein the first contrast agent and the second contrast
agent each
comprise one of an iodine contrast of varying concentration or a non-iodine
contrast agent.
[0831]
Embodiment 332: The computer-implemented method of any of
Embodiments 326-327, wherein the first image capture technology and the second
image
capture technology each comprise one of CT, x-ray, ultrasound,
echocardiography,
intravascular ultrasound (IVUS), MR imaging, optical coherence tomography
(OCT),
nuclear medicine imaging, positron-emission tomography (PET), single photon
emission
computed tomography (SPECT), or near-field infrared spectroscopy (NIRS).
[0832]
Embodiment 333: The computer-implemented method of any of
Embodiments 326-332, wherein a first medical imager that captures the first
medical
imager is different than a second medical image that capture the second
medical image.
[0833]
Embodiment 334: The computer-implemented method of any of
Embodiments 326-333, wherein the subject of the first medical image is
different than the
subject of the first medical image.
[0834]
Embodiment 335: The computer-implemented method of any of
Embodiments 326-333, wherein the subject of the first medical image is the
same as the
subject of the second medical image.
[0835]
Embodiment 336: The computer-implemented method of any of
Embodiments 326-333, wherein the subject of the first medical image is
different than the
subject of the second medical image.
[0836]
Embodiment 337: The computer-implemented method of any of
Embodiments 326-336, wherein the capture of the first medical image is
separated from
the capture of the second medical image by at least one day.
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[0837]
Embodiment 338: The computer-implemented method of any of
Embodiments 326-337, wherein the capture of the first medical image is
separated from
the capture of the second medical image by at least one day.
[0838]
Embodiment 339: The computer-implemented method of any of
Embodiments 326-338, wherein a location of the capture of the first medical
image is
geographically separated from a location of the capture of the second medical
image.
[0839]
Embodiment 340: The computer-implemented method of any of
Embodiments 326-339, wherein the normalization device comprises the
normalization
device of any of Embodiments 271-325.
108401
Embodiment 341: The computer-implemented method of any of
Embodiments 326-340, wherein the region of the subject comprises a coronary
region of
the subject.
[0841]
Embodiment 342: The computer-implemented method of any of
Embodiments 326-341, wherein the region of the subject comprises one or more
coronary
arteries of the subject.
[0842]
Embodiment 343: The computer-implemented method of any of
Embodiments 326-340, wherein the region of the subject comprises one or more
of carotid
arteries, renal arteries, abdominal aorta, cerebral arteries, lower
extremities, or upper
extremities of the subject.
Additional Detail ¨ Normalization Device
[0843]
As described above and throughout this application, in some
embodiments, a normalization device may be used to normalize and/or calibrate
a medical
image of a patient before that image is analyzed by an algorithm-based medical
imaging
analysis.
This section provides additional detail regarding embodiments of the
normalization device and embodiments of the use thereof
[0844]
In general, the normalization device can be configured to provide at least
two functions: (1) the normalization device can be used to normalize and
calibrate a
medical image to a known relative spectrum; and (2) the normalization device
can be used
to calibrate a medical image such that pixels within the medical image
representative of
various materials can be normalized and calibrated to materials of known
absolute
density¨this can facilitate and allow identification of materials within the
medical image.
In some embodiments, each of these two functions play a role in providing
accurate
algorithm-based medical imaging analysis as will be described below.
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[0845]
For example, it can be important to normalize and calibrate a medical
image to a known relative spectrum. As a specific example, a CT scan generally
produces
a medical image comprising pixels represented in gray scale. However, when two
CT scans
are taken under different conditions, the gray scale spectrum in the first
image may not (and
likely will not) match the gray scale spectrum of the second image. That is,
even if the first
and second CT images represent the same subject, the specific grayscale values
in the two
images, even for the same structure may not (and likely will not) match. A
pixel or group
of pixels within the first image that represents a calcified plaque buildup
within a blood
vessel, may (and likely will) appear different (a different shade of gray, for
example, darker
or lighter) than a pixel or group of pixels within the second image, even if
the pixel or
groups of pixels within the first and second images is representative of the
same calcified
plaque buildup.
[0846]
Moreover, the differences between the first and second images may not
be linear. That is, the second image may not be uniformly lighter or darker
than the first
image, such that it is not possible to use a simple linear transform to cause
the two images
to correspond. Rather, it is possible that, for example, some regions in the
first image may
appear lighter than corresponding regions in the second image, while at the
same time,
other regions in the first image may appear darker than corresponding regions
in the second
image. In order to normalize the two medical images such that each appears on
the same
grayscale spectrum, a non-linear transform may be necessary. Use of the
normalization
device can facilitate and enable such a non-linear transform such that
different medical
images, that otherwise would not appear to have the same grayscale spectrum,
are adjusted
so that the same grayscale spectrum is used in each image.
108471
A wide variety of factors can contribute to different medical images,
even of the same subject, falling on different grayscale spectrums. This can
include, for
example, different medical imaging machine parameters, different parameters
associated
with the patient, differences in contrast agents used, and/or different
medical image
acquisition parameters.
[0848]
It can be important to normalize and calibrate a medical image to a
known relative spectrum to facilitate the algorithm-based analysis of the
medical image.
As described herein, some algorithm-based medical image analysis can be
performed using
artificial intelligence and/or machine learning systems. Such artificial
intelligence and/or
machine learning systems can be trained using a large number of medical
images. The
training and performance of such artificial intelligence and/or machine
learning systems
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can be improved when the medical images are all normalized and calibrated to
the same or
similar relative scale.
[0849]
Additionally, the normalization device can be used to normalize or
calibrate a medical image such that pixels within the medical image
representative of
various materials can be normalized and calibrated to materials of known
absolute density.
For example, when analyzing an image of a coronary region of to characterize,
for example,
calcified plaque buildup, it can be important to accurately determine which
pixels or groups
of pixels within the medical image correspond to regions of calcified plaque
buildup.
Similarly, it can be important to be able to accurately identify contrast
agents, blood, vessel
walls, fat, and other samples within the image. The use of normalization
device can
facilitate and enable identification of specific materials within the medical
image.
[0850]
The normalization devices described throughout this application can be
configured to achieve these two functions. In particular, a normalization
device can include
a substrate or body configured with compartments that hold different samples.
The
arrangement (e.g., the spatial arrangement) of the samples is known, as well
as other
characteristics associated with each of the samples, such as the material of
sample, the
volume of the sample, the absolute density of the sample, and the relative
density of the
sample relative to that of the other samples in the normalization device.
During use, in
some embodiments, the normalization device can be included in the medical
imager with
the patient, such that an image of the normalization device¨including the
known samples
positioned therein¨appears in the image. An image-processing algorithm can be
configured to recognize the normalization device within the image and use the
known
samples of the normalization device to perform the two functions described
above.
108511
For example, the image-processing algorithm can detect the known
samples within the medical image and use the known samples to adjust the
medical image
such that it uses a common or desired relative spectrum. For example, if the
normalization
device includes a sample of calcium of a given density, then that sample of
calcium will
appear with a certain grayscale value within the image. Due to the various
different
conditions under which the medical image was taken, however, the particular
grayscale
value within the image will likely not correspond to the desired relative
spectrum. The
image-processing algorithm can then adjust the grayscale value in the image
such that it
falls at the appropriate location on the desired relative spectrum. At the
same time, the
image-processing algorithm can adjust other pixels within the image that do
not correspond
to the normalization device but that share the same grayscale value within the
medical
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image, such that those pixels fall at the appropriate location on the desired
relative
spectrum. This can be done for all pixels in the image. As noted previously,
this
transformation may not be linear. Once complete, however, the pixels of the
medical image
will be adjusted such that they all fall on the desired relative grayscale
spectrum. In this
way, two images of the same subject captured under different conditions, and
thus initially
appearing differently, can be adjusted so that they appear the same (e.g.,
appearing on the
same relative grayscale spectrum).
[0852]
Additionally, the normalization device can be used to identify particular
materials within the medical image. For example, because the samples of the
normalization
device are known (e.g., known material, volume, position, absolute density,
and/or relative
density), pixels representative of the patient's anatomy can be compared
against the
materials of the normalization device (or a scale established by the materials
of the
normalization device) such that the materials of the patient's anatomy
corresponding to the
pixels can be identified. As a simple example, the normalization device can
include a
sample of calcium of a given density. Pixels that appear the same as the
pixels that
correspond to the sample of calcium can be identified as representing calcium
having the
same density as the sample.
[0853]
In some embodiments, the normalization device is designed such that
the samples contained therein correspond to the disease or condition for which
the resulting
image will be analyzed, the materials within the region of interest of the
patient's anatomy,
and/or the type of medical imager that will be used. By using a normalization
device within
the image, the image-processing algorithms described throughout this
application can be
easily expanded for use with other imaging modalities, including new imaging
modalities
now under development or yet to be developed. This is because, when these new
imaging
modalities come online, suitable normalization devices can be designed for use
therewith.
[0854]
Further, although this application primarily describes use of the
normalization device for diagnosis and treatment of coronary conditions, other
normalization devices can be configured for use in other types of medical
procedures or
diagnosis. This can be done by selecting samples that are most relevant to the
procedure
to be performed or disease to be analyzed.
[0855]
The normalization devices described in this application are
distinguishable from conventional phantom devices that are commonly used in
medical
imaging applications. Conventional phantom devices are typically used to
calibrate a
medical imager to ensure that it is working properly. For example,
conventional phantom
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devices are often imaged by themselves to ensure that the medical image
produces an
accurate representation of the phantom device. Conventional phantom devices
are imaged
periodically to verify and calibrate the machine itself These phantom devices,
are not,
however, imaged with the patient and/or used to calibrate or normalize an
image of the
patient.
[0856]
In contrast, the normalization device is often imaged directly with the
patient, especially where the size of the normalization device and the imaging
modality
permit the normalization device and the patient to be imaged concurrently. If
concurrent
image is not possible, or in other embodiments, the normalization device can
be imaged
separately from the patient. However, in these cases, it is important that the
image of the
patient and the image of the normalization device be imaged under the same
conditions.
Rather than verifying that the imaging device is functioning properly, the
normalization
device is used during an image-processing algorithm to calibrate and normalize
the image,
providing the two functions discussed above.
[0857]
To further illustrate the difference between conventional phantom
devices and the normalization device, it will be noted that use of the
normalization device
does not replace the use of a conventional phantom. Rather, both may be used
during an
imaging procedure. For example, first, a conventional phantom can be imaged
alone. The
resulting image of the phantom can be reviewed and analyzed to determine
whether the
imaging device is correctly calibrated. If it is, the normalization device and
the patient can
be imaged together. The resulting image can be analyzed to detect the
normalization device
within the image, adjust the pixels of the image based on the representation
of the
normalization device within the image, and then, identify specific materials
within the
image using the normalization device as described above.
[0858]
Several embodiments of normalization devices have been described
above with reference to Figures 12A-121. Figure 15 present another embodiment
of a
normalization device 1500. In the illustrated embodiment, the normalization
device 1500
is configured for use with medical images of a coronary region of a patient
for analysis and
diagnosis of coronary conditions; however, the normalization device 1500 may
also be used
or may be modified for use with other types of medical images and for other
types of
medical conditions. As will be described below, in the illustrated embodiment,
the
normalization device 1500 is configured so as to mimic a blood vessel of a
patient, and thus
may be particularly suitable for use with analysis and diagnosis of conditions
involving a
patient's blood vessels.
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[0859]
As shown in Figure 15, the normalization device 1500 comprises a
substrate having a plurality of compartments holding samples formed therein.
In the
illustrated embodiment, the samples are labeled A1-A4, B1-B4, and C1-C4. As
shown in
Figure 15, the samples A1-A4 are positioned towards the center of the
normalization device
1500, while the samples B1-B4 and C1-C4 are generally arranged around the
samples Al -
A4. For each of the samples, the material, volume, absolute density, relative
density, and
spatial configuration is known.
[0860]
The samples themselves can be selected such that normalization device
1500 generally corresponds to a cross-sectional blood sample. For example, in
one
embodiment, the samples A1-A4 comprise samples of contrast agents having
different
densities or concentrations. Examples of different contrast agents have been
provided
previously and those contrast agents (or others) can be used here. In general,
during a
procedure, contrast agents flow through a blood vessel. Accordingly, this can
be mimicked
by placing the contrast agents as samples A1-A4, which are at the center of
the
normalization device. In some embodiments, one or more of the samples A1-A4
can be
replaced with other samples that may flow through a blood vessel, such as
blood.
[0861]
The samples B1-B4 can be selected to comprise samples that would
generally be found on or around an inner blood vessel wall. For example, in
some
embodiments, one or more of the samples B1-B4 comprise samples of calcium of
different
densities, and/or one or more of the samples of Bl-B4 comprise samples of fat
of different
densities. Similarly, the samples Cl -C4 can be selected to comprise samples
that would
generally be found on or around an outer blood vessel wall. For example, in
some
embodiments, one or more of the samples CI-C4 comprise samples of calcium of
different
densities, and/or one or more of the samples of Cl-C4 comprise samples of fat
of different
densities. In one example, the samples Bl, B3, and C4 comprise fat samples of
different
densities, and the samples B2, B4, Cl, C2, and C3, comprise calcium samples of
different
densities. Other arrangements are also possible, and, in some embodiments, one
or more
of the compartments may hold other samples, such as, for example, air, tissue,
radioactive
contrast agents, gold, iron, other metals, distilled water, water, or others.
[0862]
The embodiment of the normalization device 1500 of Figure 15, further
illustrates several additional features that may be present in some
normalization devices.
One such feature is represented by the different sized compartments or volumes
for the
samples. For example, in the illustrated embodiment the sample B1 has a
smaller volume
than the sample B2. Similarly, the sample C4 has a volume that is larger than
the sample
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C3. This illustrates that, in some embodiments, the volumes of the samples
need to be all
of the same size. In other embodiments, the volumes of the samples may be the
same size.
[0863]
The embodiment of Figure 15 also illustrates that various samples can
be placed adjacent to (e.g., immediately adjacent to or juxtaposed with) other
samples. This
can be important because, in some cases of medical imaging, the radiodensity
of one pixel
may affect the radiodensity of an adjacent pixel. Accordingly, in some
embodiments, it
can be advantageous to configure the normalization device such that material
samples that
are likely to be found in proximity to each other are similarly located in
proximity to or
adjacent to each other on the normalization device. The blood vessel-like
arrangement of
the normalization device 1500 may advantageously provide such a configuration.
[0864]
In the illustrated embodiment, each sample A1-A4 is positioned so as to
be adjacent to two other samples A1-A4 and to two samples B1-B4. Samples C1-C4
are
each positioned so at to be adjacent to two other samples C1-C4 and to a
sample Bl-B4.
Although a particular configuration is illustrated, various other
configurations for placing
samples adjacent to one another can be provided. Although the normalization
device 1500
is illustrated within a plane, the normalization device 1500 will also include
a depth
dimension such that each of the samples A1-A4, B1-B4, and C1-C4 comprises a
three-
dimensional volume.
[0865]
As noted previously, the normalization device can be calibrated
specifically for different types of medical imagers, as well as for different
types of diseases.
The described embodiment of the normalization device 1500 may be suitable for
use with
CT scans and for the analysis of coronary conditions.
[0866]
When configuring the normalization device for use with other types of
medical imagers, the specific characteristics of the medical imager must be
accounted for.
For example, in an MRI machine, it can be important to calibrate for the
different depths
or distances to the coils. Accordingly, a normalization device configured for
use with MRI
may have a sufficient depth or thickness that generally corresponds to the
thickness of the
body (e.g., from front to back) that will be imaged. In these cases, the
normalization device
can be placed adjacent to the patient such that a top of the normalization
device is positioned
at the same height as the patient's chest, while the bottom of the
normalization device is
positioned at the same height as the patient's back. In this way, the
distances between the
patient's anatomy and the coils can be mirrored by the distances between the
normalization
device and the coils.
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[0867]
In some embodiments, the sample material can be inserted within tubes
positioned within the normalization device.
[0868]
As noted previously, in some embodiments, the normalization device
may be configured to account for various time-based changes. That is, in
addition to
providing a three-dimensional (positional) calibration tool, the normalization
device may
provide four-dimensional (positional plus time) calibration tool. This can
help to account
for changes that occur in time, for example, as caused by patient movement due
to
respiration, heartbeat, blood flow, etc. To account for heartbeat, for
example, the
normalization device may include a mechanical structure that causes it to beat
at the same
frequency as the patient's heart. As another example of a time-based change,
the
normalization device can be configured to simulate spreading of a contrast
agent through
the patient's body. For example, as the contrast agent is injected into the
body, a similar
sample of contrast agent can be injected into or ruptured within the
normalization device,
allowing for a time-based mirroring of the spread.
[0869]
Accounting for time-based changes can be particularly important where
patient images are captured over sufficiently large time steps that, for
example, cause the
image to appear blurry. In some embodiments, artificial intelligence or other
image-
processing algorithms can be used to reconstruct clear images from such blurry
images. In
these cases, the algorithms can use the normalization device as a check to
verify that the
transformation of the image is successful. For example, if the normalization
device (which
has a known configuration) appears correctly within the transformed image,
then an
assumption can be made that the rest of the image has been transformed
correctly as well.
Medical Reports Overview
[0870]
Traditional reporting of medical information is designated for physician
or other provider consumption and use. Diagnostic imaging studies, laboratory
blood tests,
pathology reports, EKG readings, etc. are all interpreted and presented in a
manner which
is often difficult to understand or even unintelligible by most patients. The
text, data and
images from a typically report usually assumes that the reader has significant
medical
experience and education, or at least familiarity with medical jargon that,
while
understandable by medical professionals, are often opaque to the non-medical
layperson
patient. To be concise, the medical reports do not include any sort of
background
educational content and it assumes that the reader has formal medical
education and
understands the meaning of all of the findings in the report as well as the
clinical
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implications of those findings for the patient. Further, often findings are
seen in concert
with each other for specific disease states (e.g., reduced ejection fraction
is often associated
with elevated left ventricular volumes), and these relationships are not
typically reported
as being as part of a constellation of symptoms associated with a disease
state or syndrome,
so the non-medical layperson patient cannot understand the relationship of
findings to
his/her disease state.
[0871]
It is then the responsibility and role of the medical provider to
"translate"
the reports into simple language which is typically verbally communicated with
the patient
at the time of their encounter with the provider be it in person or more
recently during
telehealth visits. The provider explains what the test does, how it works,
what its limitations
may be, what the patient's results were and finally what those results might
mean for the
patient's future. Unfortunately, patients frequently are unable to fully
interpret and retain
all the information that the provider might discuss with them in a short 10-15
typical patient
encounter. The patients are then left confused and only partly educated on the
results of
their medical reports. Often the provider will give the patient a copy of the
report both for
their records as well as to be able to review on their own after the patient
encounter.
[0872]
Even with the patient report in hand and after hearing the physician's
explanation, the patient often remains incompletely informed regarding the
results and their
meaning. This can be a major source of frustration for both the provider as
well as the
patient. The patient does not understand fully the results of the study and
their implications.
Frequently patients will either reach out to friends and family to help
understand the results
of their examination or they will perform searches on the Internet for
additional background
education and meaning. Frequently however this is not successful as the
patient may not
understand even what they are supposed to be searching for or asking about the
disease
process and many online health information sites maybe inaccurate or
misleading. All of
this can impact current medical status of the patient, his relation with the
health provider,
but also future health implications including but not only therapeutic and
future diagnostic
test adherence.
[0873]
In response to this, providers sometimes refer patients to websites or
provide them with written materials that may help explain their test findings
and how this
may relate to disease. But these are -generic" material that are not patient-
specific, do not
incorporate patient specific findings, and do not relate to a patient's
specific conditions or
symptoms. To date, however, no methods have been devised or described that
combines
patient facing educational content as well as the patient's specific
individual report findings
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in a way that can be easily accessed, reviewed, and is available at the
patients leisure for
repeated consumption as they may require. Thus, it is advantageous for systems
and
methods that enable communication of these findings beyond a simple paper
report by
leveraging patient-specific information for generation of reports in the forms
of more
advanced and contemporary technology, such as movies, mixed reality or
holographic
environments.
[0874]
Various aspects of systems and methods of generating a medical report
dataset and a corresponding medical report for a specific patient are
disclosed herein. In
one example, a process includes receiving selection of a report generation
request, for a
patient, for display on a display of a computing system having one or more
computer
processors and one or more displays, receiving patient information from a
patient
information source storing said patient information, the patient information
associated with
the report generation request, determining patient characteristics associated
with the report
generation request based on the patient information, accessing a data
structure storing
associations between patient characteristics and respective patient medical
information,
medical images, and test results of one or more test performed on the patient,
and storing
associations between patient characteristics and multimedia report data that
is not related
to a specific patient, selecting from the data structure a report package
associated with the
patient medical information and the report generation request, wherein the
selected report
package comprises a patient greeting in the language of the patient and
presented by an
avatar selected based on the patient data, a multimedia presentation conveying
an
explanation of the test performed, of the results of the test, an explanation
of the results of
the test, and a conclusion segment presented by the avatar, wherein at least a
portion of the
multimedia presentation includes report multimedia data from the report data
source, test
results from the results information source, medical information from the
medical
information source, and medical images related to the test from the medical
image source,
automatically generating the selected report package, and displaying the
selected report
package on the one or more displays, wherein the selected reports are
configured to receive
input from a user of the computing system that is usable in interacting with
the selected
parent report.
[0875]
Systems for generating medical report can utilize existing patient
medical information, new images and test data, and/or contemporaneous
information of the
patient received from, for example, the medical wearable device monitoring one
or more
physiological conditions or characteristics of the patient. Such systems can
be configured
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to automatically generate a desired report. In some embodiments, the systems
may use
medical practitioner and/or patient interactive inputs to the determine
certain aspects to
include in the medical report. In one example, a system for automatically
generating a
medical report can include a patient information source providing stored
patient
information patient information format, a medical information source providing
medical
information in a medical information format, and a medical image source
providing
medical images in a medical image format. The medical images can be any images
depicting a portion of a patient's anatomy, for example, an arterial bed, one
or more arterial
beds. In an example, an arterial bed includes arteries of one of the aorta,
carotid arteries,
lower extremity arteries, renal arteries, or cerebral arteries. The medical
images can be any
images depicting one or more arterial beds. In an example, a first arterial
bed includes
arteries of one of the aorta, carotid arteries, lower extremity arteries,
renal arteries, or
cerebral arteries, and a second arterial bed includes arteries of one of the
aorta, carotid
arteries, lower extremity arteries, renal arteries, or cerebral arteries that
are different than
the arteries of the first arterial bed. In some embodiments, a normalization
device (e.g., as
described herein) is used when generating the medical images, and the
information from
the normalization device is used when processing the medical images. The
medical images
can be processes using any of the methods, processes, and/or systems described
herein, or
other methods, processes, and/or systems. Any of the methods described herein
can be
based on imaging using the normalization device to improve quality of the
automatic image
assessment of the generated images_ The system for automatically generating a
medical
report can also include a test results information source providing test
results of one or more
test performed on the patient in a results information format, a report data
source, the report
data source providing multimedia data for including in a medical report, the
multimedia
data indexed by at least some of the stored patient information relating to
non-medical
characteristics of the patient, a report generation interface unit to receive
said patient
information, the patient information including non-medical characteristics of
a patient
including characteristics indicative of the patients age, gender, language,
race, education
level, and/or culture, and the like, wherein said report generation interface
unit can be
adapted to automatically create medical report data links associated with said
patient
characteristics and associated with report multimedia data on the report data
source that is
indexed by said respective patient characteristics based on a received report
generation
request associated with the patient and a test, and wherein the report
generation interface
unit is further adapted to automatically create links to patient information,
medical
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information, medical images, and test results associated with the patient and
the test based
on the report generation request. The system further includes a medical report
dataset
generator adapted to automatically access and retrieve the report multimedia
data, patient
information, medical information, medical images, the test results using the
medical report
data links, and automatically generate a medical report associated with the
test and the
patient based on the report multimedia data, patient information, medical
information,
medical images, the test results, the medical report conveying a patient
greeting in the
language of the patient and presented by an avatar selected based on the
patient data, a
multimedia presentation conveying an explanation of the test performed, of the
results of
the test, an explanation of the results of the test, and a conclusion segment
presented by the
avatar, wherein at least a portion of the multimedia presentation includes
report multimedia
data from the report data source, test results from the results information
source, medical
information from the medical information source, and medical images related to
the test
from the medical image source.
[0876]
As described herein, one innovation relates to generating interactive
medical data reports. More particularly, the present application describes
methods and
systems for generating interactive coronary artery medical reports that are
optimized for
interactive presentation and clearer understanding by the patient. One
innovation includes
a method of generating a medical report of a medical test associated with one
or more
patient tests. The method can include receiving an input of a request of a
medical report to
generate for a particular patient, the request indicating a selection of a
format of the medical
report, and receiving patient information from a patient information source
storing said
patient information, where the patient information is associated with the
report generation
request. The method can include determining patient characteristics associated
with the
patient based on the patient information, and accessing one or more data
structures storing
associations of types of medical reports, patient characteristics and
respective patient
medical information, medical images, and test results of one or more test
performed on the
patient. The data structures are structured to store associations between
patient
characteristics and multimedia report data that is not related to a specific
patient. Such
methods can include accessing report content associated with the patient's
medical
information and the medical report request using the one or more data
structures.
[0877]
The content of the medical report can include multimedia content
including a greeting in the language of the patient, an explanation segment of
a type of test
conducted, a results segment for conveying test results, an explanation
segment explaining
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results of the test, and a conclusion segment, wherein at least a portion of
the multimedia
content includes report data from the report data source, test results from
the results
information source, medical information from the medical information source,
and medical
images related to the test from the medical image source. Such methods can
also include
automatically generating the requested medical report using the accessed
report content
based at least in part on the selected format of the medical report. Such
methods can also
include displaying the medical report to the patient. In some embodiments, the
multimedia
information further comprises data for generating and displaying an avatar on
a display, the
avatar being included in the medical report. In some embodiments, the method
further
comprising generating the avatar based on one or more patient characteristics.
In some
embodiments, the patient characteristics include one or more of age, race, and
gender.
[0878]
In some embodiments of such methods, a method can include displaying
the medical report on one or more displays of a computer system, receiving
user input while
the medical report can be displayed, and changing at least one portion of the
medical report
based on said received user input. In some embodiments, displaying the medical
report
comprises displaying the medical report on the patient's smart device. In some
embodiments, the method includes storing the medical report. In some
embodiments, the
one or more data structures is configured to store information representative
of the severity
of the patient's medical condition, wherein selection of the content of the
segments of the
medical report are based on in part on the stored information representative
of the severity
of the patient's medical condition.
[0879]
Such methods can also include selecting a greeting segment for the
medical report based on one or more of the patient's race, age, gender,
ethnicity, culture,
language, education, geographic location, and severity of prognosis. The
method can also
include selecting multimedia content for the explanation segment based on one
or more of
the patient's race, age, gender, ethnicity, culture, language, education,
geographic location,
and severity of prognosis. The method can also include selecting multimedia
content for
the explanation of the results segment based on one or more of the patient's
race, age,
gender, ethnicity, culture, language, education, geographic location, and
severity of
prognosis. The method can also include selecting multimedia content for the
conclusion
segment based on one or more of the patient's race, age, gender, ethnicity,
culture,
language, education, geographic location, and severity of prognosis. In some
embodiments,
the one or more data structures are configured to store associations related
to normality,
risk, treatment type, and treatment benefit of medical conditions, and wherein
the method
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further includes automatically determining normality, risk, treatment type,
and treatment
benefit to include in the report based on the patients test results, and the
stored associations
related to normality, risk, treatment type, and treatment benefits. In some
embodiments, the
method can further include generating an updated medical report based on a
previously
generated medical report, new test results, and an input by a medical
practitioner.
Example System and IVIethod for Automatically Generating Coronaty Artety
Medical
Data
[0880]
Described herein are systems and methods for generating medical
reports that provides an in-depth explanation of what the medical test or
examination was
intended to look for, the results of the patient's specific medical findings,
and what those
findings may mean to the patient. The medical reports can be automatically
generated,
understandable educational empowering movie of individualized adapted personal
aggregated medical information. As an example, a computer implemented method
of
generating a multi-media medical report for a patient, the medical report
associated with
one or more tests of the patient. One or more images used to determine
information for the
medical report, and/or one or more of the images used in the medical report,
can be based
on images generated using a normalization device described herein, the
normalization
device improving accuracy of the non-invasive medical image analysis. In an
example, a
method comprises receiving an input of a request to generate the medical
report for a
patient, the request indicating a format for the medical report, receiving
patient information
relating to the patient, the patient information associated with the report
generation request,
determining one or more patient characteristics associated with the patient
using the patient
information, accessing associations between types of medical reports and
patient medical
information, wherein the patient medical information includes medical images
relating to
the patient and test results of one or more test that were performed on the
patient, the
medical images generated using the normalization device, and accessing report
content
associated with the patient's medical information and the medical report
requested. The
report content can include multimedia content that is not related to a
specific patient. For
example, the multimedia content can include a greeting segment in the language
of the
patient, an explanation segment explaining a type of test conducted, a results
segment for
conveying test results, and an explanation segment explaining results of the
test, and a
conclusion segment, wherein at least a portion of the multimedia content
includes a test
result and one or more medical images that are related to a test performed on
the patient.
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The method can further include generating, based at least in part on the
format of the
medical report, the requested medical report using the patient information and
report
content.
108811
Certain components of certain embodiments of such systems and
methods are described herein. An example of cardiac CT study imaging in a
single
examination is provided.
1) Transform individual patient specific medical information into an
understandable movie. This invention combines patient facing medical
education with patient specific medical results in a manner that has not been
previously performed. While many online sites explain medical disease
processes, they do not have the results of the patients' medical tests and the
patients often do not know if they are even looking in the right area. By
combining patient facing educational background as well as specific
analysis of their test results and meaning, the patients will be educated in a
manner that empowers them to make better health decisions. This approach
can then combine additional materials beyond just the present test findings,
including additional information derived from patient history, physical,
clinical electronic medical record, wearable fitness and wellness trackers,
patient-specific web browser search history and so on.
2) Provide an in-depth explanation of the test performed. To understand
what the results of a test may be, patients must understand what the test was
intended to do, an explanation regarding how it works, as well as the
potential range of results, both normal and abnormal. An explanation of the
test performed would include simple understandable methods of what the
test is intended to find and what the range of possibilities of the results
may
be. In the example provided a coronary artery CT angiogram is intended to
evaluate if there are blockages or plaque within the patient's coronary
arteries. In order to understand the results, a patient needs to understand
that
the test is intended to evaluate the blood vessels that feed the heart muscle,
that by injecting contrast and doing CT images their coronary arteries can
be evaluated for the presence of plaque and associated blockages. This
understanding can be conveyed to the patient using a patient's actual images
so that there is increased engagement and understanding.
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3) Provide the results of the patient's individual patient specific
examination. Having educated the patient regarding what test they had as
well as the range of all possible results, they are now better empowered to
understand what their specific results are in the context of the range of
potential results from the examination. Combining the results of the patient's
findings with an explanation of what the test was looking for enables the
patient to better understand the meaning of those results. The patient's
individual results, whether they are quantitative values from a blood test,
images and resulting interpretation from a diagnostic imaging study such as
CT, MRI, ultrasound etc., results from an ECG exam etc. Quantitative
results, images, PDFs, or other results can be uploaded and presented within
the movie.
4) Give explanations of the results. In addition to presenting the results
directly to the patient, an explanation of the meaning of the results can then
be presented simultaneously. This is performed using defined aggregation
algorithms with previously recorded definitions and discussions of the range
of results expected for an individual test. For example, in the case of the
cardiac CT angiogram report, we will develop short explanations of the
significance of the result of narrowing of a blood vessel. If there is no
narrowing present then a short, animated video discussion will explain that
no narrowing was present and what that means, if there is a mild narrowing
which is clinically defined as a narrowing between one and 24%, then a
different video will be played. If the narrowing is between 24 and 49%,
another video is played etc. Previously created video explanations of the
range of expected results will have been created and are available to then be
placed within the video depending on the individual results of the
examination. In some cases, there may only be a binary result, and therefore
only two explanations are necessary. In other cases, it may be many videos
depending on the initial test and the range of possible clinically significant
results. The patient specific results can sometimes even be compared to what
would be expected to an average patient of the same age and sex or to what
age that result would be considered -average - normal". Specifically, in this
step, the patient's test findings can be linked to clinical treatment or
additional diagnostic recommendations that can be based upon professional
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societal practice guidelines or contemporary research science, such as that
derived from large-scale registries and trials. In this way, this approach can
also be educational to the medical professional and may allow for improved
and contemporary clinical decision support. This will allow for a shared
decision-making moment for the patient and the medical professional,
without the need for them to read through scientific literature.
5) Use animation that is patient friendly and non-threatening. The
animation selected for the video will be intended to be professional but
friendly and non-threatening to the patient in order to put them more at ease
and make them more open to hearing and understanding the explanations.
The animated physician or other explainer in the video can also be matched
to the patience sex, age, and race and even be presented in the patient's
primary language. Alternatively, the patient's own countenance can be the
patient within the video in a manner that is from a photography or,
alternatively, rendered as a cartoon or avatar.
6) Can be delivered via web based and non-web-based methods. The
method of delivery to the patient can be via encrypted HIPAA compliant
web-based methods or non-web-based methods such as computer disks,
other storage media, etc.
7) Can be viewed on computers, cell phones, and other devices. In this
manner, all patients will have access to the reports regardless of their
socioeconomic status. Not all patients have access to the Internet, cell
phones or other devices. Making it available on multiple media platforms
increases the degree of access.
8) Uses mixed reality for explanations. The use of advanced computer
graphics an augmented or virtual reality may make some of the explanations
easier for the patients to understand. For example, a virtual reality trip
into
the body and through a blood vessel then demonstrating the blood flow
slowing down and/or stopping at the sight of a blockage will help the patient
to understand the significance of having that blockage in their body.
Demonstrating the deployment of a stent in that blood vessel at the sight of
that blockage will then help the patient understand how their pathology may
be treated and why. This could also be done in a 3D/4D virtual reality
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manner; or as a hologram; or by other visual display. Similarly, this
information can be conveyed by audio methods, such as a podcast or others.
9) Can be saved by the patient for future reference. The patient specific
movie containing an explanation of the test, their results and additional
information becomes property of the patient that they can store for future
use.
10) Can be compared to a normal reference population value. In some cases,
there may be findings that, to maximize patient understanding, can be
compared to normative reference values that are derived from population-
based cohorts or other disease cohorts. This may be provided in percentile,
by age comparison (e.g., heart age versus biological age), or by visual
display (e.g., on a bell-shaped curve or histogram).
11) Can be compared to prior studies. In some cases, the patient may have 2
studies (either the same test, e.g., CT-CT or different tests CT-ultrasound)
that can be automatically compared for differences and reported as
described above in #1-10. This will allow a patient to understand his/her
progress over time in response to lifestyle or medical therapy or
interventional therapies. In other cases, the test findings can be conveyed as
in #1-10 as a function of heritability (e.g., from genomics or other omics or
family history), susceptibility (e.g., from lab markers over time, or from
environmental lifestyle insults, such as smoking).
12) Can be configured to communicate the likelihood of success. In some
cases, the video generated will estimate the likelihood of success or failure
of any given intervention by calculating the likelihood through risk
calculators or using clinical trial data or practice guidelines; and this can
be
reported in the movie.
Examples of Medical Report Generation Systems and Methods
[0882]
Figure 16 is a system diagram which shows various components of an
example of a system 1600 for automatically generating patient medical reports,
for
example, patient medical reports based on CT scans and analysis, utilizing
certain systems
and methods described herein. Various embodiments of such systems may include
fewer
components than is shown in Figure 16, additional components, or different
components.
In this example, the system 1600 includes an MRI scanner 16160, an ultrasound
scanner
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1611, the CT scanner 1612, and other types of imaging devices 1613.
Information from
scanners and imaging devices is provided to other components of the system
through one
or more communication links 1601 or other communication mechanism for
communicating
information. The communication link is also connected to other components the
system
illustrated in Figure 16.
[0883]
The system 1600 further includes archived patient medical information
and records 1602 which may have been collected in a variety of sources and
over a period
of time. The information and records may include patient data 1604, patient
results 1606,
patient images 1608, (e.g., stored images of CT scans, ultrasound scans. MR1
scans, or
other imaging data.
[0884]
The system 1600 further includes stored images 1614 (which may or
may not be patient related). The system 1600 further includes patient wearable
information
1616 which may be collected one or more devices worn by patient, devices
sensing or
measuring one or more types of physiological data or a characteristic of the
patient,
typically over a period of time. The system 1600 can further include
laboratory data 1618
(e.g., recent blood analysis results), and medical practitioner analysis 1620
of any patient
related data (e.g., images, laboratory data, wearable information, etc.). The
system 1600
may communicate with other systems and devices over a network 1650 which is in
communication with communication links 1601.
[0885]
System 1600 may further include a computing system 1622 which may
be used perform any of the functionality related to communicating, analyzing,
gathering,
or viewing information on the system 1600. The computing system 1622 can
include a bus
(not shown) that is coupled to the illustrated components of the computing
system 1622
(e.g., processor 1624, memory 1628, display 1630, interfaces 1632,
input/output devices
1634, communication link 1601, and may also be coupled to other components of
the
computing system 1622. The computing system 1622 may include a processor 1624
or
multiple processors for processing information and executing computer
instructions.
Hardware processor 1624 may be, for example, one or more general purpose
microprocessors. Computer system 1622 also includes memory (e.g., a main
memory)
1628, such as a random-access memory (RAM), cache and/or other dynamic storage
devices, for storing information and instructions to be executed by processor
1624. Memory
1628 also may be used for storing temporary variables or other intermediate
information
during execution of instructions to be executed by processor 1624. Such
instructions, when
stored in storage media accessible to processor 1624, render computer system
1622 into a
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special-purpose machine that is customized to perform the operations specified
in the
instructions. The memory 1628 may, for example, include instructions to allow
a user to
manipulate time-series data to store the patient information and medical data,
for example
as described in reference to Figure's 16 and 17. The memory 1628 can include
read only
memory (ROM) or other static storage device(s) coupled in communication with
the
processor 1624 storing static information and instructions for processor 1624.
Memory
1628 can also include a storage device, such as a magnetic disk, optical disk,
or USB thumb
drive (Flash drive), etc., coupled the processor 1628 and configured for
storing information
and instructions.
108861
The computer system 1622 may be coupled via a bus to a display 1630,
for example, a cathode ray tube (CRT), light emitting diode (LED), or a liquid
crystal
display (LCD). The display may include a touchscreen interface. The computing
system
1622 may include an input device 1634, including alphanumeric and other keys,
is coupled
to bus for communicating information and command selections to processor 1622.
Another
type of user input device is cursor control, such as a mouse, a trackball, or
cursor direction
keys for communicating direction information and command selections to
processor 1622
and for controlling cursor movement on display 1630. The input device
typically has two
degrees of freedom in two axes, a first axis (e.g., x) and a second axis
(e.g., y), that allows
the device to specify positions in a plane. In some embodiments, the same
direction
information and command selections as cursor control may be implemented via
receiving
touches on a touch screen without a cursor.
[0887]
Computing system 1622 may include a user interface module 1632 to
implement a GUI that may be stored in a mass storage device as computer
executable
program instructions that are executed by the computing device(s). Computer
system 1622
may further, implement the techniques described herein using customized hard-
wired logic,
one or more ASICs or FPGAs, firmware and/or program logic which in combination
with
the computer system causes or programs computer system 1622 to be a special-
purpose
machine. According to one embodiment, the techniques herein are performed by
computer
system 1622 in response to processor(s) 1624 executing one or more sequences
of one or
more computer readable program instructions contained in memory 1628. Such
instructions
may be read into memory 1628 from another storage medium. Execution of the
sequences
of instructions contained in the memory 1628 causes processor(s) 1624 to
perform the
process steps described herein. In alternative embodiments, hard-wired
circuitry may be
used in place of or in combination with software instructions.
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[0888]
Various forms of computer readable storage media may be involved in
carrying one or more sequences of one or more computer readable program
instructions to
processor 1624 for execution. The instructions received by memory 1628 may
optionally
be stored before or after execution by processor 1624.
[0889]
Computer system 1622 also includes a communication interface 1637
coupled to other components of the computer system and to communication link
1601.
Communication interface 1637 provides a two-way data communication coupling to
a
network link that is connected to a communication link 1601. For example,
communication
interface 1637 may be an integrated services digital network (ISDN) card,
cable modem,
satellite modem, or a modem to provide a data communication connection to a
corresponding type of telephone line. As another example, communication
interface 1637
may be a local area network (LAN) card to provide a data communication
connection to a
compatible LAN (or WAN component to communicate with a WAN). Wireless links
may
also be implemented. In any such implementation, communication interface 1637
sends
and receives electrical, electromagnetic or optical signals that carry digital
data streams
representing various types of information.
[0890]
A network link typically provides data communication through one or
more networks to other data devices. For example, a network link may provide a
connection
through local network to a host computer or to data equipment operated by an
Internet
Service Provider (ISP). An ISP in turn provides data communication services
through the
worldwide packet data communication network now commonly referred to as the
"Internet." Computer system 1622 can send messages and receive data, including
program
code, through the network(s), communication link 1601 and communication
interface
1637. In the Internet example, a server might transmit a requested code for an
application
program through the Internet, ISP, local network communication link 1601, and
a
communication interface. The received code may be executed by processor 1624
as it is
received, and/or stored in memory 1628, or other non-volatile storage for
later execution.
The processor 1624, operating system 1626, memory components 1628, one or more
displays 1630, one or more interfaces 1632, input devices 1634, and modules
1636, which
may be hardware or software, or a combination of hardware and software, that
when
utilized performs functionality for the system. For example, the modules 1626
may include
computer executable instructions that are executed by processor 1624 to
perform the
functionality of system 1600.
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108911
The system 1600 may further include medical report generation system
1638 ("or medical report generator") which can include various components that
are used
to generate medical report data set for a particular patient for a requested
type of report.
Medical report generation system 1638 may include a computing system, e.g., a
server or
a computing system 1640. In some embodiments, the computing system 1640
includes a
server. The medical report generation system also includes collected or
determined patient
specific information 1648, and a report template data structure 1642 which
includes
associations between a patient, the patient information 1648 (images, medical
analysis and
test results associated with the patient), and report segments, report
elements, reports of
elements for the desired. Medical report generation system 1638 further
includes user
parameters 1646 that may be specific to a medical practitioner and/or to a
patient or entered
by a medical practitioner and/or the patient.
[0892]
The system 1600 may also include one or more computing devices 1652
communication with the components of the system via a communication link(s)
1601.
Communication link(s) 1601 may include wired and wireless links. Computing
device 1652
may be a tablet computer, laptop computer, a desktop computer, a smart phone,
or another
mobile device.
[0893]
Figure 17 is a block diagram that shows an example of data flow and
functionality 1700 for generating the patient medical report based on one or
more scans of
the patient, patient information, medical practitioner's analysis of the
scans, and/or
previous test results. At the beginning of this data flow new medical images
1702 are
received by the system or are generated by a scanner. The images can be
generated using a
normalization device described herein. Information derived from images
generated and
processed using the normalization device can be more consistent and/or
accurate, as
described herein. The images can be from a CT, MRI, ultrasound, or other type
of scanner.
The images depict a target feature of a patient's body, for example, coronary
arteries. The
images may be archived in a patient medical information storage component
1708, which
stores other types of patient data (for example, previously generated images.
patient test
results, patient specific information that can include age, gender, race, BMI,
medication,
blood pressure, heart rate, weight, height, body habitus, smoking, diabetes,
hypertension,
prior CAD, family history, lab test results, and the like). The new images
1702 are provided
for image analysis 1704, which may include analysis of the images using
artificial
intelligence / machine learning algorithms that have been trained to detect
features in
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certain characteristics in the images. Other test 1706 may also have been
conducted on the
patient (e.g., blood work or another test).
[0894]
The new images 1702, machine generated results to 1712, results
determined by medical practitioners 1714, and previous test results 1716 are
collected in a
results phase 1710, and this information is communicated to medical report
data set
generation block 1720. Other patient medical information 1718 can also be
provided to
medical report data set generation 1720. As indicated above, this information
may include,
for example, a patient's age, gender, race, BMI, medication, blood pressure,
heart rate,
weight, height, body habitus, smoking, diabetes, hypertension, prior CAD,
family history,
lab test results, and the like. In addition to the results 1710 and the other
patient medical
information 1718, medical report data set generation 1720 can also receive
report data
1728. Report data 1728 can include multimedia information used for the report.
For
example, audio, images, sequences of images (i.e., video), text, backgrounds,
avatars, or
anything else for the report that is not related to the specific patient's
medical information.
[0895]
Medical report data set generation 1720 can use the new images 1702,
the results 1710, other patient medical information 1718, and report data 1728
to generate
a medical report dataset for a requested type of report. The medical report
data set
generation 1770 can be interactive, and a medical practitioner can provide
input identified
what type of report is being generated. At block 1722, during the medical
report data set
generation, all of the information that is needed for the requested report, is
aggregated and
the medical report is generated. For example, images, patient data, and other
information
needed for the report are identified collected from the various inputs. At
block 1724, the
process uses certain patient information to tailor the report for the
particular patient. For
example, one or more characteristics of an avatar that presents information in
the report to
the patient can be identified from the patient data such that the avatar is
created to best
convey report information to the patient. In some examples, such information
includes the
gender, age, language, education, culture, and the like, characteristics of
the patient. At
block 1726, the process determines the test explanation that is best used for
the report. For
example, there may be ten different explanations for a particular test, and
one of the ten
explanations is selected for the report. The determination of the test
explanation may be
based on patient and/or the diagnosis or prognosis of results of the test. In
other words, the
same test may be explained in various ways based on what the results of the
test turned out
to be. At block 1728, the process determines results explanation. There can be
multiple
explanations for the same results, and one of the explanations the selected
port. The
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selection of the results explanation can be based on, for example, patient
information, the
substance of the results, or other information.
[0896]
At block 1730, the process determines a greeting to be used in the report.
The greeting selected for the report may be one of numerous possible
greetings. In various
embodiments, the greeting may be selected based on patient information, user
input, or the
results the test. For example, if the test results indicate great news for the
patient, a first
type of greeting may be selected. If the test results are unfavorable to the
patient, a second
type of greeting may be selected is more appropriate for subsequently
delivered results.
[0897]
At block 1732, the process determines the conclusion to be used in the
report. The conclusion selected for the report may be one of numerous possible
conclusions.
In various embodiments, inclusion may be selected based on patient
information, user
input, or the results of the test. For example, the test results indicate
great is for the patient
the first type of the selected. The test results are unfavorable to the
patient, the second type
of conclusion selected is more appropriate for the previously reported
unfavorable results.
[0898]
The medical report data set generation 1720 provides a medical report
1736. In some embodiments, the medical report is a video that includes a
patient
identification greeting 1738, and for each test, an explanation of the test
1740 results of the
test 1742 and explanation of the results 1744. For medical reports that
include multiple
tests, the report may iteratively present a test explanation, present the
results, and present
an explanation results for each test conducted. The medical report also
includes a
conclusion segment 1746. In some embodiments, the medical report is displayed
on the
display to the patient/patient's family. In some embodiments, the medical
report is provided
as a video for the patient to view at their home or anywhere else on a
computer. In some
embodiments, medical report can be provided is a paper copy.
[0899]
Figure 18A is a block diagram of an example of a first portion of a
process for generating medical report using the functionality and data
described in
reference to Figure 17, according to some embodiments. At block 1802, one or
more
medical tests are performed on a patient. At block 1804, results are generated
by machine
(e.g., a blood test), the train medical interpreter, and/or are
automatically/semi-
automatically determined based on artificial intelligence/machine learning
algorithms. At
block 1806, results, patient information, and other data is collected and sent
to a computer
device or network for creation of the medical report. At block 1808, results
are aggregated
with images, patient information, other data, multimedia information and the
like to
generate a medical related portion of report. At block 1810, the process
generates the video
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presenter (e.g., an avatar) of the report using certain selected patient
information, for
example, biographical data of the patient. For example, when the patient is a
child, patient
information may be used to create child avatar which presents the report to
the child. In
some embodiments, the child avatar may have been avatar pet which also helps
present the
report to the child, making the report more interesting and more fun for the
child. When the
patient is a highly educated adult, patient information may be used to create
an avatar that
is appropriate to present the report to that patient. In some embodiments, the
avatar may
mirror certain characteristics of the patient (e.g., race, age, or gender) or
be a determined
complementary avatar to certain characteristics of patient.
109001
Figure 18B is a block diagram of an example of a second portion of a
process for generating medical report using the functionality and data
described in
reference to Figure 17, according to some embodiments. At block 1812, the
process selects
a test explanation to be used for the report. The selection of the test
explanation can be
based on the patient information the severity of injury or disease, and/or the
seriousness of
the report (e.g., the final diagnosis). In one example, a certain test
explanation may be
selected from one of four test explanation videos. At block 1814, the process
selects
explanation results to be used for the report. The selection of the results
can also be based
on the patient information, severity of the injury or disease, and/or
seriousness of report
(e.g., the final diagnosis). In one example, the certain results explanation
may be selected
from one of four results explanation videos.
[0901]
Figure 18C is a block diagram of an example of a third portion of a
process for generating medical report using the functionality and data
described in
reference to Figure 17, according to some embodiments. At block 1816, the
process selects
patient identification greeting. The report and start with identification
reading of the patient
this may include a cartoon character, or avatar, reading the patient by name
and stating
what test does been explained and when it was performed, who ordered the test
and where
it was performed. At block 1818, the process explains the test conducted on
the patient. A
previously recorded segment explains, for example, the patient what test was
performed,
how it works, why it is usually ordered by a provider, and what the range of
expected results
may be. At block 1820, the report then presents the results to the patient.
The results can
include quantitative values, images, charts, videos, and other types of data
that may help to
convey the results to the patient. At block 1822, the report may present a
discussion of
results to help clarify to the patient exactly what the results mean in some
examples,
appropriate prerecorded animation of videos explains the meaning of a result.
If multiple
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tests were performed on the patient, the process may iteratively explain each
test, present
the test results, and then explain the results. At block 1824, the process
presents a
conclusion segment that may summarize information for the patient, provide
additional
information, and/or provide guidance on the next steps taken by the patient or
that will be
taken by the medical practitioner. For all the parts of the report, medical
report generation
functionality uses a combination of patient information, actual images and/or
test results,
and other multimedia information to present a comprehensive clear explanation
of each test
that was performed in the results of the test.
[0902]
Figure 18D is a diagram illustrating various portions that can make up
the medical report, and input can be provided by the medical practitioner and
by patient
information or patient input. As shown in Figure 18D, the medical practitioner
can
interactively select a type of medical report to be generated (e.g., report 1,
report 2, etc.).
Each medical report is a collection of data and information that can be
collected and
presented in various segments of the report. For example, the segments can
include a
greeting, an explanation of the test(s) performed, results, an explanation of
the results, and
a conclusion. Medical reports that include multiple tests can include multiple
segments that
present an explanation of each test performed, the results of each test, and
an explanation
of the results of each test. In some embodiments, all or portions of the
segment are
automatically generated based on patient information, types of test performed,
and the
results of each test. In some embodiments, the medical practitioner can select
or prove
information to use for each segment In some embodiments, the report can be
interactive in
a patient's input can help determine what information to use to generate a
segment or
present a portion of the report. Each segment may include a number of
elements. Each of
the elements can include one or more sub elements. For example, a segment of
test results
may include an element for each of the test results to be included in the
report. In some
embodiments, the medical practitioner can select or approve of what
information to use for
an element and/or a sub-element. In some embodiments, the elements and/or the
sub-
elements can be at least partially determined based on the patient information
and/or the
patient input. Typically, the medical practitioner can interactively select
and/or approve of
all material that is used in the report. In some embodiments, contents of the
report are based
on predetermined algorithms that use the combination of patient information,
medical tests,
medical results, and medical practitioners' preferences to determine the
elements in each
segment of the medical report.
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[0903]
Figure 18E is a schematic illustrating an example of a medical report
generation data flow and communication of data used to generate a report. As
illustrated
components and data related to the components and data illustrated in Figures
16-18D. A
medical report generator 1850 receives plurality of inputs which it uses to
generate a
particular medical report for particular patient. This medical report is
generated to educate
and inform a patient, and a patient's caregivers, of a specific patient's
medical tests and
results. This medical reporting is a process that transforms individual
medical information
in an understandable movie. The movie is made with the patient's avatar or
avatar like (e.g.,
matched by sex, age ethnicity, etc.). Viewing of the report can be done
anywhere on a
computer that a medical facility or on a patient's computer (e.g., a smart
phone, tablet,
laptop, etc.). Report may contain multimedia data audio, text, images, and/or
video. The
video may contain, cartoon, real life videos. Animation can include virtual
reality for
example video enters body, see heart pumping with blood flowing, centered at
vessels, see
blood through vessels flowing and showing plaque with changes in velocity and
flowing -
go to plaque and see its distinct types. In some embodiments, augmented
reality may be
used to simulate, age, pharmacological changes, pharmacological agents
available where
the exam is done, different degrees of disease, the effect of interventions
such as stents and
bypass, behavior changes and exercise. The report may be shareable allowing a
user able
to share with anyone with a defined time of availability or forever. For
example, it can be
transformed and condensed in a PDF, DICOM, or Word document, or another
format, for
printing. The language used in the report can be the patient's native
language. In some
embodiments, subtitles can be used for hearing impaired in native language, or
braille for
the blind. In embodiments using avatar, the avatar narration can be
individualized for the
patient, to include age, gender, ethnicity - change in patient look, level of
understanding -
change in language and depth of information.
[0904]
The medical report generator 1850 can receive input 1875 from a
medical practitioner indicating to generate a particular type of report for
particular patient.
In some embodiments, a medical practitioner can provide inputs to determine
certain
aspects of the report. For example, the medical practitioner may indicate
which image data
to use in which test results to include in the report. In another example, the
medical
practitioner can, based on the test results and/or the severity of the
diagnosis, the medical
practitioner can influence the -tone" or seriousness of the report such that
is appropriate for
reporting the test results in the diagnosis.
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[0905]
In some embodiments, the medical practitioner can provide inputs to
approve tentative automatically selected material to include in the report.
The medical
report generator 1800 in communication with data structures 1880 which store
associations
related to report generation. In some embodiments, the data structures 1880
include
associations between the particular medical practitioner and characteristics
of medical
reports that he prefers to generate. The associations may be dynamic and may
interactively
or automatically change over time. The data structures 1880 can also include
associations
that relate to all of material that can be used to generate a report. For
example, after a
medical practitioner indicates that a certain medical report generated for
certain patient, the
medical report generator 1880 receives patient information 1880 based on the
associations
data structures begins to it needs to generate the medical report.
[0906]
As illustrated in Figure 18E, medical report generator 1850 can receive
pre-existing portions of a report 1855 (segments, elements, sub-elements) that
can include
multi-media greetings, explanation of a test, presentation of results,
explanation results, and
conclusions. This material can be combined with other inputs the medical
report generator
1850 to generate the report. For example, the medical report generator 1850
can receive
patient information 1860 that includes the patient's age, gender, race,
education, ethnicity,
geographic location, in any other characteristic of pertinent information of
the patient which
may be used to tailor the medical report such that the information in the
medical report is
best conveyed to the particular patient. Medical report generator 1850 can
also receive
image data 1862 related to recent test performed on the patient (e.g., CT,
MRI, ultrasound
scans, or other image data), and/or previously collected image data 1865
(e.g., previously
collected CT, MRI, ultrasound scans, or other image data). For example, the
previously
collected image data 1865 can include image data that was taken over a period
of time (for
example, days, weeks, months, or years). The medical report generator 1850 can
also
receive other medical data 1867 including but not limited to test, results,
diagnosis of the
patient. The medical report generator 1850 can also receive multimedia report
data 1870
which is used to form portions of medical report. The multimedia report data
1870 can
include information relating to avatars, audio information, video information,
images, and
text that may be included in the report.
[0907]
The medical report can apply to and /or discuss test results - imaging
and non-imaging tests, and other medical information isolated or aggregated
with or
without therapeutic approach. For example, for a gallstone surgery, the
medical report can
aggregate information from lab tests, objective observation, medical history,
imaging tests,
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include surgery proposal, surgery explanation, virtual surgery, pathological
findings (more
important in cancer), and explain after surgery recuperation until normal life
or treatment
FUP (ex: chemotherapy in cancer). A medical report can also be educational,
and generic
and adapted to a patient, a disease, and/or a treatment, a test, and address
disease, risk
factors, treatment, behavior, and behavior changes. Some examples, medical
report can be
generated to form part of a patient's complete electronic medical record (EMR)
information. In some examples, the medical report generator 1850 can generate
a
comprehensive medical report per patient showing the patient -your medical
life movie
report."
109081
The medical report generator 1850 can be configured to generate the
medical report in many different formats. For example, a movie, augmented
reality, virtual
reality, the hologram, a podcast (audio only), a webcast (video), or for
access using an
interactive web-based portal. In some embodiments, the information generated
for the
medical report can be stored in the data structures 1880 (e.g., the data
structures 1880 can
be revised or updated to include information from any of the inputs to the
medical report
generator 1850). In some embodiments, the medical report, or the information
from the
medical report stored in the data structures 1880 can be used to determine
eligibility of the
patient for additional trials test through an auto calculation feature. In
such cases, the data
structures 1880 are configured to store information that is needed for
determining (or auto-
calculating) such eligibility, including for example information relating to
the patient's age,
gender, ethnicity, and/or race, wellness, allergies, pre-existing conditions,
medical
diagnosis, etc. In some examples, information stored in the data structures
1880 can be used
to determine whether a patient fits inclusion criteria for large-scale
randomized trials,
determine whether patient fit criteria for appropriate use criteria or
professional societal
guidelines (e.g., AHA/ACC practice guidelines), determines whether patient's
insurance
will cover certain medications (e.g., statins vs. PCSK9 inhibitors), and
determine whether
a patient qualifies for certain employee benefits (e.g., exercise program). In
some
embodiments, the information used in the data structures 1880 can be used to
determine/indicate a patient's normality, risk, treatment type and treatment
benefits, and
such information can be included in the medical report, for example, based on
medical
practitioners' preferences. Accordingly, in various embodiments, in addition
to the
predetermined video/information 1855 relating to greetings, test explanations,
results
presented, results explanation, and conclusions, the medical report generator
1850 can be
configured to generate a medical report that includes information to help the
medical
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practitioner explain the results and best way forward, the information being
based at least
in part on the patient's specific data (e.g., test data), including:
a. patient-specific findings.
b. comparison to normal values (age, gender, ethnicity, race-specific
values of
population-based norms).
c. comparison to abnormal values (e.g., comparing someone's CAD results to
database of those who experienced heart attack; or another database of
similar).
d. comparison to outcomes (e.g., identifying inclusion criteria for trials and
medication treatments therein, and auto-calculating Kaplan Meier curves or
other visual representations showing the probability of an event respective
time interval (e.g., survival rate).
e. comparison to identify benefits of treatment (e.g., auto-linking to
clinical
trials or clinical data in order to examine the relative benefits of specific
types of treatment, e.g., medication therapy with statins vs. PCSK9
inhibitors; medication treatment vs. percutaneous intervention; PCI vs.
surgical bypass).
f. calculations of previously published (or unpublished) scores, e.g.,
CONFIRM score, SYNTAX score, etc.
g. comparisons from serial studies.
h. auto-links to EMR or patient-entered data to enable patient-specific
explanation of medications and other treatments.
i. can include -test" or -quiz" at the end to promote patient engagement
and
ensure patient literacy.
j. interactive patient satisfaction surveys.
k. interactive with patients through patient input 1875, allowing a patient to
select which information they want to view and better understand.
1. ethnically, racially and gender diversity, and allow dynamic changes in
language, content based upon gender, race and ethnicity that is used to
convey report to patient; and
in. adaptations for age allowing changes in language and content based upon
age, timeframe born (millennial vs. baby boomer).
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[0909]
In some embodiments, the medical report generator 1850 can be
configured to check for updates/received updates over time (e.g., auto-
updating) such that
the medical reports change over time and include the latest available reports.
In some
embodiments, the medical report generator 1850 can communicate via a network
or web-
based portal to include information from other medical or wearable devices. In
some
embodiments, the medical report generator 1850 can be configured to provide
the patient
patient-specific education based upon published scientific evidence and
specifically
curated to the patient's medical report, and auto-update the report based upon
serial
changes.
109101
Figure 18F is a diagram illustrating a representation of an example of a
system 1881 having multiple structures for storing and accessing associated
information
that is used in a medical report, the information associated with a patient
based on one or
more of characteristics of the patient, the patient's medical condition, or an
input from the
patient and/or a medical practitioner. In some embodiments, the system 1881 is
a
representation of how the information used for generating a medical report is
stored in
systems of Figures 16, 17, or 18E. In Figure 18F, information is described as
being stored
in a plurality of databases. As used herein, a database refers to a way of
storing information
such that the information can be referenced by one or more values (e.g., other
information)
associated with stored information. In various embodiments, a "database" can
be, for
example, a database, a data storage structure, a linked list, a lookup table,
etc.). In some
embodiments, the database can be configured to store structured information
(e.g.,
information of a predetermined size, for example, a name, age, gender, or
other information
with a predetermined maximum field size). In some embodiments, database can be
configured to store structured or unstructured information (e.g., information
that may or
may not be predetermined, e.g., an image or a video). Stored information may
be associated
with any other information of the patient. For example, stored information can
be associated
with one or more of a characteristic of a patient (e.g., name, age, gender,
ethnicity,
geographic origin, education, weight, and/or height), one or more medical
conditions of a
patient, a prognosis for a patient's medical condition, medical treatments,
etc. Although the
example system 1881 in Figure 18F illustrates having 13 different databases
(e.g., for
clarity of the description), in other embodiments such systems can have more
or fewer
databases, or certain information stored in illustrated databases can be
combined with other
information and stored together in the same database.
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[0911]
System 1881 includes a communication bus 1897, which allows the
components to communicate with each other, as needed. One or more portions of
the
communication bus 1897 can be implemented as a wired communication bus, or
implemented as a wireless communication bus. In various embodiments, the
communication but 1897 includes a plurality of communication networks, or one
or more
types (e.g., a larger are network (LAN), a wide area network (WAN), the
Internet, or a local
wireless network (e.g., Bluetooth). System 1881 also includes a medical report
generator
1894, which is in communication with the communication bus 1897. The medical
report
generator 1894 is also in communication with one or more input components
1895, which
can be used for a patient and/or a medical practitioner to interface with the
medical report
generator 1894 using a computer (e.g., a desktop computer, a laptop computer,
a tablet
computer, or a mobile device, e.g., a smart phone.
[0912]
The medical report generator 1894 can communicate with any of the
databases data structures using the communication bus 1897. In various
embodiments,
medical report generator 1894 can use information from one or more of the
illustrated
databases in a workflow, for generating a patient specific report, that
includes patient
identification, patient preferences, medical image findings, patient
diagnosis,
prognostication, clinical decision making, health literacy, patient education,
image
generation/display, and post-report education.
[0913]
Patient identification is used by the medical report generator 1894 for
generating an avatar that will be included in the medical report_ For example,
to be
displayed during at least a portion of the medical report, or to be displayed
and to "present"
at least a portion of the medical report to the patient. Determining patient
information can
be based upon either active or passive methods.
Passive
[0914]
In some embodiments, a medical report generator 1894 can be
configured to automatically communicate with an electronic medical record
(EMR)
database 1893 to (for a certain patient) ascertain patient demographic
characteristics to
determine patient age, gender, ethnicity, and other potential relevant
characteristics to
understand patient biometrics (e.g., height, weight).
[0915]
In some embodiments, the medical report generator 1894 can be
configured to automatically query a proprietary or web-based name origin
database 1883
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containing names and ethnic origins of names to determine, wholly or in part,
a patient's
gender and ethnicity based on the patient's name and/or other patient
information.
Active
[0916]
In some embodiments, the medical report generator 1894 can receive
input information from an interface system 1895, and the input information can
be used to
generate portions of the medical report. For example, a patient, family/friend
member, or
medical professional can enter patient age, gender and ethnicity, and other
potential
relevant characteristics. This can be done, for example, at the time of
receiving report and
in advance of playing the report; or at the time of registration of the
patient into the system.
[0917]
In some embodiments, the medical report generator 1894 can receive a
picture of the patient through an interface system 1895, or via the
communication bus 1897,
and the picture can be used to generate portions of the medical report. For
example, a
picture of the patient can be input into the system or be taken (e.g., input
as an electronic
image, or input by scanning in a photograph), and the picture can be used by
the medical
report generator (or a system coupled to the medical report generator) to
automatically
morph the picture into a relevant avatar (e.g., relevant to the patient). The
determination of
characteristics of the avatar can done using linked image-based algorithms
that determine
or choose an avatar from a repository of avatars that exist within the data
system, the avatar
selected at least partially based on the picture of the patient.
109181
In some embodiments, a QR code can be used for all products related to
a company (e.g., Cleerly-related products) that can house information about
the patient that
can be used to generate the avatar.
[0919]
Patient Preferences. In some embodiments, in this step the medical
report generator 1902 can be configured to receive input from a patient, or a
medical
practitioner (e.g., via the interface system 1895) to identify the ideal or
desired educational
method to maximize patient understanding of the medical report. In some
embodiments,
the system generates graphical user interfaces (GUIs) that include options
that can be
selected by a patient. In some embodiments, GUIs can include one or more
fields that a
user (e.g., patient, medical practitioner, or another) can enter data related
to a preference
(e.g., the length of the report in minutes). Examples of inputs that can be
received by a
system are illustrated below:
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= Method of delivery ¨ The patient may choose to view their medical report
as a
movie, in mixed reality (AR/VR), holography, podcast. In other embodiments,
the method of delivery is determined at least in part by patient information.
= Length of report. Some patients are more detailed than other, and would
like
more vs. less information. Patients can select the length of their report
(e.g., <5
minutes, 5-10 minutes, >10 minutes). In other embodiments, the length of the
report is determined automatically at least in part using patient information.
= Popularity of report. If patients do not know what type of report they
want, the
patients can select the "most popular- options. In other embodiments, the type
of report is determined automatically at least in part using patient
information.
= Effectiveness of the report. If patients do not have a preference of what
type of
report they want, they can choose -most educational," which can be linked to
report methods that have been demonstrated by patient voting or by scientific
study to maximize healthy literacy. In other embodiments, the "effectiveness"
of the report is determined automatically at least in part based on patient
information.
= Report delivery voice. Patients can select what type of voice they would
like to
hear for the report.
[0920]
The medical report generator 1894 can also utilize a medical image
findings database 1884 for the patient-specific medical report. There are a
number of
"medical image findings" that can be determined and stored in the medical
image findings
database 1884, and any one or more of them can be incorporated into the
medical report.
The following are some examples of the information that can be determined and
stored in
the medical image findings database 1884.
[0921]
Image processing algorithms process the heart and heart arteries from a
CT scan to segment:
= Coronary arteries ¨ atherosclerosis, vascular morphology, ischemia
= Cardiovascular structures ¨ left ventricular mass, left ventricular
volume, atrial
volumes, aortic dimensions, epicardial fat, fatty liver, valves
[0922]
Heart and heart artery findings are quantified by, for example, the
following:
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= Coronary artery plaque ¨ e.g., plaque burden, volume; plaque type,
percent
atheroma volume, location, directionality, etc.
= Vascular morphology ¨ e.g., lumen volume, vessel volume, arterial
remodeling,
anomaly, aneurysm, bridging, dissection, etc.
= Left ventricular mass ¨ in grams or indexed to body surface area or body
mass
index
= Left ventricular volume ¨ in ml or indexed to body surface area or body
mass
index
= Atrial volumes ¨ in ml or indexed to body surface area or body mass index
= Aortic dimensions ¨ in ml or indexed to body surface area or body mass
index
= Epicardial fat ¨ in ml or indexed to body surface area or body mass index
= Fatty liver ¨ Hounsfield unit density alone or in relevance to spleen
[0923]
Quantified heart and heart artery findings are automatically sent to a
medical image quantitative findings database 1885 that has well-defined areas
for
classification of each of these findings.
[0924]
In some embodiments, the medical image quantitative findings database
1885 has an algorithm that links together relevant findings that comprise
syndromes over
single disease states.
[0925]
In an example, the presence of left ventricular volume elevation, along
with the presence of left atrial volume elevation, along with thickening of
the mitral valve,
along with a normal right atrial volume may suggest a patient with significant
mitral
regurgitation (or leaky mitral valve).
[0926]
In another example, the presence of an increased aortic dimension and
increased left ventricular mass may suggest a person has hypertension.
[0927]
The medical image quantitative findings database 1885 can link to other
electronic data source (e.g., company database, electronic health record,
etc.) to identify
potential associative relationships between study findings. For example,
perhaps the
electronic health record indicates the patient has hypertension, in which
case, the report
will automatically curate a health report card for patients specifically with
hypertension,
i.e., normality or left ventricular mass, atrial volume, ventricular volume,
aortic
dimensions.
[0928]
The medical image quantitative findings database 1885 can link to the
Internet to perform medical imaging finding-specific search (i.e., search is
based upon the
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image data curation as described above), to retrieve information that may link
relevant
findings that comprise syndromes.
[0929] Diagnosis: morphologic classification of heart and
heart artery findings:
[0930] Morphologic classification can be based upon:
[0931] Comparison to a population-based normative
reference range database
1886 which includes ranges that have the mean/95% confidence interval,
median/interquartile interval; deciles for normality; quintiles of normality,
etc. These data
can also be reported in the medical report in "ages." For example, perhaps a
patient's
biological age is 50 years, while their heart age is 70 years based upon
comparison to the
age- and gender-based normative reference range database.
[0932] If the population-based normative reference range
database 1886 does
not exist in a system 1881, in some embodiments the system 1881 can search the
Internet
looking for these normative ranges, e.g., in PubMed search and by natural
language
processing and -reading" of the scientific papers.
[0933] Classification grades: can be done in many ways:
= presence/absent
= normal, mild, moderate, severe
= elevated or reduced
= percentile for age, gender and ethnicity
[0934] Any of the above categorization systems, also
accounting for other
patient conditions (e.g., if a patient has hypertension, their expected plaque
volume may be
higher than for a patient without hypertension).
[0935] Temporal / Dynamic changes can be done and
integrated into the
medical report by automatic comparison of findings with a patient's prior
study which
exists in a specific prior exams database 1887, e.g., reporting the change
that has occurred,
and direct comparison to the population-based normative reference range
database 1886 to
determine whether this change in disease is expectedly normal, mild, moderate,
severe, etc.
(or other classification grading method).
[0936] Temporal / Dynamic changes may be done by
comparison of >2 studies
(e.g., 4 studies) in the database of patient's studies, in which changes can
be reported by
absolute, relative %, along a regression line, or by other mathematical
transformation, with
these findings compared to the population-based normative reference range
database.
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Prognostication
[0937]
Automatic prognostication of patient outcomes can be done by
integrating the medical imaging findings (+ coupled to other patient data +
coupled to
normative reference range database) by direct interrogation of a prognosis
database 1888
that exists with patient-level outcomes. The prognosis database 1888 may be a
single
database (e.g., of coronary plaque findings), or multiple databases (e.g., one
database for
coronary plaque, one database for ventricular findings, one database for non-
coronary
vascular findings, etc.).
[0938]
In some embodiments, several and separate databases may exist for
different types of prognosis, e.g., one database may exist for auto-
calculation of risk of
major adverse cardiovascular events (MACE), while another database may exist
for auto-
calculation of rapid disease progression. These databases may be interrogated
sequentially,
or they may be interactive with each other (e.g., a person who has a higher
rate of rapid
disease progression may also have a higher risk of MACE, but the presence of
rapid disease
progression may increase risk of MACE beyond that of someone who does not
experience
rapid disease progression).
[0939]
Prognostic findings can be reported into the movie report by:
elevated/reduced; % risk, hazards ratio, time-to-event Kaplan Meier curves,
and others.
Clinical Decision Making
109401
Automatic recommendation of treatments can be done by integrating the
above findings with a treatment database 1889. The treatment database 1889 can
house
scientific and clinical evidence data to which a patient's medical image
findings, diagnosis,
syndromes and prognosis can be linked. Based upon these findings ¨ as well as
clinical trial
inclusion / exclusion / eligibility criteria ¨ a treatment recommendation can
be given for a
specific medication or procedure that may improve the patient's condition.
[0941]
For example, perhaps a patient had a specific amount of plaque on the
patient's 1st study and that plaque progressed significantly on the patient's
2nd study. The
system will report the change as high, normal, or low based upon query of the
normative
reference range database and the prior studies database and, based upon this,
render a
prognosis. The system could then query the EMR database to see which
medications the
patient is currently taking, and the system finds out that the patient is
taking a statin. The
system could then examine the databases that would let the system know that
adding a
PCSK9 inhibitor medication on top of the statin medication would be associated
with an
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XX% relative risk reduction. A similar example will be for a patient being
considered for
an invasive procedure.
[0942]
In many cases, a treatment path is not 100% clear where there is benefit
as well as risk for doing a specific kind of therapy. In this case, the system
can query the
shared decision database 1890, which lists the scientific evidence for
treatment options,
and lists all of the benefits as well as limitations of these approaches. The
"pros- and "cons"
of the different treatment approaches can be integrated into the patient
medical report.
[0943]
For example, based upon the medical image findings, normative
reference comparison, prognosis evaluation and treatment query, perhaps an 81-
year-old
woman would highly benefit from a medication whose side effect is worsening of
osteoporosis. In this case, the woman may have severe osteoporosis and for
her, the benefits
of the medication outweigh the risk as is illustrated and communicated through
the shared
decision making database. For these types of cases, an alternative may be
provided.
[0944]
For example, the shared decision making database may show
comparative effectiveness of treatments, similar to the way Consumer Reports
or
amazon.com product options are listed so that the patient can understand the
options, pros
and cons.
[0945]
The system 1881 can also include a health literacy database 1891. This
portion of the workflow to produce a medical report can be an interactive
"quiz" to ensure
that the patient understood the study findings, the diagnosis, the prognosis,
and the
treatment decision making. If the patient fails the "quiz", then the system
would
automatically curate content into more and more simple terms so that the
patient does
understand their condition.
109461
Thus, the health literacy database 1891 can be a tiered database of
movies based upon simple to complex, and would be tailored to the patient's
preferences
as well as their score on the "quiz". This information can be kept for future
movies for that
patient.
[0947]
The opposite can also occur. As an example, perhaps a patient passes
the "quiz" and the system asks the patient whether they would like to know
more about the
condition. If the patient answers 'yes', then the system can extract more and
more complex
movies for display to the patient. In this way, the health literacy database
1891 is multilevel
and interactive.
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[0948]
The system 1881 can also include an education database 1892, which
has educational materials that are based upon science and medicine, and are
redundant in
content but different in delivery method.
[0949]
As an example, if the system notes that the patient has a certain finding,
the system can inquire with the patient whether they would like to learn more
about a
specific conditions. If the patient indicates 'yes,' then the system can
inquire whether the
patient would like to see a summary infographic page, a slide presentation, a
movie, etc.
[0950]
The system 1881 can also include an image display database 1893 that
includes images that the medical report generator 1894 uses to morph medical
images into
cartoon formats, or simpler formats, that a patient can better understand.
[0951]
The system can also include a post-report education database 1896 that
continually uploads new information in real time related to specific medical
conditions.
The medical report generator 1894 can query this post-report education
database 1896, and
curate educational content (e.g., scientific articles, publications,
presentations, etc.) that
exist on the internet, and then modify them through the post-report education
database 1896
to information that the patient would like to see, for example, as determined
by the patient
information or by a user input.
[0952]
The medical report generator 1894 system can be interactive, not just
passive. Different types of reports and information can be generated as a set
of information
for a medical report, and a user can interactively select what information to
view using the
interface system 1895 (e.g., a computer system of the user), and can select
other
information to be presented/displayed by providing input to the medical report
generator
1894.
Systems and methods for imaging methods of non-contiguous, or different,
arterial beds
for determining Atherosclerotic Cardiovascular Disease (ASCVD)
[0953]
This portion of the disclosure relates to systems and methods for
assessing atherosclerotic cardiovascular disease risk using sequential non-
contiguous
arterial bed imaging. Various embodiments described herein relate to
quantification and
characterization of sequential non-contiguous arterial bed images to generate
a ASCVD
assessment, or ASCVD risk score. Any risk score generated can be a suggested
risk score,
and a medical practitioner can use the suggested ASCVD risk score to provide a
ASCVD
risk score for a patient. In various embodiments, a suggested ASCVD risk score
can be
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used to provide a ASCVD risk score to a patient based on the suggested ASCVD
risk score,
or with additional information.
[0954]
In some embodiments, the ASCVD risk score is a calculation of your
risk of having a cardiovascular problem over a duration of time, for example,
1 year, 3
years, 5 years, 10 years, or longer). In some embodiments, the cardiovascular
problem can
include one or more of a heart attack or stroke. However, other cardiovascular
problems
can also be included, that is, assessed as a risk. In some embodiments, this
risk estimate
considers age, sex, race, cholesterol levels, blood pressure, medication use,
diabetic status,
and/or smoking status. In some embodiments, the ASCVD risk score is given as a
percentage. This is your chance of having heart disease or stroke in the next
10 years. There
are different treatment recommendations depending on your risk score. As an
example, an
ASCVD risk score of 0.0 to 4.9 percent risk can be considered low. Eating a
healthy diet
and exercising will help keep your risk low. Medication is not recommended
unless your
LDL, or -bad" cholesterol, is greater than or equal to 190. An ASCVD risk
score of 5.0 to
7.4 percent risk can be considered borderline. Use of a statin medication may
be
recommended if y ou have certain conditions, which may be referred to as "risk
enhancers."
These conditions may increase your risk of a heart disease or stroke. Talk
with your primary
care provider to see if you have any of the risk enhancers in the list below.
An ASCVD risk
score of 7.5 to 20 percent risk can be considered intermediate. Typically for
a patient with
a score in this range, it is recommended that a moderate-intensity statin
therapy is started.
An ASCVD risk score of greater than 20 percent risk can be considered high.
When the
ASCVD risk score indicates a high risk, it may be recommended that the patient
start a
high-intensity statin therapy.
109551
Various embodiments described herein also relate to systems and
methods for quantifying and characterizing ASCVD of different arterial beds,
e.g., from a
single imaging examination. In some embodiments, the systems and methods can
quantify
and characterize ASCVD of different arterial beds from two or more imaging
examinations.
Any of the imaging performed can be done in conjunction with a normalization
device,
described elsewhere herein. Various embodiments described herein also relate
to systems
and methods for determining an integrated metric to prognosticate ASCVD events
by
weighting findings from each arterial bed. Examples of systems and methods are
described
for quantifying and characterizing ASCVD burden, type and progression to
logically guide
clinical decision making through improved diagnosis, prognostication, and
tracking of
CAD after medical therapy or lifestyle changes. As such, some systems and
methods can
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provide both holistic patient-level ASCVD risk assessment, as well as arterial
bed-specific
ASCVD burden, type and progression.
[0956]
As an example relating to imaging of non-contiguous arterial beds that
is done in conjunction with a normalization device, a normalization device is
configured to
normalize a medical image of a coronary region of a subject for an algorithm-
based medical
imaging analysis. In an example, a normalization device includes a substrate
configured in
size and shape to be placed in a medical imager along with a patient so that
the
normalization device and the patient can be imaged together such that at least
a region of
interest of the patient and the normalization device appear in a medical image
taken by the
medical imager, a plurality of compartments positioned on or within the
substrate, wherein
an arrangement of the plurality of compartments is fixed on or within the
substrate, and a
plurality of samples, each of the plurality of samples positioned within one
of the plurality
of compartments, and wherein a volume, an absolute density, and a relative
density of each
of the plurality of samples is known. The plurality of samples can include a
set of contrast
samples, each of the contrast samples comprising a different absolute density
than absolute
densities of the others of the contrast samples, a set of calcium samples,
each of the calcium
samples comprising a different absolute density than absolute densities of the
others of the
calcium samples, and a set of fat samples, each of the fat samples comprising
a different
absolute density than absolute densities of the others of the fat samples. The
set contrast
samples can be arranged within the plurality of compartments such that the set
of calcium
samples and the set of fat samples surround the set of contrast samples
[0957]
In an example, a computer implemented method for generating a risk
assessment of atherosclerotic cardiovascular disease (ASCVD) uses a
normalization device
(as described herein) to improve accuracy of the algorithm-based imaging
analysis. In some
embodiments, the medical imaging method includes receiving a first set of
images of a first
arterial bed and a first set of images of a second arterial bed, the second
arterial bed being
noncontiguous with the first arterial bed, and wherein at least one of the
first set of images
of the first arterial bed and the first set of images of the second arterial
bed are normalized
using the normalization device, quantifying ASCVD in the first arterial bed
using the first
set of images of the first arterial bed, quantifying ASCVD in the second
arterial bed using
the first set of images of the second arterial bed, and determining a first
ASCVD risk score
based on the quantified ASCVD in the first arterial bed and the quantified
ASCVD in the
second arterial bed. In some embodiments, determining a first weighted
assessment of the
first arterial bed based on the quantified ASCVD of the first arterial bed and
weighted
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adverse events for the first arterial bed, and determining a second weighted
assessment of
the second arterial bed based on the quantified ASCVD of the second arterial
bed and
weighted adverse events for the second arterial bed. Determining the first
ASCVD risk
score further comprises determining the ASCVD risk score based on the first
weighted
assessment and the second weighted assessment. In some embodiments, a method
can
further include receiving a second set of images of the first arterial bed and
a second set of
images of the second arterial bed, the second set of images of the first
arterial bed generated
subsequent to generating the first set of image of the first arterial bed, and
the second set
of images of the second arterial bed generated subsequent to generating the
first set of
image of the second arterial bed, quantifying ASCVD in the first arterial bed
using the
second set of images of the first arterial bed, quantifying ASCVD in the
second arterial bed
using the second set of images of the second arterial bed, and determining a
second ASCVD
risk score based on the quantified ASCVD in the first arterial bed using the
second set of
images, and the quantified ASCVD in the second arterial bed using the second
set of
images. Determining the second ASCVD risk score can be further based on the
first
ASCVD risk score. In some embodiments, the first arterial bed includes
arteries of one of
the aorta, carotid arteries, lower extremity arteries, renal arteries, or
cerebral arteries. The
second arterial bed includes arteries of one of the aorta, carotid arteries,
lower extremity
arteries, renal arteries, or cerebral arteries that are different than the
arteries of the first
arterial bed. Any of the methods described herein can be based on imaging
using a
normalization device to improve quality of the automatic image assessment of
the
generated images.
[0958]
In an embodiment, an output of these methods can be a single patient-
level risk score that can improve arterial bed-specific event-free survival in
a personalized
fashion. In some embodiments, any of the quantization of characterization
techniques and
processes described in U.S. Patent Application 17/142,120, filed January 5,
2020, titled
Systems, Methods, and Devices for Medical Image Analysis, Risk Stratification,
Decision
Making and/or Disease Tracking" (which is incorporated by reference herein),
can be
employed, in whole or in part, to generate a ASCVD risk assessment.
[0959]
Traditional cardiovascular imaging using 3D imaging by computed
tomography, magnetic resonance imaging, nuclear imaging or ultrasound have
relied upon
imaging single vascular beds (or territories) as regions of interest.
Sometimes, multiple
body parts may be imaged if they are contiguous, for example, chest-abdomen-
pelvis CT,
carotid and cerebral artery imaging, etc. Multi-body part imaging can be
useful to identify
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disease processes that affect adjoining or geographically close anatomic
regions. Multi-
body part imaging can be used to enhance diagnosis, prognostication and guide
clinical
decision making of therapeutic interventions (e.g., medications, percutaneous
interventions, surgery, etc.).
109601
Additionally, methods that employ multi-body part imaging of non-
contiguous arterial beds can be advantageous for enhancing diagnosis,
prognostication and
clinical decision making of atherosclerotic cardiovascular disease (ASCVD).
ASCVD is a
systemic disease that can affect all vessel beds, including coronary arteries,
carotid arteries,
aorta, renal arteries, lower extremity arteries, cerebral arteries and upper
extremity arteries.
While historically considered as a single diagnosis, the relative prevalence,
extent, severity
and type of ASCVD (and its consequent effects on vascular morphology) can
exhibit very
high variance between different arterial beds. As an example, patients with
severe carotid
artery atherosclerosis may have no coronary artery atherosclerosis.
Alternatively, patients
with severe coronary artery atherosclerosis may have milder forms of lower
extremity
atherosclerosis. As with the prevalence, extent and severity, so too can the
types of
atherosclerosis differ amongst vascular beds.
109611
A significant body of research now clarifies the clinical significance of
atherosclerotic cardiovascular disease (ASCVD) burden, type and progression,
as
quantified and characterized by advanced imaging. As an example, coronary
computed
tomographic angiography (CCTA) now allows for quantitative assessment of ASCVD
and
vascular morphology in all major vascular territories. Several research trials
have
demonstrated that not only the amount (or burden) of ASCVD, but also the type
of plaque
is important in risk stratification; in particular, low attenuation plaques
(LAP) and non-
calcified plaques which exhibit positive arterial remodeling are implicated in
greater
incidence of future major adverse cardiovascular events (MACE); calcified
plaques and, in
particular, calcified plaques of higher density appear to be more stable. Some
studies that
have evaluated this concept have been observational and within randomized
controlled
trials. Further, medication use has been associated with a reduction in LAP
and an
acceleration in calcified plaque formation in populations, with within-person
estimates not
yet reported. Medications such as statins, icosapent ethyl, and colchicine
have been
observed by coronary computed tomography angiography (CCTA) to be associated
with
modification of ASCVD in the coronary arteries. Similar findings relating the
complexity
or type of ASCVD in the carotid arteries has been espoused as an explanation
for stroke,
as well as for renal arteries and lower extremity arteries.
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[0962]
Accordingly, understanding the presence, extent, severity and type of
ASCVD in each of the vascular arterial beds improves understanding of future
risk of
adverse cardiovascular events as well as the types of adverse cardiovascular
events that will
occur (e.g., heart attack versus stroke versus amputation, etc.), and can
allow tracking of
the effects of salutary medication and lifestyle modifications on the disease
process in
multiple arterial beds. Further, integrating the findings from non-contiguous
arterial beds
into a single prediction model can improve holistic assessment of an
individual's risk and
response to therapy over time in a personalized, precision-based fashion. In
some examples,
such assessments can include integrating an assessment of coronary arteries
with an
assessment of one or more other arterial beds, for example, one or more of the
aorta, carotid
arteries, lower extremity arteries, upper extremity arteries, renal arteries,
and cerebral
arteries. In some examples, such assessments can include integrating an
assessment of any
of the aorta, carotid arteries, lower extremity arteries, upper extremity
arteries, renal
arteries, or cerebral arteries with a different one of the aorta, carotid
arteries, lower
extremity arteries, upper extremity arteries, renal arteries, or cerebral
arteries.
[0963]
Various embodiments described herein relate to systems and methods
for determining assessments that may be used for reducing cardiovascular risk
and/or
events. For example, such assessments can be used to, at least partly,
determine or generate
lifestyle, medication and/or interventional therapies based upon actual
atherosclerotic
cardiovascular disease (ASCVD) burden, ASCVD type, and/or and ASCVD
progression.
In some embodiments, the systems and methods described herein are configured
to
dynamically and/or automatically analyze medical image data, such as for
example non-
invasive CT, MRI, and/or other medical imaging data of the arterial beds of a
patient, to
generate one or more measurements indicative or associated with the actual
ASCVD
burden, ASCVD type, and/or ASCVD progression, for example using one or more
artificial
intelligence (AI) and/or machine learning (ML) algorithms. The arterial beds
can include
for example, coronary arteries, carotid arteries, and lower extremity
arteries, renal arteries,
and/or cerebral arteries. In some embodiments, the systems and methods
described herein
can further be configured to automatically and/or dynamically generate
assessments that
can be used in one or more patient-specific treatments and/or medications
based on the
actual ASCVD burden, ASCVD type, and/or ASCVD progression, for example using
one
or more artificial intelligence (Al) and/or machine learning (ML) algorithms.
[0964]
In some embodiments, the systems and methods described herein are
configured to utilize one or more CCTA algorithms and/or one or more medical
treatment
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algorithms on two or more arterial bodies to quantify the presence, extent,
severity and/or
type of ASCVD, such as for example its localization and/or pen-lesion tissues.
In some
embodiments, the one or more medical treatment algorithms are configured to
analyze any
medical images obtained from any imaging modality, such as for example
computed
tomography (CT), magnetic resonance (MR), ultrasound, nuclear medicine,
molecular
imaging, and/or others. In some embodiments, the systems and methods described
herein
are configured to utilize one or more medical treatment algorithms that are
personalized
(rather than population-based), treat actual disease (rather than surrogate
markers of
disease, such as risk factors), and/or are guided by changes in CCTA-
identified ASCVD
over time (such as for example, progression, regression, transformation,
and/or
stabilization). In some embodiments, the one or more CCTA algorithms and/or
the one or
more medical treatment algorithms are computer-implemented algorithms and/or
utilize
one or more AT and/or ML algorithms.
[0965]
In some embodiments, the systems and methods are configured to assess
a baseline ASCVD in an individual using two or more arterial bodies. In some
embodiments, the systems and methods are configured to evaluate ASCVD by
utilizing
coronary CT angiography (CCTA). In some embodiments, the systems and methods
are
configured to identify and/or analyze the presence, local, extent, severity,
type of
atherosclerosis, pen-lesion tissue characteristics, and/or the like. In some
embodiments,
the method of ASCVD evaluation can be dependent upon quantitative imaging
algorithms
that perform analysis of coronary, carotid, and/or other vascular beds (such
as, for example,
lower extremity, aorta, renal, and/or the like).
[0966]
In some embodiments, the systems and methods are configured to
categorize ASCVD into specific categories based upon risk. For example, some
example
of such categories can include: Stage 0, Stage I, Stage II, Stage III; or
none, minimal, mild,
moderate; or primarily calcified vs. primarily non-calcified; or X units of
low density non-
calcified plaque); or X% of NCP as a function of overall volume or burden. In
some
embodiments, the systems and methods can be configured to quantify ASCVD
continuously. In some embodiments, the systems and methods can be configured
to define
categories by levels of future ASCVD risk of events, such as heart attack,
stroke,
amputation, dissection, and/or the like. In some embodiments, one or more
other non-
ASCVD measures may be included to enhance risk assessment, such as for example
cardiovascular measurements (left ventricular hypertrophy for hypertension;
atrial volumes
for atrial fibrillation; fat; etc.) and/or non-cardiovascular measurements
that may contribute
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to ASCVD (e.g., emphysema). In some embodiments, these measurements can be
quantified using one or more CCTA algorithms.
[0967]
In some embodiments, the systems and methods described herein can be
configured to generate a personalized or patient-specific treatment based on
an assessment
of two or more arterial bodies. More specifically, in some embodiments, the
systems and
methods can be configured to generate therapeutic recommendations based upon
ASCVD
presence, extent, severity, and/or type. In some embodiments, rather than
utilizing risk
factors (such as, for example, cholesterol, diabetes), the treatment algorithm
can comprise
and/or utilize a tiered approach that intensifies medical therapy, lifestyle,
and/or
interventional therapies based upon ASCVD directly in a personalized fashion.
In some
embodiments, the treatment algorithm can be configured to generally ignore one
or more
conventional markers of success¨such as lowering cholesterol, hemoglobin Ale,
etc.¨
and instead leverage ASCVD presence, extent, severity, and/or type of disease
to guide
therapeutic decisions of medical therapy intensification. In some embodiments,
the
treatment algorithm can be configured to combine one or more conventional
markers of
success¨such as lowering cholesterol, hemoglobin AlC, etc., with ASCVD
presence,
extent, severity, and/or type of disease to guide therapeutic decisions of
medical therapy
intensification. In some embodiments, the treatment algorithm can be
configured to
combine one or more novel markers of success
____________________________________ such as genetics, transcriptomics, or
other
`omic measurements¨with ASCVD presence, extent, severity, and/or type of
disease to
guide therapeutic decisions of medical therapy intensification. In some
embodiments, the
treatment algorithm can be configured to combine one or more other imaging
markers of
success¨such as carotid ultrasound imaging, abdominal aortic ultrasound or
computed
tomography, lower extremity arterial evaluation, and others¨with ASCVD
presence,
extent, severity, and/or type of disease to guide therapeutic decisions of
medical therapy
intensification.
[0968]
In some embodiments, the systems and methods are configured to
update personalized treatment based upon response assessment of two or more
arterial
bodies. In particular, in some embodiments, based upon the change in ASCVD
between
the baseline and follow-up CCTA, personalized treatment can be updated and
intensified if
worsening occurs or de-escalated / kept constant if improvement occurs. As a
non-limiting
example, if stabilization has occurred, this can be evidence of the success of
the current
medical regimen. Alternatively, as another non-limiting example, if
stabilization has not
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occurred and ASCVD has progressed, this can be evidence of the failure of the
current
medical regimen, and an algorithmic approach can be taken to intensify medical
therapy.
[0969] To facilitate an understanding of the systems and
methods discussed
herein, several terms are described below. These terms, as well as other terms
used herein,
should be construed to include the provided descriptions, the ordinary and
customary
meanings of the terms, and/or any other implied meaning for the respective
terms, wherein
such construction is consistent with context of the term. Thus, the
descriptions below do
not limit the meaning of these terms, but only provide example descriptions.
[0970] Presence of ASCVD: This can be the presence vs.
absence of plaque;
or the presence vs. absence of non-calcified plaque; or the presence vs.
absence of low
attenuation plaque
[0971] Extent of ASCVD: This can include the following:
= Total ASCVD Volume
= Percent atheroma volume (atheroma volume / vessel volume
100)
= Total atheroma volume normalized to vessel length (TAVnorm).
= Diffuseness (% of vessel affected by ASCVD)
[0972] Severity of ASCVD: This can include the following:
= ASCVD severity can be linked to population-based estimates
normalized to age, gender, ethnicity, and/or CAD risk factors
= Angiographic stenosis >70% or >50% in none, 1-. 2-, or 3-VD
[0973] Type of ASCVD: This can include the following:
= Proportion (ratio, %, etc.) of plaque that is non-calcified vs.
calcified
= Proportion of plaque that is low attenuation non-calcified vs.
non-calcified vs. low density calcified vs. high-density calcified
= Absolute amount of non-calcified plaque and calcified plaque
= Absolute amount of plaque that is low attenuation non-calcified
vs. non-calcified vs. low density calcified vs. high-density
calcified
= Continuous grey-scale measurement of plaques without ordinal
classification
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= Vascular remodeling imposed by plaque as positive remodeling
(1.10 or >1.05 ratio of vessel diameter / normal reference
diameter; or vessel area / normal reference area; or vessel
volume / normal reference volume) vs. negative remodeling
(--:-.1.10 or <1.05)
= Vascular remodeling imposed by plaque as a continuous ratio
[0974] ASCVD Progression
= Progression can be defined as rapid vs. non-rapid, with
thresholds to define rapid progression (e.g., >1.0% percent
atheroma volume, >200 mm3 plaque, etc.)
= Serial changes in ASCVD can include rapid progression,
progression with primarily calcified plaque formation;
progression with primarily non-calcified plaque formation; and
regression.
[0975] Categories of Risk
= Stages: 0, I, II, or III based upon plaque volumes associated with
angiographic severity (none, non-obstructive, and obstructive
1VD, 2VD and 3VD)
= Percentile for age and gender and ethnicity and presence of risk
factor (e.g., diabetes, hypertension, etc.)
= % calcified vs. % non-calcified as a function of overall plaque
volume
= X units of low density non-calcified plaque
= Continuous 3D histogram analysis of grey scales of plaque by
lesion, by vessel and by patient
= Risk can be defined in a number of ways, including risk of
MACE, risk of angina, risk of ischemia, risk of rapid
progression, risk of medication non-response, etc.
[0976] Certain features in embodiments of systems and
methods relating to
determining an assessment of non-contiguous arterial beds are described below.
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Medical Imaging of Non-Contiguous Arterial Beds
[0977]
Systems and methods described herein also relate to medical imaging of
non-contiguous arterial beds. For example, imaging of non-contiguous arterial
beds in a
single imaging examination. In other embodiments, imaging of non-contiguous
arterial
beds in two or more imaging examinations, and the information from the
generated images
can be used to determine information relating to a patient's health. As an
example, coronary
artery and carotid arteries are imaged using the same contrast bolus. In this
case, the
coronary arteries can be imaged by CCTA. Immediately after CCTA image
acquisition, the
CT table moves and images the carotid artery using the same or supplemental
contrast dose.
The example here is given for CT imaging in a single examination, but can be
also applied
to combining information from multiple imaging examinations; or multimodality
imaging
integration (e.g., ultrasound of the carotid; computed tomography of the
coronary).
Automated Arterial Bed-Specific Risk Assessment
[0978]
This is accomplished by an automated method for quantification and
characterization of ASCVD in individual artery territories for improved
diagnosis,
prognostication, clinical decision making and tracking of disease changes over
time. These
findings may be arterial bed-specific. As an example, conversion of non-
calcified plaque
to calcified plaque may be a feature that is considered beneficial and a sign
of effective
medical therapy in the coronaries, but may be considered to be a pathologic
process in the
lower extremity arteries. Further, the prognostication enabled by the
quantification and
characterization of ASCVD in different artery territories may differ. As an
example,
untoward findings in the carotid arteries may prognosticate future stroke;
while untoward
findings in the coronary arteries may prognostic future myocardial infarction.
Partial
overlap of risk may occur, e.g., wherein adverse findings in the carotid
arteries may be
associated with an increase in coronary artery events.
Patient-Specific Risk Assessment
[0979]
By combining the findings from each arterial bed, along with relative
weighting of arterial bed findings, risk stratification, clinical decision
mating and disease
tracking can be done with greater precision in a personalized fashion. Thus,
patient-level
prediction models are based upon understanding the ASCVD findings of non-
contiguous
arterial beds but communicated as a single integrated metric (e.g., 1-100,
mild/moderate/severe risk, etc.).
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Longitudinal Updating of Arterial Bed- and Patient-Specific Risk
[0980]
By longitudinal serial imaging after treatment changes (e.g., medication,
lifestyle, and others), changes in ASCVD can be quantified and characterized
and both
arterial bed-specific as well as patient-level risk can be updated based upon
the changes as
well as the most contemporary ASCVD image findings.
[0981]
Figure 19A illustrates an example of a process 1900 for determining a
risk assessment using sequential imaging of noncontiguous arterial beds of
patient,
according to some embodiments. At block 1905, sequential imaging of
noncontiguous
arterial beds of a patient may be performed. An example, sequential imaging be
noncontiguous first arterial bed second arterial bed performed. In some
embodiments, the
first arterial bed includes one of aorta, carotid arteries, lower extremity
arteries, upper
extremity arteries, renal arteries, or cerebral arteries, and the second
arterial bed includes a
different one of aorta, carotid arteries, lower extremity arteries, upper
extremity arteries,
renal arteries, or cerebral arteries. In some embodiments the third arterial
bed may be
imaged. In some embodiments a fourth arterial bed may be imaged. The third and
fourth
arterial beds may include one of aorta, carotid arteries, lower extremity
arteries, upper
extremity arteries, renal arteries, or cerebral arteries. The sequential
imaging of the
noncontiguous arterial beds may be done the same settings on the imaging
machine, at
different times, or with different imaging modalities, for example, CT and
ultrasound).
[0982]
At block 1910, the process 1900 automatically quantifies and
characterizes ASCVD in the imaged arterial beds. In some embodiments, the
ASCVD in
the first arterial bed and the second arterial bed are quantified and
characterized using any
of the qualifications and characterization disclosed herein. For example,
images of the first
arterial bed are analyzed by a system configured with a machine learning
program that has
been trained on a plurality of arterial bed images and annotated features of
arterial bed
images. In other embodiments, the ASCVD and the first arterial bed and second
arterial
bed are quantified using other types of qualifications the characterizations.
[0983]
At block 1915, the process 1900 generates a prognostic assessment of
arterial bed specific adverse events. An example, for the coronary arteries
the adverse event
can be a heart attack. In another example, for the carotid arteries the
adverse event is a
stroke. In another example, for the lower extremity arteries the adverse event
is amputation.
The adverse events can be determined from patient information that is
accessible to the
system performing the assessment. For example, from archived patient medical
information
(e.g., patient medical information 1602 illustrated in Figure 16) or any other
stored
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information of a previous adverse event. Each event can be associated with a
weight based
on a predetermined scheme. The weights can be, for example, a value between
0.00 and
1.00. The weights associated with different adverse events can be stored in a
non-transient
storage medium, for example, a database. For a patient, a weighted assessment
of each
particular occurrence of an adverse event can be determined. In some
embodiments, the
weights are multiplied by the event. For example, for a 1st occurrence of
event 1 that has a
weight of 0.05, one occurrence of that event results in a weighted assessment
of 00.05. A
second occurrence of event 1 may have the same weight, or a different weight.
For example,
an increased weight. In one example, a second occurrence of event 1 has a
weight of 0.15,
such that when two occurrences of the event occur the weighted assessment is
the sum of
the weights of the first and second occurrence (for example, 0.20). Other
events can have
difference weights, and the weighted assessment can include the sum of all of
the weights
for all of the events that occurred.
[0984]
At block 1920, the process 1900 uses the arterial bed specific risk
assessment to determine a patient level risk score, for example, an ASCVD risk
score. In
an example, the ASCVD risk score is based on a weighted assessment of an
arterial bed. In
an example, the ASCVD risk score is based on a weighted assessment of an
arterial bed
and other patient information.
[0985]
At block 1925, the process 1900 tracks changes in ASCVD based upon
treatment and lifestyle to determine beneficial or adverse changes in ASCVD.
In some
embodiments, as indicated in block 1930, the process 1900 uses additional
sequential
imaging, taken at a different time (e.g., days, weeks, months or years later)
of one or more
noncontiguous arterial beds and the process 1900 updates arterial bed and
patient level risk
assessments, and determines an updated ASCVD score based on the additional
imaging.
The baseline and updated assessment can also integrate non-imaging findings
that are
associated with arterial bed- and patient-specific risk. These may include
laboratory tests
(e.g., troponin, b-type natriuretic peptide, etc.); medication type, dose and
duration (e.g.,
lovastatin 20 mg per day for 6 years); interactions between multiple
medications (e.g.,
lovastatin alone versus lovastatin plus ezetimibe); biometric information
(e.g., heart rate,
heart rate variability, pulse oximetry, etc.) and patient history (e.g.,
symptoms, family
history, etc.). By monitoring the ASCVD score and correlating changes in the
ASCVD
score with patient treatment(s) and patient lifestyle changes, better
treatment protocols and
lifestyle choices for that patient may be determined.
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[0986]
Figure 19B illustrates an example where a sequential, non-contiguous
arterial bed imaging is performed. In this example, a sequential, non-
contiguous arterial
bed imaging is performed for the (1) coronary arteries, and for the (2)
carotid arteries. As
can be seen in the quantification and characterization of the atherosclerosis
in both the
coronary and carotid arteries, the phenotypic makeup of the disease process is
highly
variable, with the coronary artery cross-sections showing both blue
(calcified) and red (low-
density non-calcified) plaque; and the carotid artery cross-sections only
showing yellow
(non-calcified) and red (low-density non-calcified plaque). Further, the
amount of
atherosclerosis is higher in the coronary arteries than the carotid arteries,
indicating a
differential risk of heart attack and stroke, respectively.
[0987]
Figure 19C is an example of a process 1950 for determining a risk
assessment of atherosclerotic cardiovascular disease (ASCVD) using sequential
imaging
of non-contiguous arterial beds, according to some embodiments. At block 1952
a first
arterial bed of a patient is imaged. In some embodiments, the first arterial
bed includes one
of an aorta, carotid arteries, lower extremity arteries, upper extremity
arteries, renal arteries,
or cerebral arteries. In some embodiments, the imaging used can be digital
subtraction
angiography (DSA), duplex ultrasound (DUS), computed tomography (CT), magnetic
resonance angiography (MRA), ultrasound, or magnetic resonance imaging (MR1),
or
another type of imaging that generates a representation of the arterial bed.
At block 1954
the process 1950 images a second arterial bed. The imaging of the second
arterial bed is
noncontiguous with the first arterial bed. In sonic embodiments, the second
arterial bed can
be one of an aorta, carotid arteries, lower extremity arteries, upper
extremity arteries, renal
arteries, or cerebral arteries in his different than the first arterial bed.
In some embodiments,
imaging the second arterial bed can be performed by a DSA, DUS, CT, MRA,
ultrasound,
or MM imaging process, or another imaging process. At block 1956 the process
1950
automatically quantifies ASCVD in the first arterial bed. At block 1958, the
process 1950
automatically quantifies ASCVD in the second arterial bed. The quantification
of ASCVD
in the first arterial bed and the second arterial bed can be done using any of
the
quantification disclosed herein (e.g., using a neural network trained with
annotated images)
or other quantification.
[0988]
At block 1960, the process 1950 determines a first weighted assessment
of the first arterial bed, the first weighted assessment associated with
arterial bed specific
adverse events for the first arterial bed. At block 1962 the process 1950
determines a second
weighted assessment of second arterial bed, the second weighted assessment
associated
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with arterial bed specific adverse events for the second arterial bed. At
block 1964 the
process 1950 generates an ASCVD patient risk score based on the first weighted
assessment
and the second weighted assessment.
[0989]
Figure 1911 is an example of a process 1970 for determining a risk
assessment using sequential imaging of non-contiguous arterial beds, according
to some
embodiments. At block 1972, the process 1970 receives images of the first
arterial bed and
a second arterial bed, the second arterial bed being noncontiguous with the
first arterial bed
and different than the first arterial bed. In some embodiments, the first
arterial bed can be
one of an aorta, carotid arteries, lower extremity arteries, upper extremity
arteries, renal
arteries, or cerebral arteries. The imaging of the second arterial bed is
noncontiguous with
the first arterial bed. In some embodiments, the images of the first arterial
bed were
generated by a DSA, DUS, CT, MRA, ultrasound, or MR1 imaging process, or
another
imaging process. In some embodiments, the images of the second arterial bed
were
generated by a DSA, DUS, CT, MRA, ultrasound, or MR1 imaging process, or
another
imaging process. In some embodiments, the second arterial bed can be one of an
aorta,
carotid arteries, lower extremity arteries, upper extremity arteries, renal
arteries, or cerebral
arteries, and is different than the first arterial bed. In some embodiments,
the images of the
first arterial bed and the second arterial bed may be received from a computer
storage
medium that is configured to store patient images. In some embodiments, the
images of the
first arterial bed and the second arterial bed may be received directly from a
facility which
generates the images. In some embodiments, the images of the first arterial
bed and second
arterial bed may be received indirectly from a facility which generates the
images. In some
embodiments, images of first arterial bed may be received from a different
source than
images of second arterial bed.
[0990]
At block 1974 the process 1970 automatically quantifies ASCVD in the
first arterial bed. At block 1976, the process 1970 automatically quantifies
ASCVD in the
second arterial bed. The quantification of ASCVD in the first arterial bed and
the second
arterial bed can be done using any of the quantification disclosed herein, or
other
quantification.
[0991]
At block 1978 the process 1970 determines a first weighted assessment
of the first arterial bed, the first weighted assessment associated with
arterial bed specific
adverse events for the first arterial bed. At block 1980 the process 1970
determines a second
weighted assessment of second arterial bed, the second weighted assessment
associated
with arterial bed specific adverse events for the second arterial bed. At
block 1982 the
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process 1970 generates an ASCVD patient risk score based on the first weighted
assessment
and the second weighted assessment.
[0992]
Figure 19E is a block diagram depicting an embodiment of a computer
hardware system 1985 configured to run software for implementing one or more
embodiments of systems and methods for determining a risk assessment using
sequential
imaging of noncontiguous arterial beds of a patient. In some embodiments, the
systems,
processes, and methods described herein are implemented using a computing
system, such
as the one illustrated in Figure 19E. The example computer system 1985 is in
communication with one or more computing systems 1994 and/or one or more data
sources
1995 via one or more networks 1993. While Figure 19E illustrates an embodiment
of a
computing system 1985, it is recognized that the functionality provided for in
the
components and modules of computer system 1985 can be combined into fewer
components and modules, or further separated into additional components and
modules.
[0993]
The computer system 1985 can comprise a Quantification. Weighting,
and Assessment Engine 1991 that carries out the functions, methods, acts,
and/or processes
described herein. For example, in some embodiments the functions of blocks
1956, 1958,
1960, 1962, and 1964 of Figure 19C. In some embodiments, the functions of
blocks 1972,
1974, 1976, 1978, 1980, and 1982 of Figure 19D. The Quantification, Weighting,
and
Assessment Engine 1991 is executed on the computer system 1985 by a central
processing
unit 1989 discussed further below.
[0994]
In general the word "engine," as used herein, refers to logic embodied
in hardware or firmware or to a collection of software instructions, having
entry and exit
points. Such -engines" may also be referred to as a module, and are written in
a program
language, such as JAVA, C, or C++, or the like. Software modules can be
compiled or
linked into an executable program, installed in a dynamic link library, or can
be written in
an interpreted language such as BASIC, PERL, LAU, PHP or Python and any such
languages. Software modules can be called from other modules or from
themselves, and/or
can be invoked in response to detected events or interruptions. Modules
implemented in
hardware include connected logic units such as gates and flip-flops, and/or
can include
programmable units, such as programmable gate arrays or processors.
[0995]
Generally, the modules described herein refer to logical modules that
can be combined with other modules or divided into sub-modules despite their
physical
organization or storage. The modules are executed by one or more computing
systems, and
can be stored on or within any suitable computer readable medium, or
implemented in-
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whole or in-part within special designed hardware or firmware. Not all
calculations,
analysis, and/or optimization require the use of computer systems, though any
of the above-
described methods, calculations, processes, or analyses can be facilitated
through the use
of computers. Further, in some embodiments, process blocks described herein
can be
altered, rearranged, combined, and/or omitted.
[0996]
The computer system 1985 includes one or more processing units (CPU,
GPU, TPU) 1989, which can comprise a microprocessor. The computer system 1985
further includes a physical memory 1990, such as random access memory (RAM)
for
temporary storage of information, a read only memory (ROM) for permanent
storage of
information, and a mass storage device 1986, such as a backing store, hard
drive, rotating
magnetic disks, solid state disks (SSD), flash memory, phase-change memory
(PCM), 3D
XPoint memory, diskette, or optical media storage device. Alternatively, the
mass storage
device can be implemented in an array of servers. Typically, the components of
the
computer system 1985 are connected to the computer using a standards-based bus
system.
The bus system can be implemented using various protocols, such as Peripheral
Component
Interconnect (PCI), Micro Channel, SCSI, Industrial Standard Architecture
(ISA) and
Extended ISA (EISA) architectures.
[0997]
The computer system 1985 includes one or more input/output (1/0)
devices and interfaces 1988, such as a keyboard, mouse, touch pad, and
printer. The I/O
devices and interfaces 1988 can include one or more display devices, such as a
monitor,
that allows the visual presentation of data to a user. More particularly, a
display device
provides for the presentation of GUIs as application software data, and multi-
media
presentations, for example. The I/O devices and interfaces 1988 can also
provide a
communications interface to various external devices. The computer system 1985
can
comprise one or more multi-media devices 1985, such as speakers, video cards,
graphics
accelerators, and microphones, for example.
Computing System Device / Operating System
[0998]
The computer system 1985 can run on a variety of computing devices,
such as a server, a Windows server, a Structure Query Language server, a Unix
Server, a
personal computer, a laptop computer, and so forth. In other embodiments, the
computer
system 1985 can run on a cluster computer system, a mainframe computer system
and/or
other computing system suitable for controlling and/or communicating with
large
databases, performing high volume transaction processing, and generating
reports from
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large databases. The computing system 1985 is generally controlled and
coordinated by an
operating system software, such as z/OS, Windows, Linux, UNIX, BSD, PHP,
SunOS,
Solaris, MacOS, 'Cloud services or other compatible operating systems,
including
proprietary operating systems. Operating systems control and schedule computer
processes
for execution, perform memory management, provide file system, networking, and
I/O
services, and provide a user interface, such as a graphical user interface
(GUI), among other
things.
Network
109991
The computer system 1985 illustrated in Figure 19E is coupled to a
network 1993, such as a LAN, WAN, or the Internet via a communication link
1992 (wired,
wireless, or a combination thereof). Network 1993 communicates with various
computing
devices and/or other electronic devices. Network 1993 is communicating with
one or more
computing systems 1994 and one or more data sources 1995. For example, the
computer
system 1985 can receive image information (e.g., including images of arteries
or an arterial
bed, information associated to the images, etc.) from computing systems 1994
and/or data
source 1995 via the network 1993 and store the received image information in
the mass
storage device 1986. The Quantification, Weighting, and Assessment Engine 1991
can then
access the mass storage device 1986 as needed to. In some embodiments, the
Quantification, Weighting, and Assessment Engine 1991 can access computing
systems
1994 and/or data sources 1995, or be accessed by computing systems 1985 and/or
data
sources 1995, through a web-enabled user access point. Connections can be a
direct
physical connection, a virtual connection, and other connection type. The web-
enabled
user access point can comprise a browser module that uses text, graphics,
audio, video, and
other media to present data and to allow interaction with data via the network
1993.
110001
The output module can be implemented as a combination of an all-points
addressable display such as a cathode ray tube (CRT), a liquid crystal display
(LCD), a
plasma display, or other types and/or combinations of displays. The output
module can be
implemented to communicate with input devices 1988 and they also include
software with
the appropriate interfaces which allow a user to access data through the use
of stylized
screen elements, such as menus, windows, dialogue boxes, tool bars, and
controls (for
example, radio buttons, check boxes, sliding scales, and so forth).
Furthermore, the output
module can communicate with a set of input and output devices to receive
signals from the
user.
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Other Systems
110011
The computing system 1985 can include one or more internal and/or
external data sources (for example, data sources 1995). In some embodiments,
one or more
of the data repositories and the data sources described above can be
implemented using a
relational database, such as DB2, Sybase, Oracle, CodeBase, and Microsoft SQL
Server
as well as other types of databases such as a flat-file database, an entity
relationship
database, and object-oriented database, and/or a record-based database.
110021
The computer system 1985 can also access one or more databases 1995.
The data sources 1995 can be stored in a database or data repository. The
computer system
1985 can access the one or more data sources 1995 through a network 1993 or
can directly
access the database or data repository through I/O devices and interfaces
1988. The data
repository storing the one or more data sources 1995 can reside within the
computer system
1985.
Additional Detail ¨ General
110031
In connection with any of the features and/or embodiments described
herein, in some embodiments, the system can be configured to analyze,
characterize, track,
and/or utilize one or more plaque features derived from a medical image. For
example, in
some embodiments, the system can be configured to analyze, characterize,
track, and/or
utilize one or more dimensions of plaque and/or an area of plaque, in two
dimensions, three
dimensions, and/or four dimensions, for example over time or changes over
time. In
addition, in some embodiments, the system can be configured to rank one or
more areas of
plaque and/or utilize such ranking for analysis. In some embodiments, the
ranking can be
binary, ordinal, continuous, and/or mathematically transformed. In some
embodiments, the
system can be configured to analyze, characterize, track, and/or utilize the
burden or one
or more geometries of plaque and/or an area of plaque. For example, in some
embodiments,
the one or more geometries can comprise spatial mapping in two dimensions,
three
dimensions, and/or four dimensions over time. As another example, in some
embodiments,
the system can be configured to analyze transformation of one or more
geometries. In some
embodiments, the system can be configured to analyze, characterize, track,
and/or utilize
diffuseness of plaque regions, such as spotty v. continuous. For example, in
some
embodiments, pixels or voxels within a region of interest can be compared to
pixels or
voxels outside of the region of interest to gain more information. In
particular, in some
embodiments, the system can be configured to analyze a plaque pixel or voxel
with another
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plaque pixel or voxel. In some embodiments, the system can be configured to
compare a
plaque pixel or voxel with a fat pixel or voxel. In some embodiments, the
system can be
configured to compare a plaque pixel or voxel with a lumen pixel or voxel. In
some
embodiments, the system can be configured to analyze, characterize, track,
and/or utilize
location of plaque or one or more areas of plaque. For example, in some
embodiments, the
location of plaque determined and/or analyzed by the system can include
whether the
plaque is within the left anterior descending (LAD), left circumflex artery
(LCx), and/or
the right coronary artery (RCA). In particular, in some embodiments, plaque in
the
proximal LAD can influence plaque in the mid-LAD, and plaque in the LCx can
influence
plaque in the LAD, such as via mixed effects modeling. As such, in some
embodiments,
the system can be configured to take into account neighboring structures. In
some
embodiments, the location can be based on whether it is in the proximal, mid,
or distal
portion of a vessel. In some embodiments, the location can be based on whether
a plaque
is in the main vessel or a branch vessel. In some embodiments, the location
can be based
on whether the plaque is myocardial facing or pericardial facing (for example
as an absolute
binary dichotomization or as a continuous characterization around 360 degrees
of an
artery), whether the plaque is juxtaposed to fat or epicardial fat or not
juxtaposed to fat or
epicardial fat, subtending a substantial amount of myocardium or subtending
small amounts
of myocardium, and/or the like. For example, arteries and/or plaques that
subtend large
amounts of subtended myocardium can behave differently than those that do not.
As such,
in some embodiments, the system can be configured to take into account the
relation to the
percentage of subtended myocardium.
110041
In connection with any of the features and/or embodiments described
herein, in some embodiments, the system can be configured to analyze,
characterize, track,
and/or utilize one or more pen-plaque features derived from a medical image.
In particular,
in some embodiments, the system can be configured to analyze lumen, for
example in two
dimensions in terms of area, three dimensions in terms of volume, and/or four
dimensions
across time. In some embodiments, the system can be configured to analyze the
vessel
wall, for example in two dimensions in terms of area, three dimensions in
terms of volume,
and/or four dimensions across time. In some embodiments, the system can be
configured
to analyze pen-coronary fat. In some embodiments, the system can be configured
to
analyze the relationship to myocardium, such as for example a percentage of
subtended
myocardial mass.
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110051
In connection with any of the features and/or embodiments described
herein, in some embodiments, the system can be configured to analyze and/or
use medical
images obtained using different image acquisition protocols and/or variables.
In some
embodiments, the system can be configured to characterize, track, analyze,
and/or
otherwise use such image acquisition protocols and/or variables in analyzing
images. For
example, image acquisition parameters can include one or more of mA, kVp,
spectral CT,
photon counting detector CT, and/or the like. Also, in some embodiments, the
system can
be configured to take into account ECG gating parameters, such as
retrospective v.
prospective ECG helical. Another example can be prospective axial v. no
gating. In
addition, in some embodiments, the system can be configured to take into
account whether
medication was used to obtain the image, such as for example with or without a
beta
blocker, with or without contrast, with or without nitroglycerin, and/or the
like. Moreover,
in some embodiments, the system can be configured to take into account the
presence or
absence of a contrast agent used during the image acquisition process. For
example. in
some embodiments, the system can be configured to normalize an image based on
a contrast
type, contrast-to-noise ratio, and/or the like. Further, in some embodiments,
the system can
be configured to take into account patient biometrics when analyzing a medical
image. For
example, in some embodiments, the system can be configured to normalize an
image to
Body Mass Index (BMI) of a subj ect, normalize an image to signal-to-noise
ratio, normalize
an image to image noise, normalize an image to tissue within the field of
view, and/or the
like. In some embodiments, the system can be configured to take into account
the image
type, such as for example CT, non-contrast CT, MRI, x-ray, nuclear medicine,
ultrasound,
and/or any other imaging modality mentioned herein.
110061
In connection with any of the features and/or embodiments described
herein, in some embodiments, the system can be configured to normalize any
analysis
and/or results, whether or not based on image processing. For example, in some
embodiments, the system can be configured to standardize any reading or
analysis of a
subject, such as those derived from a medical image of the subject, to a
normative reference
database. Similarly, in some embodiments, the system can be configured to
standardize
any reading or analysis of a subject, such as those derived from a medical
image of the
subject, to a diseased database, such as for example patients who experienced
heart attack,
patients who are ischemic, and/or the like. In some embodiments, the system
can be
configured to utilize a control database for comparison, standardization,
and/or
normalization purposes. For example, a control database can comprise data
derived from
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a combination of subjects, such as 50% of subjects who experience heart attack
and 50%
who did not, and/or the like. In some embodiments, the system can be
configured to
normalize any analysis, result, or data by applying a mathematical transform,
such as a
linear, logarithmic, exponential, and/or quadratic transform. In some
embodiments, the
system can be configured to normalize any analysis, result, or data by
applying a machine
learning algorithm.
[1007]
In connection with any of the features and/or embodiments described
herein, in some embodiments, the term "density," can refer to radiodensity,
such as in
Hounsfield units. In connection with any of the features and/or embodiments
described
herein, in some embodiments, the term -density," can refer to absolute
density, such as for
example when analyzing images obtained from imaging modalities such as dual
energy,
spectral, photon counting CT, and/or the like. In some embodiments, one or
more images
analyzed and/or accessed by the system can be normalized to contrast-to-noise.
In some
embodiments, one or more images analyzed and/or accessed by the system can be
normalized to signal-to-noise. In some embodiments, one or more images
analyzed and/or
accessed by the system can be normalized across the length of a vessel, such
as for example
along a transluminal attenuation gradient. In some embodiments, one or more
images
analyzed and/or accessed by the system can be mathematically transformed, for
example
by applying a logarithmic, exponential, and/or quadratic transformation. In
some one or
more images analyzed and/or accessed by the system can be transformed using
machine
learning.
[1008]
In connection with any of the features and/or embodiments described
herein, in some embodiments, the term -artery" can include any artery, such as
for example,
coronary, carotid, cerebral, aortic, renal, lower extremity, and/or upper
extremity.
[1009]
In connection with any of the features and/or embodiments described
herein, in some embodiments, the system can utilize additional information
obtained from
various sources in analyzing and/or deriving data from a medical image. For
example, in
some embodiments, the system can be configured to obtain additional
information from
patient history and/or physical examination. In some embodiments, the system
can be
configured to obtain additional information from other biometric data, such as
those which
can be gleaned from wearable devices, which can include for example heart
rate, heart rate
variability, blood pressure, oxygen saturation, sleep quality, movement,
physical activity,
chest wall impedance, chest wall electrical activity, and/or the like. In some
embodiments,
the system can be configured to obtain additional information from clinical
data, such as
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for example from Electronic Medical Records (EMR). In some embodiments,
additional
information used by the system can be linked to serum biomarkers, such as for
example of
cholesterol, renal function, inflammation, myocardial damage, and/or the like.
In some
embodiments, additional information used by the system can be linked to other
omics
markers, such as for example transcriptomics, proteomics, genomics,
metabolomics,
microbiomics, and/or the like.
[1010]
In connection with any of the features and/or embodiments described
herein, in some embodiments, the system can utilize medical image analysis to
derive
and/or generate assessment of a patient and/or provide assessment tools to
guide patient
assessment, thereby adding clinical importance and use. In some embodiments,
the system
can be configured to generate risk assessment at the plaque-level (for
example, will this
plaque cause heart attack and/or does this plaque cause ischemia), vessel-
level (for
example, will this vessel be the site of a future heart attack and/or does
this vessel exhibit
ischemia), and/or patient level (for example, will this patient experience
heart attack and/or
the like). In some embodiments, the summation or weighted summation of plaque
features
can contribute to segment-level features, which in turn can contribute to
vessel-level
features, which in turn can contribute to patient-level features.
[1011]
In some embodiments, the system can be configured to generate a risk
assessment of future major adverse cardiovascular events, such as for example
heart attack,
stroke, hospitalizations, unstable angina, stable angina, coronary
revascularization, and/or
the like. In some embodiments, the system can be configured to generate a risk
assessment
of rapid plaque progression, medication non-response (for example if plaque
progresses
significantly even when medications are given), benefit (or lack thereof) of
coronary
revascularization, new plaque formation in a site that does not currently have
any plaque,
development of symptoms (such as angina, shortness of breath) that is
attributable to the
plaque, ischemia and/or the like. In some embodiments, the system can be
configured to
generate an assessment of other artery consequences, such as for example
carotid (stroke),
lower extremity (claudication, critical limb ischemia, amputation), aorta
(dissection,
aneurysm), renal artery (hypertension), cerebral artery (aneurysm, rupture),
and/or the like.
Additional Detail ¨ Determination ofNon-Calcified Plaque from a Medical
Image(s)
[1012]
As discussed herein, in some embodiments, the system can be
configured to determine non-calcified plaque from a medical image, such as a
non-contrast
CT image and/or image obtained using any other image modality as those
mentioned
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herein. Also, as discussed herein, in some embodiments, the system can be
configured to
utilize radiodensity as a parameter or measure to distinguish and/or determine
non-calcified
plaque from a medical image. In some embodiments, the system can utilize one
or more
other factors, which can be in addition to and/or used as an alternative to
radiodensity, to
determine non-calcified plaque from a medical image.
[1013]
For example, in some embodiments, the system can be configured to
utilize absolute material densities via dual energy CT, spectral CT or photon-
counting
detectors. In some embodiments, the system can be configured to analyze the
geometry of
the spatial maps that -look" like plaque, for example compared to a known
database of
plaques. In some embodiments, the system can be configured to utilize
smoothing and/or
transform functions to get rid of image noise and heterogeneity from a medical
image to
help determine non-calcified plaque. In some embodiments, the system can be
configured
to utilize auto-adjustable and/or manually adjustable thresholds of
radiodensity values
based upon image characteristics, such as for example signal-to-noise ratios,
body morph
(for example obesity can introduce more image noise), and/or the like. In some
embodiments, the system can be configured to utilize different thresholds
based upon
different arteries. In some embodiments, the system can be configured to
account for
potential artifacts, such as beam hardening artifacts that may preferentially
affect certain
arteries (for example, the spine may affect right coronary artery in some
instances). In
some embodiments, the system can be configured to account for different image
acquisition
parameters, such as for example, prospective vs. retrospective ECG gating, how
much mA
and kvP, and/or the like. In some embodiments, the system can be configured to
account
for different scanner types, such as for example fast-pitch helical vs.
traditional helical. In
some embodiments, the system can be configured to account for patient-specific
parameters, such as for example heart rate, scan volume in imaged field of
view, and/or the
like. In some embodiments, the system can be configured to account for prior
knowledge.
For example, in some embodiments, if a patient had a contrast-enhanced CT
angiogram in
the past, the system can be configured to leverage findings from the previous
contrast-
enhanced CT angiogram for a non-contrast CT image(s) of the patient moving
forward. In
some embodiments, in cases where epicardial fat is not present outside an
artery, the system
can be configured to leverage other Hounsfield unit threshold ranges to depict
the outer
artery wall. In some embodiments, the system can be configured to utilize a
normalization
device, such as those described herein, to account for differences in scan
results (such as
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for example density values, etc.) between different scanners, scan parameters,
and/or the
like.
Additional Detail ¨ Determination of Cause of Change in Calcium
[1014]
As discussed herein, in some embodiments, the system can be
configured to determine a cause of change in calcium level of a subject by
analyzing one
or more medical images. In some embodiments, the change in calcium level can
be by
some external force, such as for example, medication treatment, lifestyle
change (such as
improved diet, physical activity), stenting, surgical bypass, and/or the like.
In some
embodiments, the system is configured to include one or more assessments of
treatment
and/or recommendations of treatment based upon these findings.
[1015]
In some embodiments, the system can be configured to determine a
cause of change in calcium level of a subject and use the same for prognosis.
In some
embodiments, the system can be configured to enable improved diagnosis of
atherosclerosis, stenosis, ischemia, inflammation in the pen-coronary region,
and/or the
like. In some embodiments, the system can be configured to enable improved
prognostication, such as for example forecasting of some clinical event, such
as major
adverse cardiovascular events, rapid progression, medication non-response,
need for
revascularization, and/or the like. In some embodiments, the system can be
configured to
enable improved prediction, such as for example enabling identification of who
will benefit
from what therapy and/or enabling monitoring of those changes over time. In
some
embodiments, the system can be configured to enable improved clinical decision
making,
such as for example which medications may be helpful, which lifestyle
interventions might
be helpful, which revascularization or surgical procedures may be helpful,
and/or the like.
In some embodiments, the system can be configured to enable comparison to one
or more
normative databases in order to standardize findings to a known ground truth
database.
[1016]
In some embodiments, a change in calcium level can be linear, non-
linear, and/or transformed. In some embodiments, a change in calcium level can
be on its
own or in other words involve just calcium. In some embodiments, a change in
calcium
level can be in relation to one or more other constituents, such as for
example, other non-
calcified plaque, vessel volume/area, lumen volume/area, and/or the like. In
some
embodiments, a change in calcium level can be relative. For example, in some
embodiments, the system can be configured to determine whether a change in
calcium level
is above or below an absolute threshold, whether a change in calcium level
comprises a
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continuous change upwards or downwards, whether a change in calcium level
comprises a
mathematical transform upwards or downwards, and/or the like.
[1017]
As discussed herein, in some embodiments, the system can be
configured to analyze one or more variables or parameters, such as those
relating to plaque,
in determining the cause of a change in calcium level. For example, in some
embodiments,
the system can be configured to analyze one or more plaque parameters, such as
a ratio or
function of volume or surface area, heterogeneity index, geometry, location,
directionality,
and/or radiodensity of one or more regions of plaque within the coronary
region of the
subject at a given point in time.
110181
As discussed herein, in some embodiments, the system can be
configured to characterize a change in calcium level between two points in
time. For
example, in some embodiments, the system can be configured to characterize a
change in
calcium level as one of positive, neutral, or negative. In some embodiments,
the system
can be configured to characterize a change in calcium level as positive when
the ratio of
volume to surface area of a plaque region has decreased, as this can be
indicative of how
homogeneous and compact the structure is. In some embodiments, the system can
be
configured to characterize a change in calcium level as positive when the size
of a plaque
region has decreased. In some embodiments, the system can be configured to
characterize
a change in calcium level as positive when the density of a plaque region has
increased or
when an image of the region of plaque comprises more pixels with higher
density values,
as this can be indicative of stable plaque. In some embodiments, the system
can be
configured to characterize a change in calcium level as positive when there is
a reduced
diffuseness. For example, if three small regions of plaque converge into one
contiguous
plaque, that can be indicative of non-calcified plaque calcifying along the
entire plaque
length.
[1019]
In some embodiments, the system can be configured to characterize a
change in calcium level as negative when the system determines that a new
region of plaque
has formed. In some embodiments, the system can be configured to characterize
a change
in calcium level as negative when more vessels with calcified plaque appear.
In some
embodiments, the system can be configured to characterize a change in calcium
level as
negative when the ratio of volume to surface area has increased. In some
embodiments,
the system can be configured to characterize a change in calcium level as
negative when
there has been no increase in Hounsfield density per calcium pixel.
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110201
In some embodiments, the system can be configured to utilize a
normalization device, such as those described herein, to account for
differences in scan
results (such as for example density values, etc.) between different scanners,
scan
parameters, and/or the like.
Additional Detail ¨ Quantification of Plaque, Stenosis, and/or CAD-RADS Score
110211
As discussed herein, in some embodiments, the system can be
configured to generate quantifications of plaque, stenosis, and/or CAD-RADS
scores from
a medical image. In some embodiments, as part of such quantification analysis,
the system
can be configured to determine a percentage of higher or lower density plaque
within a
plaque region. For example, in some embodiments, the system can be configured
to
classify higher density plaque as pixels or voxels that comprise a Hounsfield
density unit
above 800 and/or 1000. In some embodiments, the system can be configured to
classify
lower density plaque as pixels or voxels that comprise a Hounsfield density
unit below 800
and/or 1000. In some embodiments, the system can be configured to utilize
other
thresholds. In some embodiments, the system can be configured to report
measures on a
continuous scale, an ordinal scale, and/or a mathematically transformed scale.
110221
In some embodiments, the system can be configured to utilize a
normalization device, such as those described herein, to account for
differences in scan
results (such as for example density values, etc.) between different scanners,
scan
parameters, and/or the like.
Additional Detail ¨ Disease Tracking
110231
As discussed herein, in some embodiments, the system can be
configured to track the progression and/or regression of an arterial and/or
plaque-based
disease, such as atherosclerosis, stenosis, ischemia, and/or the like. For
example, in some
embodiments, the system can be configured to track the progression and/or
regression of a
disease over time by analyzing one or more medical images obtained from two
different
points in time. As an example, in some embodiments, one or more normal regions
from an
earlier scan can turn into abnormal regions in the second scan or vice versa.
110241
In some embodiments, the one or more medical images obtained from
two different points in time can be obtained from the same modality and/or
different
modalities. For example, scans from both points in time can be CT, whereas in
some cases
the earlier scan can be CT while the later scan can be ultrasound.
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110251
Further, in some embodiments, the system can be configured to track
the progression and/or regression of disease by identifying and/or tracking a
change in
density of one or more pixels and/or voxels, such as for example Hounsfield
density and/or
absolute density. In some embodiments, the system can be configured to track
change in
density of one or more pixels or voxels on a continuous basis and/or
dichotomous basis.
For example, in some embodiments, the system can be configured to classify an
increase
in density as stabilization of a plaque region and/or classify a decrease in
density as
destabilization of a plaque region. In some embodiments, the system can be
configured to
analyze surface area and/or volume of a region of plaque, ratio between the
two, absolute
values of surface area and/or volume, gradient(s) of surface area and/or
volume,
mathematical transformation of surface area and/or volume, directionality of a
region of
plaque, and/or the like.
110261
In some embodiments, the system can be configured to track the
progression and/or regression of disease by analyzing vascular morphology. For
example,
in some embodiments, the system can be configured to analyze and/or track the
effects of
the plaque on the outer vessel wall getting bigger or smaller, the effects of
the plaque on
the inner vessel lumen getting smaller or bigger, and/or the like.
110271
In some embodiments, the system can be configured to utilize a
normalization device, such as those described herein, to account for
differences in scan
results (such as for example density values, etc.) between different scanners,
scan
parameters, and/or the like
Global Isehemia Index
110281
Some embodiments of the systems, devices, and methods described
herein are configured to determine a global ischemia index that is
representative of risk of
ischemia for a particular subject. For example, in some embodiments, the
system is
configured to generate a global ischemia index for a subject based at least in
part on analysis
of one or more medical images and/or contributors of ischemia as well as
consequences
and/or associated factors to ischemia along the temporal ischemic cascade. In
some
embodiments, the generated global ischemia index can be used by the systems,
methods,
and devices described herein for determining and/or predicting the outcome of
one or more
treatments and/or generating or guiding a recommended medical treatment,
therapy,
medication, and/or procedure for the subject.
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110291
In particular, in some embodiments, the systems, devices, and methods
described herein can be configured to automatically and/or dynamically analyze
one or
more medical images and/or other data to identify one or more features, such
as plaque, fat,
and/or the like, for example using one or more machine learning, artificial
intelligence (Al),
and/or regression techniques. In some embodiments, one or more features
identified from
medical image data can be inputted into an algorithm, such as a second-tier
algorithm which
can be a regression algorithm or multivariable regression equation, for
automatically and/or
dynamically generating a global ischemia index. In some embodiments, the AI
algorithm
for determining a global ischemia index can be configured to utilize one or
more variables
as input, such as different temporal stages of the ischemia cascade as
described herein, and
compare the same to an output, such as myocardial blood flow, as a ground
truth. In some
embodiments, the output, such as myocardial blood flow, can be indicative of
the presence
or absence of ischemia as a binary measure and/or one or more moderations of
ischemia,
such as none, mild, moderate, severe, and/or the like.
110301
In some embodiments, the system can be configured to utilize a
normalization device, such as those described herein, to account for
differences in scan
results (such as for example density values, etc.) between different scanners,
scan
parameters, and/or the like.
110311
In some embodiments, by utilizing one or more computer-implemented
algorithms, such as for example one or more machine learning and/or regression
techniques, the systems, devices, and methods described herein can be
configured to
analyze one or more medical images and/or other data to generate a global
ischemia index
and/or a recommended treatment or therapy within a clinical reasonable time,
such as for
example within about 1 minute, about 2 minutes, about 3 minutes, about 4
minutes, about
minutes, about 10 minutes, about 20 minutes, about 30 minutes, about 40
minutes, about
50 minutes, about 1 hour, about 2 hours, about 3 hours, and/or within a time
period defined
by two of the aforementioned values.
110321
In generating the global ischemia index, in some embodiments, the
systems, devices, and methods described herein are configured to: (a)
temporally integrate
one or more variables along the "ischemic" pathway and weight their input
differently
based upon their temporal sequence in the development and worsening of
coronary
ischemia; and/or (b) integrate the contributors, associated factors and
consequences of
ischemia to improve diagnosis of ischemia. Furthermore, in some embodiments,
the
systems, devices, and methods described herein transcend analysis beyond just
the coronary
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arteries or just the left ventricular myocardium, and instead can include a
combination one
or more of: coronary arteries; coronary arteries after nitroglycerin or
vasodilator
administration; relating coronary arteries to the fractional myocardial mass;
non-cardiac
cardiac examination; relationship of the coronary-to-non-coronary cardiac;
and/or non-
cardiac examinations. In addition, in some embodiments, the systems, devices,
and
methods described herein can be configured to determine the fraction of
myocardial mass
or subtended myocardial mass to vessel or lumen volume, for example in
combination with
any of the other features described herein such as the global ischemia index,
to further
determine and/or guide a recommended medical treatment or procedure, such as
revascularization, stenting, surgery, medication such as statins, and/or the
like. As such, in
some embodiments, the systems, devices, and methods described herein are
configured to
evaluate ischemia and/or provide recommended medical treatment for the same in
a manner
that does not currently exist today, accounting for the totality of
information contributing
to ischemia.
110331
In some embodiments, the system can be configured to differentiate
between micro and macro vascular ischemia, for example based on analysis of
one or more
of epicardial coronaries, measures of myocardium densities, myocardium mass,
volume of
epicardial coronaries, and/or the like. In some embodiments, by
differentiating between
micro and macro vascular ischemia, the system can be configured to generate
different
prognostic and/or therapeutic approaches based on such differentiation.
[1034]
In some embodiments, when a medical image(s) of a patient is obtained,
such as for example using CT, MRI, and/or any other modality, not only
information
relating to coronary arteries but other information is also obtained, which
can include
information relating to the vascular system and/or the rest of the heart
and/or chest area that
is within the frame of reference. While certain technologies may simply focus
on the
information relating to coronary arteries from such medical scans, some
embodiments
described herein are configured to leverage more of the information that is
inherently
obtained from such images to obtain a more global indication of ischemia
and/or use the
same to generate and/or guide medical therapy.
110351
In particular, in some embodiments, the systems, devices, and methods
described herein are configured to examine both the contributors as well as
consequences
and associated factors to ischemia, rather than focusing only on either
contributors or
consequences. In addition, in some embodiments, the systems, devices, and
methods
described herein are configured to consider the entirety and/or a portion of
temporal
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sequence of ischemia or the "ischemic pathway.- Moreover, in some embodiments,
the
systems, devices, and methods described herein are configured to consider the
non-
coronary cardiac consequences as well as the non-cardiac associated factors
that contribute
to ischemia. Further, in some embodiments, the systems, devices, and methods
described
herein are configured to consider the comparison of pre- and post- coronary
vasodilation.
Furthermore, in some embodiments, the systems, devices, and methods described
herein
are configured to consider a specific list of variables, rather than a general
theme,
appropriately weighting their contribution to ischemia. Also, in some
embodiments, the
systems, devices, and methods described herein can be validated against
multiple
"measurements" of ischemia, including absolutely myocardial blood flow,
myocardial
perfusion, and/or flow ratios.
110361
Generally speaking, ischemia diagnosis is currently evaluated by either
stress tests (myocardial ischemia) or flow ratios in the coronary artery
(coronary ischemia),
the latter of which can include fractional flow reserve, instantaneous wave-
free pressure
ratio, hyperemic resistance, coronary flow, and/or the like. However, coronary
ischemia
can be thought of as only an indirect measure of what is going on in the
myocardium, and
myocardial ischemia can be thought of as only an indirect measure of what is
going on in
the coronary arteries.
110371
Further certain tests measure only individual components of ischemia,
such as contributors of ischemia (such as, stenosis) or sequelae of ischemia
(such as,
reduced myocardial perfusion or blood flow). However, there are numerous other
contributors to ischemia beyond stenosis, numerous associated factors that
increase
likelihood of ischemia, and many other early and late consequences of
ischemia.
110381
One technical shortcoming of such existing techniques is that if you only
look at factors that contribute or are associated with ischemia, then you are
always too
early¨i.e., in the pre-ischemia stage. Conversely, if you only look at factors
that are
consequences/sequelae of ischemia, then you are always too late¨i.e., in the
post-ischemia
stage.
110391
And ultimately, if you do not look at everything (including associative
factors, contributors, early and late consequences), you will not understand
where an
individual exists on the continuum of coronary ischemia. This may have very
important
implications in the type of therapy an individual should undergo - such as for
example
medical therapy, intensification of medical therapy, coronary revas cularizati
on by stenting,
and/or coronary revascularization by coronary artery bypass surgery. As such,
in some
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embodiments described herein, the systems, methods, and devices are configured
to
generate or determine a global ischemia index for a particular patient based
at least in part
on analysis of one or more medical images or data of the patient, wherein the
generated
global ischemia index is a measure of ischemia for the patient along the
continuum of
coronary ischemia or the ischemic cascade as described in further detail
below. In other
words, in some embodiments, unlike in existing technologies or techniques, the
global
ischemia index generated by the system can be indicative of a stage or risk or
development
of ischemia of a particular patient along the continuum of coronary ischemia
or the
ischemic cascade.
110401
Further, there can be a relationship between the things that
contribute/cause ischemia and the consequences/sequelae of ischemia that occur
in a
continuous and overlapping fashion. Thus, it can be much more accurate to
identify
ischemic individuals by combining various factors that contribute/cause
ischemia with
factors that are consequences/sequelae of ischemia.
110411
As such, in some embodiments described herein, the systems, devices,
and methods are configured to analyze one or more associative factors,
contributors, as well
as early and late consequences of ischemia in generating a global ischemia
index, which
can provide a more global indication of the risk of ischemia. Further, in some
embodiments
described herein, the systems, devices, and methods are configured to use such
generated
global ischemia index to determine and/or guide a type of therapy an
individual should
undergo, such as for example medical therapy, intensification of medical
therapy, coronary
revascularization by stenting, and/or coronary revascularization by coronary
artery bypass
surgery.
110421
As discussed herein, in some embodiments, the systems, devices, and
methods are configured to generate a global ischemia index indicative and/or
representative
of a risk of ischemia for a particular subject based on one or more medical
images and/or
other data. More specifically, in some embodiments, the system can be
configured to
generate a global ischemia index as a measurement of myocardial ischemia.
110431
In some embodiments, the generated global ischemia index provides a
much more accurate and/or direct measurement of myocardial ischemia compared
to
existing techniques. Ischemia, by its definition, is an inadequate blood
supply to an organ
or part of the body. By this definition, the diagnosis of ischemia can be best
performed by
examining the relationship of the coronary arteries (blood supply) to the
heart (organ or
part of the body). However, this is not the case as current generation tests
measure either
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the coronary arteries (e.g., FFR, iFR) or the heart (e.g. stress testing by
nuclear SPECT,
PET, CMR or echo). Because current generation tests fail to examine the
relationships of
the coronary arteries, they do not account for the temporal sequence of events
that occurs
in the evolution of ischemia (from none-to-some, as well as from mild-to-
moderate-to-
severe) or the "ischemic pathway," as will be described in more detail herein.
Quantifying
the relationship of the coronary arteries to the heart and other non-coronary
structures to
the manifestation of ischemia, as well as the temporal findings associated
with the stages
of ischemia in the ischemic cascade, can improve our accuracy of diagnosis¨as
well as
our understanding of ischemia severity¨in a manner not possible with current
generation
tests.
110441
As discussed above, no test currently exists for directly measuring
ischemia; rather, existing tests only measure certain specific factors or
surrogate markers
associated with ischemia, such as for example hypoperfusion or fractional flow
reserve
(FFR) or wall motion abnormalities. In other words, the current approaches to
ischemia
evaluation are entirely too simplistic and do not consider all of the
variables.
110451
Ischemia has historically been "measured" by stress tests. The possible
stress tests that exist include: (a) exercise treadmill ECG testing without
imaging; (b) stress
testing by single photon emission computed tomography (SPECT); (c) stress
testing by
positron emission tomography (PET); (d) stress testing by computed tomography
perfusion
(CTP); (e) stress testing by cardiac magnetic resonance (CMR) perfusion; and
(f) stress
testing by echocardiography. Also, SPECT, PET, CTP and CMR can measure
relative
myocardial perfusion, in that you compare the most normal appearing portion of
the left
ventricular myocardium to the abnormal-appearing areas. PET and CTP can have
the
added capability of measuring absolute myocardial blood flow and using these
quantitative
measures to assess the normality of blood supply to the left ventricle. In
contrast, exercise
treadmill ECG testing measures ST-segment depression as an indirect measure of
subendocardial ischemia (reduced blood supply to the inner portion of the
heart muscle),
while stress echocardiography evaluates the heart for stress-induced regional
wall motion
abnormalities of the left ventricle. Abnormal relative perfusion, absolute
myocardial blood
flow, ST segment depression and regional wall motion abnormalities occur at
different
points in the -ischemic pathway."
110461
Furthermore, in contrast to myocardial measures of the left ventricle,
alternative methods to determine ischemia involve direct evaluation of the
coronary arteries
with pressure or flow wires. The most common 2 measurements are fractional
flow reserve
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(FFR) or iFR. These techniques can compare the pressure distal to a given
coronary
stenosis to the pressure proximal to the stenosis. While easy to understand
and potentially
intuitive, these techniques do not account for important parameters that can
contribute to
ischemia, including diffuseness of -mild" stenoses, types of atherosclerosis
causing
stenosis; and these techniques take into account neither the left ventricle in
whole nor the
% left ventricle subtended by a given artery.
110471
In some embodiments, the global ischemia index is a measure of
myocardial ischemia, and leverages the quantitative information regarding the
contributors,
associated factors and consequences of ischemia. Further, in some embodiments,
the
system uses these factors to augment ischemia prediction by weighting their
contribution
accordingly. In some embodiments, the global ischemia index is aimed to serve
as a direct
measure of both myocardial perfusion and coronary pressure and to integrate
these findings
to improve ischemia diagnosis.
110481
In some embodiments, unlike existing ischemia -measurement"
techniques that focus only on a single factor or a single point in the
ischemic pathway, the
systems, devices, and methods described herein are configured to analyze
and/or use as
inputs one or more factors occurring at different points in the ischemic
pathway in
generating the global ischemia index. In other words, in some embodiments, the
systems,
devices, and methods described herein are configured to take into account the
whole
temporal ischemic cascade in generating a global ischemia index for assessing
the risk of
ischemia and/or generating a recommended treatment or therapy for a particular
subject
110491
Figure 20A illustrates one or more features of an example ischemic
pathway. While the ischemic pathway is not definitively proven, it is thought
to be as
shown in Figure 20A. Having said this, this ischemic pathway may not actually
occur in
this exact sequence. The ischemic pathway may in fact occur in different
order, or many
of the events may occur simultaneously and overlap. Nonetheless, the different
points
along the ischemic pathway can occur at different points in time, thereby
adding a temporal
aspect in the development of ischemia that some embodiments described herein
consider.
110501
As illustrated in Figure 20A, the ischemic pathway can illustrate
different conditions that can occur when you have a blockage in a heart artery
that reduces
blood supply to the heart muscle. In other words, the ischemic pathway can
illustrate a
sequence of pathophysiologic events caused by coronary artery disease. As
illustrated in
Figure 20A, ischemia can occur or gradually develop in a number of different
steps rather
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than a binary concept. The ischemic pathway illustrates different conditions
that may arise
as a patient gets more and more ischemic.
110511
Different existing tests can show ischemia at different stages along the
ischemic pathway. For example, a nuclear stress test can show ischemia sooner
rather than
an echo test, because nuclear imaging probes hypoperfusion, which is an
earlier event in
the ischemic pathway, whereas a stress echocardiography probes a later event
such as
systolic dysfunction. Further, an exercise treadmill EKG testing can show
ischemia
sometime after an echo stress test, as if EKG testing becomes abnormal ECG
changes will
show. In addition, a PET scan can measure flow maldistribution, and as such
can show
signs of ischemia prior to before nuclear stress tests. As such, different
tests exist for
measuring different conditions and steps along the ischemic cascade. However,
there does
not exist a global technique that takes into account all of these different
conditions that arise
throughout the course of the ischemic pathway. As such, in some embodiments
herein, the
systems, devices and methods are configured to analyze multiple different
measures along
the temporal ischemic pathway and/or weight them differently in generating a
global
ischemia index, which can be used to diagnose ischemia and/or provide a
recommended
therapy and/or treatment. In some embodiments, such multiple measures along
the
temporal ischemic pathway can be weighted differently in generating the global
ischemic
index; for example, certain measures that come earlier can be weighted less
than those
measures that arise later in the ischemic cascade in some embodiments. More
specifically,
in some embodiments, one or more measures of ischemia can be weighted from
less to
more heavily in the following general order: flow maldistribution,
hypoperfusion, diastolic
dysfunction, systolic dysfunction, ECG changes, angina, and/or regional wall
motion
abnormality.
110521
In some embodiments, the system can be configured to take the temporal
sequence of the ischemic pathway and integrate and weight various conditions
or events
accordingly in generating the global ischemia index. Further, in some
embodiments, the
system can be configured to identify certain conditions or "associative
factors" well before
actual signs ischemia occur, such as for example fatty liver which is
associated with
diabetes which is associated with coronary disease. In other words, in some
embodiments,
the system can be configured to integrate one or more factors that are
associated, causal,
contributive, and/or consequential to ischemia, take into account the temporal
sequence of
the same and weight them accordingly to generate an index representative of
and/or
predicting risk of ischemia and/or generating a recommend treatment.
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110531
As discussed herein, the global ischemia index generated by some
embodiments provide substantial technical advantages over existing techniques
for
assessing ischemia, which have a number of shortcomings. For example, coronary
artery
examination alone does not consider the wealth of potential contributors to
ischemia,
including for example: (1) 3D flow (lumen, stenosis, etc.); (2) endothelial
function /
vasodilation / vasoconstrictive ability of the coronary artery (e.g., plaque
type, burden,
etc.); (3) inflammation that may influence the vasodilation / vasoconstrictive
ability of the
coronary artery (e.g., epicardial adipose tissue surrounding the heart);
and/or (4) location
(plaques that face the myocardium are further away from the epicardial fat,
and may be less
influenced by the inflammatory contribution of the fat. Plaques that are at
the bifurcation,
trifurcation or proximal/ostial location may influence the likelihood of
ischemia more than
those that are not at the bifurcation, trifurcation or proximaPostial
location).
110541
One important consideration is that current methods for determining
ischemia by CT rely primarily on computational fluid dynamics which, by its
definition,
does not include fluid-structure interactions (FSI). However, the use of FSI
requires the
understanding of the material densities of coronary artery vessels and their
plaque
constituents, which is not known well.
110551
Thus, in some embodiments described herein, one important component
is that the lateral boundary conditions in the coronary arteries (lumen wall,
vessel wall,
plaque) can be known in a relative fashion by setting Hounsfield unit
thresholds that
represent different material densities or setting absolute material densities
to pixels based
upon comparison to a known material density (i.e., normalization device in our
prior
patent). By doing so, and coupling to a machine learning algorithm, some
embodiments
herein can improve upon the understanding of fluid-structure interactions
without having
to understand the exact material density, which may inform not only ischemia
(blood flow
within the vessel) but the ability of a plaque to "fatigue" over time.
110561
In addition, in some embodiments, the system is configured to take into
account non-coronary cardiac examination and data in addition to coronary
cardiac data.
The coronary arteries supply blood to not only the left ventricle but also the
other chambers
of the heart, including the left atrium, the right ventricle and the right
atrium. While
perfusion is not well measured in these chambers by current generation stress
tests, in some
embodiments, the end-organ effects of ischemia can be measured in these
chambers by
determining increases in blood volume or pressure (i.e., size or volumes).
Further, if blood
volume or pressure increases in these chambers, they can have effects of
"backing up-
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blood flow due to volume overload into the adjacent chambers or vessels. So,
as a chain
reaction, increases in left ventricular volume may increase volumes in
sequential order of:
(1) left atrium; (2) pulmonary vein; (3) pulmonary arteries; (4) right
ventricle; (5) right
atrium; (6) superior vena cava or inferior vena cava. In some embodiments, by
taking into
account non-coronary cardiac examination, the system can be configured to
differentiate
the role of ischemia on the heart chambers based upon how "upstream- or
"downstream"
they are in the ischemic pathway.
110571
Moreover, in some embodiments, the system can be configured to take
into account the relationship of coronary arteries and non-coronary cardiac
examination.
Existing methods of ischemia determination limit their examination to either
the coronary
arteries (e.g., FFR, iFR) or the heart left ventricular myocardium. However,
in some
embodiments herein, the relationship of the coronary arteries with the heart
chambers may
act synergistically to improve our diagnosis of ischemia.
110581
Further, in some embodiments, the system can be configured to take into
account non-cardiac examination. At present, no method of coronary /
myocardial
ischemia determination accounts for the effects of clinical contributors
(e.g., hypertension,
diabetes) on the likelihood of ischemia. However, these clinical contributors
can manifest
several image-based end-organ effects which may increase the likelihood of an
individual
to manifest ischemia. These can include such image-based signs such as aortic
dimension
(aneurysms are a common end-organ effect of hypertension) and/or non-alcoholic
steatohepatitis (fatty liver is a common end-organ effect of diabetes or pre-
diabetes). As
such, in some embodiments, the system can be configured to account for these
features to
augment the likelihood of ischemia diagnosis on a scan-specific,
individualized manner.
110591
Furthermore, at present, no method of myocardial ischemia
determination incorporates other imaging findings that may not be
ascertainable by a single
method, but can be determined through examination by other methods. For
example, the
ischemia pathway is often thought to occur, in sequential order, from
metabolic alterations
(laboratory tests), perfusion abnormalities (stress perfusion), diastolic
dysfunction
(echocardiogram), systolic dysfunction (echocardiogram or stress test), ECG
changes
(ECG) and then angina (chest pain, human patient report). In some embodiments,
the
system can be configured to integrate these factors with the image-based
findings of the
CT scan and allow for improvements in ischemia determination by weighting
these
variables in accordance with their stage of the ischemic cascade.
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110601
As described herein, in some embodiments, the systems, methods, and
devices are configured to generate a global ischemia index to diagnose
ischemia. In some
embodiments, the global ischemia index considers the totality of findings that
contribute to
ischemia, including, for example one or more of: coronary arteries +
nitroglycerin /
vasodilator administration + relating coronary arteries to the fractional
myocardial mass +
non-cardiac cardiac examination + relationship of the coronary-to-non-coronary
cardiac +
non-cardiac examinations, and/or a subset thereof In some embodiments, the
global
ischemia index provides weighted increases of variables to contribution of
ischemia based
upon where the image-based finding is in the pathophysiology of ischemia. In
some
embodiments, in generating the global ischemia index, the system is configured
to input
into a regression model one or more factors that are associative,
contributive, casual, and/or
consequential to ischemia to optimally diagnose whether a subject ischemic or
not.
110611
Figure 20B is a block diagram depicting one or more contributors and
one or more temporal sequences of consequences of ischemia utilized by an
example
embodiment(s) described herein. As illustrated in Figure 20B, in some
embodiments, the
system can be configured to analyze a number of factors, including
contributors, associated
factors, causal factors, and/or consequential factors of ischemia and/or use
the same as input
for generating the global ischemia index. Some of such factors can include
those conditions
shown in Figure 20B. For example, signs of a fatty liver and/or emphysema in
the lungs
can be associated factors used by the system as inputs for generating the
global ischemia
index. Some examples of contributors used as an input(s) by the system can
include the
inability to vasodilate with nitric oxide and/or nitroglycerin, low density
non-calcified
plaque, small artery, and/or the like. Some examples of early consequences of
ischemia
used as an input(s) by the system can include reduced perfusion in the heart
muscle,
increase in size of the volume of the heart. An example of late consequences
of ischemia
used as an input(s) by the system can include blood starting to back up into
other chambers
of heart in addition to the left ventricle.
110621
In some embodiments, the global ischemia index accounts for the direct
contributors to ischemia, the early consequences of ischemia, the late
consequences of
ischemia, the associated factors with ischemia and other test findings in
relation to
ischemia. In some embodiments, one or more these factors can be identified
and/or derived
automatically, semi-automatically, and/or dynamically using one or more
algorithms, such
as a machine learning algorithm. Some example algorithms for identifying such
features
are described in more detail below. Without such trained algorithms, it can be
difficult, if
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not impossible, to take into account all of these factors in generating the
global ischemia
index within a reasonable time.
110631
In some embodiments, these factors, weighted differently and
appropriately, can improve diagnosis of ischemia. Figure 20C is a block
diagram depicting
one or more features of an example embodiment(s) for determining ischemia by
weighting
different factors differently. In some embodiments, in generating the global
ischemia
index, the system is configured to take into account the temporal aspect of
the ischemic
cascade and weight one or more factors according to the temporal aspect, for
example
where early signs of ischemia can be weighted less heavily compared to later
signs of
ischemia. In some embodiments, the system can automatically and/or dynamically
determine the different weights for each factor, for example using a
regression model. In
some embodiments, the system can be configured to derive one or more
appropriate
weighting factors based on previous analysis of data to determine which factor
should be
more or less heavily weighted compared to others. In some embodiments, a user
can guide
and/or otherwise provide input for weighting different factors.
110641
As described herein, in some embodiments, the global ischemia index
can be generated by a machine learning algorithm and/or a regression algorithm
that
condenses this multidimensional information into an output of -ischemia" or -
no ischemia"
when compared to a -gold standard" of ischemia, as measured by myocardial
blood flow,
myocardial perfusion or flow ratios. In some embodiments, the system can be
configured
to output an indication of moderation of ischemia, such none, mild, moderate,
severe,
and/or the like. In some embodiments, the output indication of ischemia can be
on a
continuous scale.
110651
Figure 20D is a block diagram depicting one or more features of an
example embodiment(s) for calculating a global ischemia index. As illustrated
in Figure
20D, in some embodiments, the system can be configured to validate the
outputted global
ischemia index against absolute myocardial blood flow, which can be measured
for
example by PET and/or CT scans to measure different regions of the heart to
see if there
are different flows of blood within different regions. As absolute myocardial
blood flow
can provide an absolute value of volume per time, in some embodiments, the
system can
be configured to compare the absolute myocardial blood flow of one region to
another
region, which would not be possible using relative measurements, such as for
example
using nuclear stress testing.
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110661
As discussed herein, in some embodiments, the systems, devices, and
methods can be configured to utilize a machine learning algorithm and/or
regression
algorithm for analyzing and/or weighting different factors for generating the
global
ischemia index. By doing so, in some embodiments, the system can be configured
to take
into account one or more statistical and/or machine learning considerations.
More
specifically, in some embodiments, the system can be configured to
deliberately duplicate
the contribution of particular variables. For example, in some embodiments,
non-calcified
plaque (NCP), low density non-calcified plaque (LD-NCP), and/or high-risk
plaque (HRP)
may all contribute to ischemia. In traditional statistics, collinearity could
be a reason to
select only one out of these three variables, but by utilizing machine
learning in some
embodiments, the system may allow for data driven exploration of the
contribution of
multiple variables, even if they share a specific feature. In addition, in
some embodiments,
the system may take into account certain temporal considerations when training
and/or
applying an algorithm for generating the global ischemia index. For example,
in some
embodiments, the system can be configured to give greater weight to
consequences/sequelae rather than causes/contributors, as the
consequences/sequelae have
already occurred.
110671
In addition, in some situations, coronary vasodilation is induced before
a coronary CT scan because it allows the coronary arteries to be maximum in
their
size/volume. Nitroglycerin is an endothelium-independent vasodilator as
compared to, for
example, nitric oxide, which is an endothelium-dependent vasodilator. As
nitroglycerin-
induced vasodilation occurs in the coronary arteries¨and, because a "timing"
iodine
contrast bolus is often administered before the actual coronary CT angiogram,
comparison
of the volume of coronary arteries before and after a nitroglycerin
administration may allow
a direct evaluation of coronary vasodilatory capability, which may
significantly augment
accurate ischemia diagnosis. Alternatively, an endothelium-dependent
vasodilator¨like
nitric oxide or carbon dioxide¨may allow for augmentation of coronary artery
size in a
manner that can be either replaced or coupled to endothelium-independent
vasodilation (by
nitroglycerin) to maximize understanding of the ability of coronary arteries
to vasodilate.
110681
In some embodiments, the system can be configured to measure
vasodilatory effects, for example by measuring the diameter of one or more
arteries before
and/or after administration of nitroglycerin and/or nitric oxide, and use such
vasodilatory
effects as a direct measurement or indication of ischemia. Alternatively
and/or in addition
to the foregoing, in some embodiments, the system can be configured to measure
such
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vasodilatory effects and use the same as an input in determining or generating
the global
ischemia index and/or developing a recommended medical therapy or treatment
for the
subj ect.
110691
Further, in some embodiments, the system can be configured to relate
the coronary arteries to the heart muscle that it provides blood to. In other
words, in some
embodiments, the system can be configured to take into account fractional
myocardial mass
when generating a global ischemia index. For ischemia diagnosis, stress
testing can be, at
present, limited to the left ventricle. For example, in stress echocardiogram
(ultrasound),
the effects of stress-induced left ventricular regional wall motion
abnormalities are
examined, while in SPECT, PET and cardiac MM, the effects of stress-induced
left
ventricular myocardial perfusion are examined. However, no currently existing
technique
relates the size (volume), geometry, path and relation to other vessels with
the % fractional
myocardial mass subtended by that artery. Further, one assumes that the
coronary artery
distribution is optimal but, in many people, it may not be. Therefore,
understanding an
optimization platform to compute optimal blood flow through the coronary
arteries may be
useful in guiding treatment decisions.
110701
As such, in some embodiments, the system is configured to determine
the fractional myocardial mass or the relationship of coronary arteries to the
left ventricular
myocardium that they subtend. In particular, in some embodiments, the system
is
configured to determine and/or tack into account the subtended mass of
myocardium to the
volume of arterial vessel. Historically, myocardial perfusion evaluation for
myocardial
ischemia has been performed using stress tests, such as nuclear SPECT, PET,
cardiac MRI
or cardiac CT perfusion. These methods have relied upon a 17-segment
myocardial model,
which classifies perfusion defects by location. There can be several
limitations to this,
including: (1) assuming that all 17 segments have the same size; (2) assuming
that all 17
segments have the same prognostic importance; and (3) does not relate the
myocardial
segments to the coronary arteries that provide blood supply to them.
110711
As such, to address such shortcomings, in some embodiments, the
system can be configured to analyze fractional myocardial mass (FMM).
Generally
speaking, FMM aims to relate the coronary arteries to the amount of myocardium
that they
subtend. These can have important implications on prognostication and
treatment. For
example, a patient may have a 70% stenosis in an artery, which has been a
historical cut
point where coronary revascularization (stenting) is considered. However,
there may be
very important prognostic and therapeutic implications for patients who have a
70%
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stenosis in an artery that subtends 1% of the myocardium vs. a 70% stenosis in
an artery
that subtends 15% of the my ocardi um.
110721
This FMM has been historically calculated using a "stem-and-crown"
relationship between the myocardium on CT scans and the coronary arteries on
CT scans
and has been reported to have the following relationship: M = kL3/4, where M =
mass, k =
constant, and L = length.
110731
However, this relationship, while written about quite frequently, has not
been validated extensively. Nor have there been any cut points that can
effectively guide
therapy. The guidance of therapy can come in many regards, including: (1)
decision to
perform revascularization: high FMM, perform revascularization to improve
event-free
survival; low FMM, medical therapy alone without revascularization; (2)
different medical
therapy regimens: high FMM, give several medications to improve event-free
survival; low
FMM, give few medications; (3) prognostication: high FMM, poor prognosis; low
FMM,
good prognosis.
110741
Further, in the era of 3D imaging, the M=kL relationships should be
expanded to the M=kV relationship, where V = volume of the vessel or volume of
the
lumen. As such, in some embodiments, the system is configured to (1) describe
the
allometric scaling law in 3 dimensions, i.e., MAVn; (2) use FMM as a cut point
to guide
coronary revascularization; and/or (3) use FMM cut points for clinical
decision making,
including (a) use of medications vs. not, (b) different types of medications
(cholesterol
lowering, vasodilators, heart rate slowing medications, etc.) based upon FMM
cut points;
(c) number of medications based upon FMM cut points; and/or (d)
prognostication based
upon FMM cut points. In some embodiments, the use of FMM cut points by 3D FMM
calculations can improve decision making in a manner that improves event-free
survival.
110751
As described above, in some embodiments, the system can be
configured to utilize one or more contributors or causes of ischemia as inputs
for generating
a global ischemia index. An example of a contributor or cause of ischemia that
can be
utilized as input and/or analyzed by the system can include vessel caliber. In
particular, in
some embodiments, the system can be configured to analyze and/or utilize as an
input the
percentage diameter of stenosis, wherein the greater the stenosis the more
likely the
ischemia. In addition, in some embodiments, the system can be configured to
analyze
and/or utilize as in input lumen volume, wherein the smaller the lumen volume,
the more
likely the ischemia. In some embodiments, the system can be configured to
analyze and/or
utilize as an input lumen volume indexed to % fractional myocardial mass, body
surface
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area (BSA), body mass index (BMI), left ventricle (LV) mass, overall heart
size, wherein
the smaller the lumen volume, the more likely the ischemia. In some
embodiments, the
system can be configured to analyze and/or utilize as an input vessel volume,
wherein the
smaller the vessel volume, the more likely the ischemia. In some embodiments,
the system
can be configured to analyze and/or utilize as an input minimal luminal
diameter (MLD),
minimal luminal are (MLA), and/or a ratio between MLD and MLA, such as
MLD/MLA.
110761
Another example contributor or cause of ischemia that can be utilized as
input and/or analyzed by the system can include plaque, which may have marked
effects
on the ability of an artery to vasodilate/ vasoconstrict. In particular, in
some embodiments,
the system can be configured to analyze and/or utilize as an input non-
calcified plaque
(NCP), which may cause greater endothelial dysfunction and inability to
vasodilate to
hyperemia. In some embodiments, the system may utilize one or more arbitrary
cutoffs for
analyzing NCP, such as binary, trinary, and/or the like for necrotic core,
fibrous, and/or
fibrofatty. In some embodiments, the system may utilize continuous density
measures for
NCP. Further, in some embodiments, the system may analyze NCP for dual energy,
monochromatic, and/or material basis decomposition. In some embodiments, the
system
can be configured to analyze and/or identify plaque geometry and/or plaque
heterogeneity
and/or other radiomics features. In some embodiments, the system can be
configured to
analyze and/or identify plaque facing the lumen and/or plaque facing
epicardial fat. In
some embodiments, the system can be configured to derive and/or identify
imaging-based
information, which can be provided directly to the algorithm for generating
the global
ischemia index.
110771
In some embodiments, the system can be configured to analyze and/or
utilize as an input low density NCP, which may cause greater endothelial
dysfunction and
inability to vasodilate to hyperemia, for example using one or more specific
techniques
described above in relation to NCP. In some embodiments, the system can be
configured
to analyze and/or utilize as an input calcified plaque (CP), which may cause
more laminar
flow, less endothelial dysfunction and less ischemia. In some embodiments, the
system
may utilize one or more arbitrary cutoffs, such as 1K plaque (plaques >1000
Hounsfield
units), and/or continuous density measures for CP.
[1078]
In some embodiments, the system can be configured to analyze and/or
utilize as an input the location of plaque. In particular, the system may
determine that
myocardial facing plaque may be associated with reduced ischemia due to its
proximity to
myocardium (e.g., myocardial bridging rarely has atherosclerosis). In some
embodiments,
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the system may determine that pericardial facing plaque may be associated with
increased
ischemia due to its proximity to pen-coronary adipose tissue. In some
embodiments, the
system may determine that bifurcation and/or trifurcation lesions may be
associated with
increased ischemia due to disruptions in laminar flow.
110791
In some embodiments, visualization of three-dimensional plaques can
be generated and/or provided by the system to a user to improve understanding
to the
human observer of where plaques are in relationship to each other and/or to
the
myocardium to the pericardium. For example, in a particular vein, the system
may be
configured to allow the visualization of all the plaques on a single 2D image.
As such, in
some embodiments, the system can allow for all of the plaques to be visualized
in a single
view, with color-coded and/or shadowed labels and/or other labels to plaques
depending
on whether they are in the 2D field of view, or whether they are further away
from the 2D
field of view. This can be analogous to the maximum intensity projection view,
which
highlights the lumen that is filled with contrast agent, but applies an
intensity projection
(maximum, minimum, average, ordinal) to the plaques of different distance from
the field
of view or of different densities.
110801
In some embodiments, the system can be configured to visualize plaque
using maximum intensity projection (M1P) techniques. In some embodiments, the
system
can be configured to visualize plaque in 2D, 3D, and/or 4D, for example using
MIP
techniques and/or other techniques. such as volume rendering techniques (VRT).
More
specifically, for 4D, in some embodiments, the system can be configured to
visualize
progression of plaque in terms of time. In some embodiments, the system can be
configured
to visualize on an image and/or on a video and/or other digital support the
lumen and/or the
addition of plaque in 2D, 3D, and/or 4D. In some embodiments, the system can
be
configured to show changes in time or 4D. In some embodiments, the system can
be
configured to take multiple scans taken from different points in time and/or
integrate all or
some of the information with therapeutics. In some embodiments, based on the
same, the
system can be configured to decide on changes in therapy and/or determine
prognostic
information, for example assessing for therapy success.
110811
Another example contributor or cause of ischemia that can be utilized as
input and/or analyzed by the system can include fat. In some embodiments, the
system can
be configured to analyze and/or utilize as an input pen-coronary adipose
tissue, which may
cause ischemia due to inflammatory properties that cause endothelial
dysfunction. In some
embodiments, the system can be configured to analyze and/or utilize as an
input epicardial
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adipose tissue, which may be a cause of overall heart inflammation. In some
embodiments,
the system can be configured to analyze and/or utilize as input epicardial fat
and/or
radiomics or imaging-based information provided directly to the algorithm,
such as for
example heterogeneity, density, density change away from the vessel, volume,
and/or the
like.
110821
As described above, in some embodiments, the system can be
configured to utilize one or more consequences or sequelae of ischemia as
inputs for
generating a global ischemia index. An example consequence or sequelae of
ischemia that
can be utilized as input and/or analyzed by the system can be related to the
left ventricle.
For example, in some embodiments, the system can be configured to analyze the
perfusion
and/or Hounsfield unit density of the left ventricle, which can be global
and/or related to
the percentage of fractional myocardial mass. In some embodiments, the system
can be
configured to analyze the mass of the left ventricle, wherein the greater the
mass, the greater
the potential mismatch between lumen volume to LV mass, which can be global as
well as
related to the percentage of fractional myocardial mass. In some embodiments,
the system
can be the system can be configured to analyze the volume of the left
ventricle, wherein an
increase in the left ventricle volume can be a direct sign of ischemia. In
some embodiments,
the system can be configured to analyze and/or utilize as input density
measurements of the
myocardium, which can be absolute and/or relative, for example using a sticker
or
normalization device. In some embodiments, the system can be configured to
analyze
and/or use as input regional and/or global changes in densities. In some
embodiments, the
system can be configured to analyze and/or use as input endo, mid-wall, and/or
epicardial
changes in densities. In some embodiments, the system can be configured to
analyze and/or
use as input thickness, presence of fat and/or localization thereof, presence
of calcium,
heterogeneity, radiomic features, and/or the like.
110831
Another example consequence or sequelae of ischemia that can be
utilized as input and/or analyzed by the system can be related to the right
ventricle. For
example, in some embodiments, the system can be configured to analyze the
perfusion
and/or Hounsfield unit density of the right ventricle, which can be global
and/or related to
the percentage of fractional myocardial mass. In some embodiments, the system
can be
configured to analyze the mass of the right ventricle, wherein the greater the
mass, the
greater the potential mismatch between lumen volume to LV mass, which can be
global as
well as related to the percentage of fractional myocardial mass. In some
embodiments, the
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system can be the system can be configured to analyze the volume of the right
ventricle,
wherein an increase in the right ventricle volume can be a direct sign of
ischemia.
110841
Another example consequence or sequelae of ischemia that can be
utilized as input and/or analyzed by the system can be related to the left
atrium. For
example, in some embodiments, the system can be configured to analyze the
volume of the
left atrium, in which an increased left atrium volume can occur in patients
who become
ischemic and go into heart failure.
110851
Another example consequence or sequelae of ischemia that can be
utilized as input and/or analyzed by the system can be related to the right
atrium. For
example, in some embodiments, the system can be configured to analyze the
volume of the
right atrium, in which an increased right atrium volume can occur in patients
who become
ischemic and go into heart failure.
110861
Another example consequence or sequelae of ischemia that can be
utilized as input and/or analyzed by the system can be related to one or more
aortic
dimensions. For example, an increased aortic size as a long-standing
contributor of
hypertension may be associated with the end-organ effects of hypertension on
the coronary
arteries (resulting in more disease) and the LV mass (resulting in more LV
mass-coronary
lumen volume mismatch).
110871
Another example consequence or sequelae of ischemia that can be
utilized as input and/or analyzed by the system can be related to the
pulmonary veins. For
example, for patients with volume overload, engorgement of the pulmonary veins
may be
a significant sign of ischemia.
110881
As described above, in some embodiments, the system can be
configured to utilize one or more associated factors of ischemia as inputs for
generating a
global ischemia index. An example associated factor of ischemia that can be
utilized as
input and/or analyzed by the system can be related to the presence of fatty
liver or non-
alcoholic steatohepatitis, which is a condition that can be diagnosed by
placing regions of
interest (ROIs) in the liver to measure Hounsfield unit densities. Another
example
associated factor of ischemia that can be utilized as input and/or analyzed by
the system
can be related to emphysema, which is a condition that can be diagnosed by
placing regions
of interest in the lung to measure Hounsfield unit densities. Another example
associated
factor of ischemia that can be utilized as input and/or analyzed by the system
can be related
to osteoporosis, which is a condition that can be diagnosed by placing regions
of interest in
the spine. Another example associated factor of ischemia that can be utilized
as input
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and/or analyzed by the system can be related to mitral annular calcification,
which is a
condition that can be diagnosed by identifying calcium (e.g., HU>350 etc.) in
the mitral
annulus. Another example associated factor of ischemia that can be utilized as
input and/or
analyzed by the system can be related to aortic valve calcification, which is
a condition that
can be diagnosed by identifying calcium in the aortic valve. Another example
associated
factor of ischemia that can be utilized as input and/or analyzed by the system
can be related
to aortic enlargement, often seen in hypertension, can reveal an enlargement
in the proximal
aorta due to long-standing hypertension. Another example associated factor of
ischemia
that can be utilized as input and/or analyzed by the system can be related to
mitral valve
calcification, which can be diagnosed by identifying calcium in the mitral
valve.
110891
As discussed herein, in some embodiments, the system can be
configured to utilize one or more inputs or variables for generating a global
ischemia index,
for example by inputting the like into a regression model or other algorithm.
In some
embodiments, the system can be configured to use as input one or more
radiomics features
and/or imaging-based deep learning. In some embodiments, the system can be
configured
to utilize as input one or more of patient height, weight, sex, ethnicity,
body surface,
previous medication, genetics, and/or the like.
110901
In some embodiments, the system can be configured to analyze and/or
utilize as input calcium, separate calcium densities, localization calcium to
lumen, volume
of calcium, and/or the like. In some embodiments, the system can be configured
to analyze
and/or utilize as input contrast vessel attenuation. In particular, in some
embodiments, the
system can be configured to analyze and/or utilize as input average contrast
in the lumen
in the beginning of a segment and/or average contrast in the lumen at the end
of that
segment. In some embodiments, the system can be configured to analyze and/or
utilize as
input average contrast in the lumen in the beginning of the vessel to the
beginning of the
distal segment of that vessel, for example because the end can be too small in
some
instances.
110911
In some embodiments, the system can be configured to analyze and/or
utilize as input plaque heterogeneity. In particular, in some embodiments, the
system can
be configured to analyze and/or utilize as input calcified plaque volume
versus and/or non-
calcified plaque volume. In some embodiments, the system can be configured to
analyze
and/or utilize as input standard deviation of one or more of the 3 different
components of
plaque.
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110921
In some embodiments, the system can be configured to analyze and/or
utilize as input one or more vasodilation metrics. In particular, in some
embodiments, the
system can be configured to analyze and/or utilize as input the highest
remodeling index of
a plaque. In some embodiments, the system can be configured to analyze and/or
utilize as
input the highest, average, and/or smallest thickness of plaque, and for
example for its
calcified and/or non-calcified components. In some embodiments, the system can
be
configured to analyze and/or utilize as input the highest remodeling index
and/or lumen
area. In some embodiments, the system can be configured to analyze and/or
utilize as input
the lesion length and/or segment length of plaque.
110931
In some embodiments, the system can be configured to analyze and/or
utilize as input bifurcation lesion, such as for example the presence of
absence thereof In
some embodiments, the system can be configured to analyze and/or utilize as
input
coronary dominance, for example left dominance, right dominance, and/or
codominance.
In particular, in some embodiments, if left dominance, the system can be
configured to
disregard and/or weight less one or more right coronary metrics. Similarly, if
right
dominance, the system can be configured to disregard and/or weight less one or
more left
coronary metrics.
110941
In some embodiments, the system can be configured to analyze and/or
utilize as input one or more vascularization metrics. In particular, in some
embodiments,
the system can be configured to analyze and/or utilize as input the volume of
the lumen of
one or more, some, or all vessels. In some embodiments, the system can be
configured to
analyze and/or utilize as input the volume of the lumen of one or more
secondary vessels,
such as for example, non-right coronary artery (non-RCA), left anterior
descending artery
(LAD) vessel, circumflex (CX) vessel, and/or the like. In some embodiments,
the system
can be configured to analyze and/or utilize as input the volume of vessel
and/or volume of
plaque and/or a ratio thereof
110951
In some embodiments, the system can be configured to analyze and/or
utilize as input one or more inflammation metrics. In particular. in some
embodiments, the
system can be configured to analyze and/or utilize as input the average
density of one or
more pixels outside a lesion, such as for example 5 pixels and/or 3 or 4
pixels of 5,
disregarding the first 1 or 2 pixels. in some embodiments, the system can be
configured to
analyze and/or utilize as input the average density of one or more pixels
outside a lesion
including the first 2/3 of each vessel that is not a lesion or plaque. In some
embodiments,
the system can be configured to analyze and/or utilize as input one or more
pixels outside
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a lesion and/or the average of the same pixels on a 3mm section above the
proximal right
coronary artery (RI) if there is no plaque in that place. In some embodiments,
the system
can be configured to analyze and/or utilize as input one or more ratios of any
factors and/or
variables described herein.
110961
As described above, in some embodiments, the system can be
configured to utilize one or more machine learning algorithms in identifying,
deriving,
and/or analyzing one or more inputs for generating the global ischemia index,
including for
example one or more direct contributors to ischemia, early consequences of
ischemia, late
consequences of ischemia, associated factors with ischemia, and other test
findings in
relation to ischemia. In some embodiments, one or more such machine learning
algorithms
can provide fully automated quantification and/or characterization of such
factors.
110971
As an example, in some embodiments, the system can be configured to
utilize one or more machine learning algorithms to identify, derive, and/or
analyze inferior
vena cava from one or more medical images. Measures of inferior vena cava can
be of
high importance in patients with right-sided heart failure and tricuspid
regurgitation.
110981
In addition, in some embodiments, the system can be configured to
utilize one or more machine learning algorithms to identify, derive, and/or
analyze the
interatrial septum from one or more medical images. Interatrial septum
dimensions can be
vital for patients undergoing left-sided transcatheter procedures.
110991
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze
descending thoracic
aorta from one or more medical images. Measures of descending thoracic aorta
can be of
critical importance in patients with aortic aneurysms, and for population-
based screening
in long-time smokers.
[1100]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze the
coronary sinus
from one or more medical images. Coronary sinus dimensions can be vital for
patients
with heart failure who are undergoing biventricular pacing. In some
embodiments, by
analyzing the coronary sinus, the system can be configured to derive all or
some
myocardium blood flow, which can be related to coronary volume, myocardium
mass. In
addition, in some embodiments, the system can be configured to analyze,
derive, and/or
identify hypertrophic cardiomyopathy (HCM), other hypertrophies, ischemia,
and/or the
like to derive ischemia and/or microvascular ischemia.
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[1101]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze the
anterior mitral
leaflet from one or more medical images. For a patient being considered for
surgical or
transcatheter mitral valve repair or replacement, no current method currently
exists to
measure anterior mitral leaflet dimensions.
[1102]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze the left
atrial
appendage from one or more medical images. Left atrial appendage morphologies
are
linked to stroke in patients with atrial fibrillation, but no automated
characterization
solution exists today.
[1103]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze the left
atrial free wall
mass from one or more medical images. No current method exists to accurately
measure
left atrial free wall mass, which may be important in patients with atrial
fibrillation.
[1104]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze the left
ventricular
mass from one or more medical images. Certain methods of measuring left
ventricular
hypertrophy as an adverse consequence of hypertension rely upon
echocardiography,
which employs a 2D estimated formula that is highly imprecise. 3D imaging by
magnetic
resonance imaging (MRI) or computed tomography (CT) are much more accurate,
but
current software tools are time-intensive and imprecise_
[1105]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze the left
atrial volume
from one or more medical images. Determination of left atrial volume can
improve
diagnosis and risk stratification in patients with and at risk of atrial
fibrillation.
[1106]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze the left
ventricular
volume from one or more medical images. Left ventricular volume measurements
can
enable determination of individuals with heart failure or at risk of heart
failure.
[1107]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze the left
ventricular
papillary muscle mass from one or more medical images. No current method
currently
exists to measure left ventricular papillary muscle mass.
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[1108]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze the
posterior mitral
leaflet from one or more medical images. For patients being considered for
surgical or
transcatheter mitral valve repair or replacement, no current method currently
exists to
measure posterior mitral leaflet dimensions.
[1109]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze pulmonary
veins from
one or more medical images. Measures of pulmonary vein dimensions can be of
critical
importance in patients with atrial fibrillation, heart failure and mitral
regurgitation.
111101
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze pulmonary
arteries
from one or more medical images. Measures of pulmonary artery dimensions can
be of
critical importance in patients with pulmonary hypertension, heart failure and
pulmonary
emboli.
[1111]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze the right
atrial free
wall mass from one or more medical images. No current method exists to
accurately
measure right atrial free wall mass, which may be important in patients with
atrial
fibrillation.
[1112]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze the right
ventricular
mass from one or more medical images. Methods of measuring right ventricular
hypertrophy as an adverse consequence of pulmonary hypertension and/or heart
failure do
not currently exist.
[1113]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze the
proximal
ascending aorta from one or more medical images. Aortic aneurysms can require
highly
precise measurements of the aorta, which are more accurate by 3D techniques
such as CT
and MRI. At present, current algorithms do not allow for highly accurate
automated
measurements.
[1114]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze the right
atrial volume
from one or more medical images. Determination of right atrial volume can
improve
diagnosis and risk stratification in patients with and at risk of atrial
fibrillation.
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[1115]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze the right
ventricular
papillary muscle mass from one or more medical images. No current method
currently
exists to measure right ventricular papillary muscle mass.
[1116]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze the right
ventricular
volume from one or more medical images. Right ventricular volume measurements
can
enable determination of individuals with heart failure or at risk of heart
failure.
[1117]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, and/or analyze the
superior vena cava
from one or more medical images. No reliable method exists to date to measure
superior
vena cava dimensions, which may be important in patients with tricuspid valve
insufficiency and heart failure.
[1118]
In some embodiments, the system can be configured to utilize one or
more machine learning algorithms to identify, derive, analyze, segment, and/or
quantify
one or more cardiac structures from one or more medical images, such as the
left and right
ventricular volume (LVV, RVV), left and right atrial volume (LAV, RAY), and/or
left
ventricular myocardial mass (L V M).
[1119]
Further, in some embodiments, the system can be configured to utilize
one or more machine learning algorithms to identify, derive, analyze, segment,
and/or
quantify one or more cardiac structures from one or more medical images, such
as the
proximal ascending and descending aorta (PAA, DA), superior and inferior vena
cava
(SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall
(RVW),
and left atrial wall (LAW).
[1120]
In addition, in some embodiments, the system can be configured to
utilize one or more machine learning algorithms to identify, derive, analyze,
segment,
and/or quantify one or more cardiac structures from one or more medical
images, such as
the left atrial appendage, left atrial wall, coronary sinus, descending aorta,
superior vena
cava, inferior vena cava, pulmonary artery, right ventricular wall, sinuses of
Valsalva, left
ventricular volume, left ventricular wall, right ventricular volume, left
atrial volume, right
atrial volume, and/or proximal ascending aorta.
[1121]
Figure 20E is a flowchart illustrating an overview of an example
embodiment(s) of a method for generating a global ischemia index for a subject
and using
the same to assist assessment of risk of ischemia for the subject. As
illustrated in Figure
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20E, in some embodiments, the system can be configured to access one or more
medical
images of a subject at block 202, in any manner and/or in connection with any
feature
described above in relation to block 202. In some embodiments, the system is
configured
to identify one or more vessels, plaque, and/or fat in the one or more medical
images at
block 2002. For example, in some embodiments, the system can be configured to
use one
or more Al and/or ML algorithms and/or other image processing techniques to
identify one
or more vessels, plaque, and/or fat.
[1122]
In some embodiments, the system at block 2004 is configured to analyze
and/or access one or more contributors to ischemia of the subject, including
any
contributors to ischemia described herein, for example based on the accessed
one or more
medical images and/or other medical data. In some embodiments, the system at
block 2006
is configured to analyze and/or access one or more consequences of ischemia of
the subject,
including any consequences of ischemia described herein, including early
and/or late
consequences, for example based on the accessed one or more medical images
and/or other
medical data. In some embodiments, the system at block 2008 is configured to
analyze
and/or access one or more associated factors to ischemia of the subject,
including any
associated factors to ischemia described herein, for example based on the
accessed one or
more medical images and/or other medical data. In some embodiments, the system
at block
2010 is configured to analyze and/or access one or more results from other
testing, such as
for example invasive testing, non-invasive testing, image-based testing, non-
image based
testing, and/or the like.
[1123]
In some embodiments, the system at block 2012 can be configured to
generate a global ischemia index based on one or more parameters, such as for
example
one or more contributors to ischemia, one or more consequences of ischemia,
one or more
associated factors to ischemia, one or more other testing results, and/or the
like. In some
embodiments, the system is configured to generate a global ischemia index for
the subject
by generating a weighted measure of one or more parameters. For example, in
some
embodiments, the system is configured to weight one or more parameters
differently and/or
equally. In some embodiments, the system can be configured weight one or more
parameters logarithmically, algebraically, and/or utilizing another
mathematical transform.
In some embodiments, the system is configured to generate a weighted measure
using only
some or all of the parameters.
[1124]
In some embodiments, at block 2014, the system is configured to verify
the generated global ischemia index. For example, in some embodiments, the
system is
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configured to verify the generated global ischemia index by comparison to one
or more
blood flow parameters such as those discussed herein. In some embodiments, at
block
2016, the system is configured to generate user assistance to help a user
determine an
assessment of risk of ischemia for the subject based on the generated global
ischemia index,
for example graphically through a user interface and/or otherwise.
CAD Score(s)
[1125]
Some embodiments of the systems, devices, and methods described
herein are configured to generate one or more coronary artery disease (CAD)
scores
representative of a risk of CAD for a particular subject. In some embodiments,
the risk
score can be generated by analyzing and/or combining one or more aspects or
characteristics relating to plaque and/or cardiovascular features, such as for
example plaque
volume, plaque composition, vascular remodeling, high-risk plaque, lumen
volume, plaque
location (proximal v. middle v . distal), plaque location (myocardial V.
pericardial facing),
plaque location (at bifurcation or trifurcation v. not at bifurcation or
trifurcation), plaque
location (in main vessel v. branch vessel), stenosis severity, percentage
coronary blood
volume, percentage fractional myocardial mass, percentile for age and/or
gender, constant
or other correction factor to allow for control of within-person, within-
vessel, inter-plaque,
plaque-myocardial relationships, and/or the like. In some embodiments, a CAD
risk
score(s) can be generated based on automatic and/or dynamic analysis of one or
more
medical images, such as for example a CT scan or an image obtained from any
other
modality mentioned herein. In some embodiments, data obtained from analyzing
one or
more medical images of a patient can be normalized in generating a CAD risk
score(s) for
that patient. In some embodiments, the systems, devices, and methods described
herein
can be configured to generate a CAD risk score(s) for different vessels,
vascular territories,
and/or patients. In some embodiments, the systems, devices, and methods
described herein
can be configured to generate a graphical visualization of risk of CAD of a
patient based
on a vessel basis, vascular territory basis, and/or patient basis. In some
embodiments, based
on the generated CAD risk score(s), the systems, methods, and devices
described herein
can be configured to generate one or more recommended treatments for a
patient. In some
embodiments, the system can be configured to utilize a normalization device,
such as those
described herein, to account for differences in scan results (such as for
example density
values, etc.) between different scanners, scan parameters, and/or the like.
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[1126]
In some embodiments, the systems, devices, and methods described
herein can be configured to assess patients with suspected coronary artery
disease (CAD)
by use of one or more of a myriad of different diagnostic and prognostic
tools. In particular,
in some embodiments, the systems, devices, and methods described herein can be
configured to use a risk score for cardiovascular care for patients without
known CAD.
[1127]
As a non-limiting example, in some embodiments, the system can be
configured to generate an Atherosclerotic Cardiovascular Disease (ASCVD) risk
score,
which can be based upon a combination of age, gender, race, blood pressure,
cholesterol
(total, HDL and LDL), diabetes status, tobacco use, hypertension, and/or
medical therapy
(such as for example, statin and aspirin).
[1128]
As another non-limiting example, in some embodiments, the system can
be configured to generate a Coronary Artery Calcium Score (CACS), which can be
based
upon a non-contrast CT scan wherein coronary arteries are visualized for the
presence of
calcified plaque. In some embodiments, an Agatston (e.g., a measure of calcium
in a
coronary CT scan) score may be used to determine the CACS. In particular, in
some
embodiments, a CACS score can be calculated by: Agatston score = surface area
x
Hounsfield unit density (with brighter plaques with higher density receiving a
higher score).
However, in some embodiments, there may be certain limitations with a CACS
score. For
example, in some embodiments, because surface area to volume ratio decreases
as a
function of the overall volume, more spherical plaques can be incorrectly
weighted as less
contributory to the Agatston score in addition, in some embodiments, because
Hounsfield
unit density is inversely proportional to risk of major adverse cardiac events
(MACE),
weighting the HU density higher can score a lower risk plaque as having a
higher score.
Moreover, in some embodiments, 2.5-3mm thick CT "slices" can miss smaller
calcified
plaques, and/or no use of beta blocker results in significant motion artifact,
which can
increase the calcium score due to artifact.
[1129]
In some embodiments, for symptomatic patients undergoing coronary
CT angiography, the system can be configured to generate and/or utilize one or
more
additional risk scores, such as a Segment Stenosis Score, Segment Involvement
Score,
Segments-at-Risk Score, Duke Prognostic Index, CTA Score, and/or the like.
More
specifically, in some embodiments, a Segment Stenosis Score weights specific
stenoses
(0=0%, 1=1-24%, 2=25-49%, 3=50-69%, 4=>70%) across the entire 18 coronary
segment,
resulting in a total possible score of 72. In some embodiments, a Segment
Involvement
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Score counts the number of plaques located in the 18 segments and has a total
possible
score of 18.
[1130]
In some embodiments, a Segments-at-Risk Score reflects the potential
susceptibility of all distal coronary segments subtended by severe proximal
plaque. Thus,
in some embodiments, all segments subtended by severe proximal plaque can be
scored as
severe as well, then summated over 18 segments to create a segment-at-risk
score. For
example, if the proximal portion of the LCx is considered severely
obstructive, the
segments-at-risk score for the LCx can be proximal circumflex (=3) + mid
circumflex (=3)
+ distal circumflex (=3) + proximal obtuse marginal (=3) + mid obtuse marginal
(=3) +
distal obtuse marginal (=3), for a total circumflex segments-at-risk score of
18. In this
individual, if the LAD exhibits mild plaque in the proximal portion (=1) and
moderate
plaque in the midportion (=2), the LAD segments-at-risk score can be 3. If the
RCA
exhibits moderate plaque in the proximal portion (=3), the RCA segments-at-
risk score can
be 2. Thus, for this individual, the total segments-at-risk score can be 23
out of a possible
48.
[1131]
In some embodiments, a Duke Prognostic Index can be a reflection of
the coronary' artery plaque severity considering plaque location. In some
embodiments, a
modified Duke CAD index can consider overall plaque extent relating it to
coexistent
plaque in the left main or proximal LAD. In some embodiments, using this
scoring system,
individuals can be categorized into six distinct groups: no evident coronary
artery' plaque;
>2 mild plaques with proximal plaque in any artery or 1 moderate plaque in any
artery; 2
moderate plaques or 1 severe plaque in any artery; 3 moderate coronary artery
plaques or
2 severe coronary artery plaques or isolated severe plaque in the proximal
LAD; 3 severe
coronary artery plaques or 2 severe coronary artery plaques with proximal LAD
plaque,
moderate or severe left main plaque.
[1132]
In some embodiments, a CT angiography (CTA) Score can be calculated
by determining CAD in each segment, such as for example proximal RCA, mid RCA,
distal
RCA, R-PDA, R-PLB, left main, proximal LAD, mid LAD, distal LAD, D1, D2,
proximal
LCX, distal LCX, IM/AL, OM, L-PL, L-PDA, and/or the like. In particular, for
each
segment, when plaque is absent, the system can be configured to assign a score
of 0, and
when plaque is present, the system can be configured to assign a score of 1.1,
1.2 or 1.3
according to plaque composition (such as calcified, non-calcified and mixed
plaque,
respectively). In some embodiments, these scores can be multiplied by a weight
factor for
the location of the segment in the coronary artery tree (for example, 0.5 ¨ 6
according to
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vessel, proximal location and system dominance). In some embodiments, these
scores can
also be multiplied by a weight factor for stenosis severity (for example, 1.4
for >50%
stenosis and 1.0 for stenosis <50%). In some embodiments, the final score can
be
calculated by addition of the individual segment scores.
[1133]
In some embodiments, the systems, devices, and methods described
herein can be configured to utilize and/or perform improved quantification
and/or
characterization of many parameters on CT angiography that were previously
very difficult
to measure. For example, in some embodiments, the system can be configured to
determine
stenosis severity leveraging a proximal/distal reference and report on a
continuous scale,
for example from 0-100%, by diameter, area, and/or volumetric stenosis. In
some
embodiments, the system can be configured to determine total atheroma burden,
reported
in volumes or as a percent of the overall vessel volume (PAV), including for
example non-
calcified plaque volume (for example, as a continuous variable, ordinal
variable or single
variable), calcified plaque volume (for example, as a continuous variable,
ordinal variable
or single variable), and/or mixed plaque volume (for example, as a continuous
variable,
ordinal variable or single variable).
[1134]
In some embodiments, the system can be configured to determine low
attenuation plaque, for example reported either as yes/no binary or continuous
variable
based upon HU density. In some embodiments, the system can be configured to
determine
vascular remodeling, for example reported as ordinal negative, intermediate or
positive (for
example, <0_90, 0.90-1.10, or >1.0) or continuous in some embodiments, the
system can
be configured to determine and/or analyze various locations of plaque, such as
for example
proximal/mid/distal, myocardial facing vs. pericardial facing, at bifurcation
v. not at
bifurcation, in main vessel vs. branch vessel, and/or the like.
[1135]
In some embodiments, the system can be configured to determine
percentage coronary blood volume, which can report out the volume of the lumen
(and
downstream subtended vessels in some embodiments) as a function of the entire
coronary
vessel volume (for example, either measured or calculated as hypothetically
normal). In
some embodiments, the system can be configured to determine percentage
fractional
myocardial mass, which can relate the coronary lumen or vessel volume to the
percentage
downstream subtended myocardial mass.
[1136]
In some embodiments, the system can be configured to determine the
relationship of all or some of the above to each other, for example on a
plaque-plaque basis
to influence vessel behavior/risk or on a vessel-vessel basis to influence
patient
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behavior/risk. In some embodiments, the system can be configured to utilize
one or more
comparisons of the same, for example to normal age- and/or gender-based
reference values.
[1137]
In some embodiments, one or more of the metrics described herein can
be calculated on a per-segment basis. In some embodiments, one or more of the
metrics
calculated on a per-segment basis can then summed across a vessel, vascular
territory,
and/or patient level. In some embodiments, the system can be configured to
visualize one
or more of such metrics, whether on a per-segment basis and/or on a vessel,
vascular
territory, and/or patient basis, on a geographical scale. For example, in some
embodiments,
the system can be configured to visualize one or more such metrics on a
graphical scale
using 3D and/or 4D histograms.
[1138]
Further, in some embodiments, cardiac CT angiography enables
quantitative assessment of a myriad of cardiovascular structures beyond the
coronary
arteries, which may both contribute to coronary artery disease as well as
other
cardiovascular diseases. For example, these measurements can include those of
one or
more of: (1) left ventricle ¨ e.g., left ventricular mass, left ventricular
volume, left ventricle
Hounsfield unit density as a surrogate marker of ventricular perfusion; (2)
right ventricle ¨
e.g., right ventricular mass, right ventricular volume; (3) left atrium ¨
e.g., volume, size,
geometry; (4) right atrium ¨ e.g., volume, size, geometry; (5) left atrial
appendage ¨ e.g.,
morphology (e.g., chicken wing, windsock, etc.), volume, angle, etc.; (6)
pulmonary vein
¨ e.g., size, shape, angle of takeoff from the left atrium. etc.; (7) mitral
valve ¨ e.g., volume,
thickness, shape, length, calcification, anatomic orifice area, etc.; (8)
aortic valve - e.g.,
volume, thickness, shape, length, calcification, anatomic orifice area, etc.;
(9) tricuspid
valve ¨ e.g., volume, thickness, shape, length, calcification, anatomic
orifice area, etc.; (10)
pulmonic valve - e.g.; volume, thickness, shape, length, calcification,
anatomic orifice area,
etc.; (11) pericardial and pericoronary fat ¨ e.g., volume, attenuation, etc.;
(12) epicardial
fat¨ e.g., volume, attenuation, etc.; (13) pericardium ¨ e.g., thickness,
mass, volume; and/or
(14) aorta ¨ e.g., dimensions, calcifications, atheroma.
[1139]
Given the multitude of measurements that can help characterize
cardiovascular risk, certain existing scores can be limited in their holistic
assessment of the
patient and may not account for many key parameters that may influence patient
outcome.
For example, certain existing scores may not take into account the entirety of
data that is
needed to effectively prognosticate risk. In addition, the data that will
precisely predict risk
can be multi-dimensional, and certain scores do not consider the relationship
of plaques to
one another, or vessel to one another, or plaques-vessels-myocardium
relationships or all
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of those relationships to the patient-level risk. Also, in certain existing
scores, the data may
categorize plaques, vessels and patients, thus losing the granularity of pixel-
wise data that
are summarized in these scores. In addition, in certain existing scores, the
data may not
reflect the normal age- and gender-based reference values as a benchmark for
determining
risk. Moreover, certain scores may not consider a number of additional items
that can be
gleaned from quantitative assessment of coronary artery disease, vascular
morphology
and/or downstream ventricular mass. Further, within-person relationships of
plaques,
segments, vessels, vascular territories may not considered within certain risk
scores.
Furthermore, no risk score to date that utilizes imaging normalizes these
risks to a standard
that accounts for differences in scanner make/model, contrast type, contrast
injection rate,
heart rate / cardiac output, patient characteristics, contrast-to-noise ratio,
signal-to-noise
ratio, and/or image acquisition parameters (for example, single vs. dual vs.
spectral energy
imaging; retrospective helical vs. prospective axial vs. fast-pitch helical;
whole-heart
imaging versus non-whole-heart [i.e., non-volumetric] imaging; etc.).
In some
embodiments described herein, the systems, methods, and devices overcome such
technical
shortcomings.
[1140]
In particular, in some embodiments, the systems, devices, and methods
described herein can be configured to generate and/or a novel CAD risk score
that addresses
the aforementioned limitations by considering one or more of: (1) total
atheroma burden,
normalized for density, such as absolute density or Hounsfield unit (HU)
density (e.g., can
be categorized as total volume or relative volume, i.e., plaque volume /
vessel volume x
100%); (2) plaque composition by density or HU density (e.g., can be
categorized
continuously, ordinally or binarily); (3) low attenuation plaque (e.g., can be
reported as
yes/no binary or continuous variable based upon density or HU density); (4)
vascular
remodeling (e.g., can be reported as ordinal negative, intermediate or
positive (<0.90, 0.90-
1.10, or >1.0) or continuous); (5) plaque location ¨ proximal v. mid v.
distal; (6) plaque
location ¨ which vessel or vascular territory; (7) plaque location ¨
myocardial facing v.
pericardial facing; (8) plaque location ¨ at bifurcation v. not at
bifurcation; (9) plaque
location ¨ in main vessel v. branch vessel; (10) stenosis severity; (11)
percentage coronary
blood volume (e.g., this metric can report out the volume of the lumen (and
downstream
subtended vessels) as a function of the entire coronary vessel volume (e.g.,
either measured
or calculated as hypothetically normal)); (12) percentage fractional
myocardial mass (e.g.,
this metric can relate the coronary lumen or vessel volume to the percentage
downstream
subtended myocardial mass); (13) consideration of normal age- and/or gender-
based
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reference values; and/or (14) statistical relationships of all or some of the
above to each
other (e.g., on a plaque-plaque basis to influence vessel behavior/risk or on
a vessel-vessel
basis to influence patient behavior/risk).
[1141]
In some embodiments, the system can be configured to determine a
baseline clinical assessment(s), including for such factors as one or more of:
(1) age; (2)
gender; (3) diabetes (e.g., presence, duration, insulin-dependence, history of
diabetic
ketoacidosis, end-organ complications, which medications, how many
medications, and/or
the like); (4) hypertension (e.g., presence, duration, severity, end-organ
damage, left
ventricular hypertrophy, number of medications, which medications, history of
hypertensive urgency or emergency, and/or the like); (5) dyslipidemia (e.g.,
including low-
density lipoprotein (LDL), triglycerides, total cholesterol, lipoprotein(a)
Lp(a),
apolipoprotein B (ApoB), and/or the like); (6) tobacco use (e.g., including
what type, for
what duration, how much use, and/or the like); (7) family history (e.g.,
including which
relative, at what age, what type of event, and/or the like); (8) peripheral
arterial disease
(e.g., including what type, duration, severity, end-organ damage, and/or the
like); (9)
cerebrovascular disease (e.g., including what type, duration, severity, end-
organ damage,
and/or the like); (10) obesity (e.g., including how obese, how long, is it
associated with
other metabolic derangements, such as hypertriglyceridemia, centripetal
obesity, diabetes,
and/or the like); (11) physical activity (e.g., including what type,
frequency, duration,
exertional level, and/or the like); and/or (12) psychosocial state (e.g.,
including depression,
anxiety, stress, sleep, and/or the like).
[1142]
In some embodiments, a CAD risk score is calculated for each segment,
such as for example for segment 1, segment 2, or for some or all segments. In
some
embodiments, the score is calculated by combining (e.g., by multiplying or
applying any
other mathematical transform or generating a weighted measure of) one or more
of: (1)
plaque volume (e.g., absolute volume such as in mm3 or PAV; may be weighted);
(2)
plaque composition (e.g., NCP/CP, Ordinal NCP/Ordinal CP; Continuous; may be
weighted); (3) vascular remodeling (e.g., Positive/Intermediate/Negative;
Continuous; may
be weighted); (4) high-risk plaques (e.g., positive remodeling + low
attenuation plaque;
may be weighted); (5) lumen volume (e.g., may be absolute volume such as in
mm3 or
relative to vessel volume or relative to hypothetical vessel volume; may be
weighted); (6)
location ¨ proximal / mid / distal (may be weighted); (7) location ¨
myocardial vs.
pericardial facing (may be weighted): (8) location ¨ at bifurcation /
trifurcation vs. not at
bifurcation / trifurcation (may be weighted); (9) location ¨ in main vessel
vs. branch vessel
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(may be weighted); (10) stenosis severity (e.g., ><70%, <>50%, 1-24, 25-49, 50-
69, >70%;
0, 1-49, 50-69, >70%; continuous; may use diameter, area or volume; may be
weighted);
(11) percentage Coronary Blood Volume (may be weighted); (12) percentage
fractional
myocardial mass (e.g., may include total vessel volume-to-LV mass ratio; lumen
volume-
to-LV mass ratio; may be weighted); (13) percentile for age- and gender; (14)
constant /
correction factor (e.g., to allow for control of within-person, within-vessel,
inter-plaque,
and/or plaque-myocardial relationships). As a non-limiting example, if Segment
1 has no
plaque, then it can be weighted as 0 in some embodiments.
[1143]
In some embodiments, to determine risk (which can be defined as risk
of future myocardial infarction, major adverse cardiac events, ischemia, rapid
progression,
insufficient control on medical therapy, progression to angina, and/or
progression to need
of target vessel revascularization), all or some of the segments are added up
on a per-vessel,
per-vascular territory and per-patient basis. In some embodiments, by using
plots, the
system can be configured to visualize and/or quantify risk based on a vessel
basis, vascular
territory basis, and patient-basis.
[1144]
In some embodiments, the score can be normalized in a patient- and
scan-specific manner by considering items such as for example: (1) patient
body mass
index; (2) patient thorax density; (3) scanner make/model; (4) contrast
density along the Z-
axis and along vessels and/or cardiovascular structures; (5) contrast-to-noise
ratio; (6)
signal-to-noise ratio; (7) method of ECG gating (e.g., retrospective helical,
prospective
axial, fast-pitch helical); (8) energy acquisition (e.g., single, dual,
spectral, photon
counting); (9) heart rate; (10) use of pre-CT medications that may influence
cardiovascular
structures (e.g., nitrates, beta blockers, anxiolytics); (11) mA; and/or (12)
kvp.
111451
In some embodiments, without normalization, cardiovascular structures
(coronary arteries and beyond) may have markedly different Hounsfield units
for the same
structure (e.g., if 100 vs. 120 kvp is used, a single coronary plaque may
exhibit very
different Hounsfield units). Thus, in some embodiments, this "normalization"
step is
needed, and can be performed based upon a database of previously acquired
images and/or
can be performed prospectively using an external normalization device, such as
those
described herein.
[1146]
In some embodiments, the CAD risk score can be communicated in
several ways by the system to a user. For example, in some embodiments, a
generated
CAD risk score can be normalized to a scale, such as a 100 point scale in
which 90-100 can
refer to excellent prognosis, 80-90 for good prognosis, 70-80 for satisfactory
prognosis, 60-
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70 for below average prognosis, <60 for poor prognosis, and/or the like. In
some
embodiments, the system can be configured to generate and/or report to a user
based on the
CAD risk score(s) vascular age vs. biological age of the subject. In some
embodiments,
the system can be configured to characterize risk of CAD of a subject as one
or more of
normal, mild, moderate, and/or severe. In some embodiments, the system can be
configured to generate one or more color heat maps based on a generated CAD
risk score,
such as red, yellow, green, for example in ordinal or continuous display. In
some
embodiments, the system can be configured to characterize risk of CAD for a
subject as
high risk vs. non-high-risk, and/or the like.
111471
As a non-limiting example, in some embodiments, the generated CAD
risk score for Lesion 1 can be calculated as Vol X Composition (HU) X RI X HRP
X Lumen
Volume X Location x Stenosis% X %CBV X %FMM X Age-/Gender Normal Value % X
Correction Constant) X Correction factor for scan- and patient-specific
parameters X
Normalization factor to communicate severity of findings.
Similarly, in some
embodiments, the generated CAD risk score for Lesion 2 can be calculated as
Vol X
Composition (HU) X RI X HRP X Lumen Volume X Location x Stenosis% X %CBV X
%FMM X Age-/Gender Normal Value % X Correction Constant) X Correction factor
for
scan- and patient-specific parameters X Normalization factor to communicate
severity of
findings. In some embodiments, the generated CAD risk score for Lesion 3 can
be
calculated as Vol X Composition (HU) X RI X HRP X Lumen Volume X Location x
Stenosis% X %CBV X %FMM X Age-/Gender Normal Value % X Correction Constant)
X Correction factor for scan- and patient-specific parameters X Normalization
factor to
communicate severity of findings. In some embodiments, the generated CAD risk
score
for Lesion 4 can be calculated as Vol X Composition (HU) X RI X HRP X Lumen
Volume
X Location x Stenosis% X %CBV X %FMM X Age-/Gender Normal Value % X
Correction Constant) X Correction factor for scan- and patient-specific
parameters X
Normalization factor to communicate severity of findings. In some embodiments,
a CAD
risk score can similarly be generated for any other lesions.
[1148]
In some embodiments, the CAD risk score can be adapted to other
disease states within the cardiovascular system, including for example: (1)
coronary artery
disease and its downstream risk (e.g., myocardial infarction, acute coronary
syndromes,
ischemia, rapid progression, progression despite medical therapy, progression
to angina,
progression to need for target vessel revascularization, and/or the like); (2)
heart failure;
(3) atrial fibrillation; (4) left ventricular hypertrophy and hypertension;
(5) aortic aneurysm
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and/or dissection; (6) valvular regurgitation or stenosis; (7) sudden coronary
artery
dissection, and/or the like.
[1149]
Figure 21 is a flowchart illustrating an overview of an example
embodiment(s) of a method for generating a coronary artery disease (CAD)
Score(s) for a
subject and using the same to assist assessment of risk of CAD for the
subject. As
illustrated in Figure 21, in some embodiments, the system is configured to
conduct a
baseline clinical assessment of a subject at block 2102. In particular, in
some embodiments,
the system can be configured to take into account one or more clinical
assessment factors
associated with the subject, such as for example age, gender, diabetes,
hypertension,
dyslipidemia, tobacco use, family history, peripheral arterial disease,
cerebrovascular
disease, obesity, physical activity, psychosocial state, and/or any details of
the foregoing
described herein. In some embodiments, one or more baseline clinical
assessment factors
can be accessed by the system from a database and/or derived from non-image-
based and/or
image-based data.
[1150]
In some embodiments, at block 202, the system can be configured to
access one or more medical images of the subject at block 202, in any manner
and/or in
connection with any feature described above in relation to block 202. In some
embodiments, the system is configured to identify one or more segments,
vessels, plaque,
and/or fat in the one or more medical images at block 2104. For example, in
some
embodiments, the system can be configured to use one or more Al and/or ML
algorithms
and/or other image processing techniques to identify one or more segments,
vessels, plaque,
and/or fat.
[1151]
In some embodiments, the system at block 2106 is configured to analyze
and/or access one or more plaque parameters. For example, in some embodiments,
one or
more plaque parameters can include plaque volume, plaque composition, plaque
attenuation, plaque location, and/or the like. In particular, in some
embodiments, plaque
volume can be based on absolute volume and/or PAV. In some embodiments, plaque
composition can be determined by the system based on density of one or more
regions of
plaque in a medical image, such as absolute density and/or Hounsfield unit
density. In
some embodiments, the system can be configured to categorize plaque
composition
binarily, for example as calcified or non-calcified plaque, and/or
continuously based on
calcification levels of plaque. In some embodiments, plaque attenuation can
similarly be
categorized binarily by the system, for example as high attenuation or low
attenuation based
on density, or continuously based on attenuation levels of plaque. In some
embodiments,
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plaque location can be categorized by the system as one or more of proximal,
mid, or distal
along a coronary artery vessel. In some embodiments, the system can analyze
plaque
location based on the vessel in which the plaque is located. In some
embodiments, the
system can be configured to categorize plaque location based on whether it is
myocardial
facing, pericardial facing, located at a bifurcation, located at a
trifurcation, not located at a
bifurcation, and/or not located at a trifurcation. In some embodiments, the
system can be
configured to analyze plaque location based on whether it is in a main vessel
or in a branch
vessel.
[1152]
In some embodiments, the system at block 2108 is configured to analyze
and/or access one or more vessel parameters, such as for example stenosis
severity, lumen
volume, percentage of coronary blood volume, percentage of fractional
myocardial mass,
and/or the like. In some embodiments, the system is configured to categorize
or determine
stenosis severity based on one or more predetermined ranges of percentage
stenosis, for
example based on diameter, area, and/or volume. In some embodiments, the
system is
configured to determine lumen volume based on absolute volume, volume relative
to a
vessel volume, volume relative to a hypothetical volume, and/or the like. In
some
embodiments, the system is configured to determine percentage of coronary
blood volume
based on determining a volume of lumen as a function of an entire coronary
vessel volume.
In some embodiments, the system is configured to determine percentage of
fractional
myocardial mass as a ratio of total vessel volume to left ventricular mass, a
ratio of lumen
volume to left ventricular mass, and/or the like.
[1153]
In some embodiments, the system at block 2110 is configured to analyze
and/or access one or more clinical parameters, such as for example percentile
condition for
age, percentile condition for gender of the subject, and/or any other clinical
parameter
described herein.
[1154]
In some embodiments, the system at block 2112 is configured to
generate a weighted measure of one or more parameters, such as for example one
or more
plaque parameters, one or more vessel parameters, and/or one or more clinical
parameters.
In some embodiments, the system is configured to generate a weighted measure
of one or
more parameters for each segment. In some embodiments, the system can be
configured
to generate the weighted measure logarithmically, algebraically, and/or
utilizing another
mathematical transform. In some embodiments, the system can be configured to
generate
the weighted measure by applying a correction factor or constant, for example
to allow for
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control of within-person, within-vessel, inter-plaque, and/or plaque-
myocardial
relationships.
[1155]
In some embodiments, the system at block 2114 is configured to
generate one or more CAD risk scores for the subject. For example, in some
embodiments,
the system can be configured to generate a CAD risk score on a per-vessel, per-
vascular
territory, and/or per-subject basis. In some embodiments, the system is
configured to
generate one or more CAD risk scores of the subject by combining the generated
weighted
measure of one or more parameters.
[1156]
In some embodiments, the system at block 2116 can be configured to
normalize the generated one or more CAD scores. For example, in some
embodiments, the
system can be configured to normalize the generated one or more CAD scores to
account
for differences due to the subject, scanner, and/or scan parameters, including
those
described herein.
[1157]
In some embodiments, the system at block 2118 can be configured to
generate a graphical plot of the generated one or more per-vessel, per-
vascular territory, or
per-subject CAD risk scores for visualizing and quantifying risk of CAD for
the subject.
For example, in some embodiments, the system can be configured to generate a
graphical
plot of one or more CAD risk scores on a per-vessel, per-vascular, and/or per-
subject basis.
In some embodiments, the graphical plot can include a 2D, 3D, or 4D
representation, such
as for example a histogram.
[1158]
In some embodiments, the system at block 2120 can be configured to
assist a user to generate an assessment of risk of CAD for the subject based
the analysis.
For example, in some embodiments, the system can be configured to generate a
scaled CAD
risk score for the subject. In some embodiments, the system can be configured
to determine
a vascular age for the subject. In some embodiments, the system can be
configured to
categorize risk of CAD for the subject, for example as normal, mild, moderate,
or severe.
In some embodiments, the system can be configured to generate one or more
colored heart
maps. In some embodiments, the system can be configured to categorize risk of
CAD for
the subject as high risk or low risk.
Treat to the Image
[1159]
Some embodiments of the systems, devices, and methods described
herein are configured to track progression of a disease, such as a coronary
artery disease
(CAD), based on image analysis and use the results of such tracking to
determine treatment
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for a patient. In other words, in some embodiments, the systems, methods, and
devices
described herein are configured to treat a patient or subject to the image. In
particular, in
some embodiments, the system can be configured to track progression of a
disease in
response to a medical treatment by analyzing one or more medical images over
time and
use the same to determine whether the medical treatment is effective or not.
For example,
in some embodiments, if the prior medical treatment is determined to be
effectiveness based
on tracking of disease progression based on image analysis, the system can be
configured
to propose continued use of the same treatment. On the other hand, in some
embodiments,
if the prior medical treatment is determined to be neutral or non-effective
based on tracking
of disease progression based on image analysis, the system can be configured
to propose a
modification of the prior treatment and/or a new treatment for the subject. In
some
embodiments, the treatment can include medication, lifestyle changes or
actions, and/or
revascularizati on procedures.
[1160]
In particular, some embodiments of the systems, devices, and methods
described herein are configured to determine one or more of the progression,
regression or
stabilization, and/or destabilization of coronary artery disease or other
vascular disease over
time in a manner that will reduce adverse coronary events. For example, in
some
embodiments, the systems, devices, and methods described herein are configured
to
provide medical analysis and/or treatment based on plaque attenuation
tracking. In some
embodiments, the systems, devices, and methods described herein can be
configured to
utilize a computer system and/or an artificial intelligence platform to track
the attenuation
of plaque, wherein an automatically detected transformation from low
attenuation plaque
to high attenuation plaque on a medical image, rather than regression of
plaque, can be used
as the main basis for generating a plaque attenuation score or status, which
can be
representative of the rate of progression and/or rate of increased/decreased
risk of coronary
disease. As such, in some embodiments, the systems, devices, and methods
described
herein can be configured to provide response assessment of medical therapy,
lifestyle
interventions, and/or coronary revascularization along the life course of an
individual.
[1161]
In some embodiments, the system can be configured to utilize computed
tomography angiography (CCTA).
Generally speaking, computed tomography
angiography (CCTA) can enable evaluation of presence, extent, severity,
location and/or
type of atherosclerosis in the coronary and other arteries. These factors can
change with
medical therapy and lifestyle modifications and coronary interventions. As a
non-limiting
example, in some cases, Omega-3 fatty acids, after 38.6 months can lower high-
risk plaque
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prevalence, number of high-risk plaques, and/or napkin-ring sign. Also, the CT
density of
plaque can be higher in omega-3 fatty acids group. As another non-limiting
example, in
some cases, icosapent ethyl can result in reduced low attenuation plaque (LAP)
volume by
17% and overall plaque volume by 9% compared to baseline and placebo. In
addition, as
another non-limiting example, in some cases of HIV positive patients, higher
non-calcified
and high-risk plaque burden on anti-retroviral therapy can be higher and can
involve higher
cardiovascular risk. Further, as another non-limiting example, in some cases
of patients
taking statins, there can be slower rate of percent atheroma progression with
more rapid
progression of calcified percent atheroma volume. Other changes in plaque can
also occur
due to some other exposure. Importantly, in some instances, patients may often
be taking
combinations of these medications and/or living healthy or unhealthy
lifestyles that may
contribute multi-factorially to the changes in plaque over time in a manner
that is not
predictable, but can be measurable, for example utilizing one or more
embodiments
described herein.
[1162]
In some embodiments, the systems, methods, and devices described
herein can be configured to analyze dichotomous and/or categorical changes in
plaque (e.g.,
from non-calcified to calcified, high-risk to non-high-risk, and/or the like)
and burden of
plaque (e.g., volume, percent atheroma volume, and/or the like), as well as
analyze serial
continuous changes over time. In addition, in some embodiments, the systems,
methods,
and devices described herein can be configured to leverage the continuous
change of a
plaque's features as a longitudinal method for guiding need for
intensification of medical
therapy, change in lifestyle, and/or coronary revascularization. Further, in
some
embodiments, the systems, methods, and devices described herein can be
configured to
leverage the difference in these changes over time as a method to guide
therapy in a manner
that improves patient-specific event-free survival.
[1163]
As such, in some embodiments, the systems, methods, and devices
described herein can be configured to determine the progression, regression or
stabilization,
and/or destabilization of coronary artery disease and/or other vascular
disease over time,
for example in response to a medical treatment, in a manner that will reduce
adverse
coronary events. In particular, in some embodiments, the systems, methods, and
devices
described herein can be configured to analyze the density / signal intensity,
vascular
remodeling, location of plaques, plaque volume / disease burden, and/or the
like. In some
embodiments, the system can be configured to utilize a normalization device,
such as those
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described herein, to account for differences in scan results (such as for
example density
values, etc.) between different scanners, scan parameters, and/or the like.
[1164]
In some embodiments, the system can be configured to track imaging
density (CT) and/or signal intensity (MR1) of coronary atherosclerotic lesions
over time by
serial imaging. In some embodiments, the system can be configured to leverage
directionality changes in coronary lesions over time (e.g. lower-to-higher CT
density,
higher-to-even higher CT density, etc.) as measurements of stabilization of
plaque. In some
embodiments, the system can be configured to leverage directionality changes
to link to
risk of disease events (e.g., high CT density is associated with lower risk of
heart attack).
In some embodiments, the system can be configured to guide decision making as
to whether
to add another medication / intensity medical therapy. For example, if there
is no change
in density / signal intensity for a patient after 1 year, the system can be
configured to
propose addition of another medication. In some embodiments, the system can be
configured to guide decision making in the above manner in order to reduce
adverse
coronary events (e.g., acute coronary syndrome, rapid progression, ischemia,
and/or the
like).
[1165]
Figure 22A illustrates an example(s) of tracking the attenuation of
plaque for analysis and/or treatment of coronary artery and/or other vascular
disease. As a
non-limiting example, Figure 22A illustrates example cross sections of
arteries from a CT
image. In the illustrated example embodiment, the yellow circles are the
lumen, the orange
circles are the outer vessel wall and everything in between is plaque tissue
or similar. In
the illustrated example embodiment, the "high-risk plaques" by CT are
indicated to the left,
where they are classified as such by having low attenuation plaque (e.g., <30
Hounsfield
units) and positive (>1) vascular remodeling (e.g., cross-sectional area or
diameter at the
site of maximum plaque compared to cross-sectional area at the most proximal
normal
appearing cross-section). In some embodiments, positive arterial remodeling
can be
defined as >1.05 or >1.10.
[1166]
As illustrated in the example embodiment of Figure 22A, in some
embodiments, plaques can be of continuously different density. In the left
most cross-
section of the illustrated example embodiment, the plaque is black, and turns
progressively
gray and then lighter and then brighter until it becomes very bright white,
with a Hounsfield
unit density of >1000 in the right most cross-section of the illustrated
example embodiment.
In some embodiments, this density can be reported out continuously as
Hounsfield unit
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densities or other depending on the acquisition mode of the CT image, which
can include
single-energy, dual energy, spectral, and/or photon counting imaging.
[1167]
In some embodiments, using imaging methods (e.g., by CT), darker
plaques (e.g., with lower Hounsfield unit densities) can represent higher risk
(e.g., of
myocardial infarction, of causing ischemia, of progressing rapidly, and/or the
like), while
brighter plaques (e.g., with higher Hounsfield unit density) can represent
lower risk.
[1168]
In some embodiments, the system is configured to leverage the
continuous scale of the plaque composition density as a marker for increased
stabilization
of plaque after treatment, and to leverage this information to continually
update prognostic
risk stratification for future coronary events (e.g., acute coronary
syndromes, ischemia,
etc.). Thus, in some embodiments, an individual's risk of a heart attack can
be dependent
on the density of the plaque, and changes in the density after treatment can
attenuate that
risk, increase that risk, and/or have no effect on risk.
[1169]
In some embodiments, the system can be configured to generate and/or
suggest treatment in a number of different forms, which may include:
medications (e.g.,
statins, human immunodeficiency virus (HIV) medications, icosapent ethyl,
bempedoic
acid, rivaroxaban, aspirin, proprotein convertase subtilisin/kexin type 9
(PCSK-9)
inhibitors, inclisiran, sodium-glucose cotransporter-2 (SGLT-2) inhibitors,
glucagon-like
peptide-1 (GLP-1) receptor agonists, low-density lipoprotein (LDL) apheresis,
etc.);
lifestyle (increased exercise, aerobic exercise, anaerobic exercise, cessation
of smoking,
changes in diet, etc.); and/or revascularization (after bypass grafting,
stenting,
bioabsorbable scaffolds, etc.).
[1170]
In some embodiments, the system can be configured to generate and/or
provide a "treat to the image" continuous approach that offers clinicians and
patients a
method for following plaque changes over time to ensure that the plaque is
stabilizing and
the prognosis is improving. For example, in some embodiments, a patient may be
started
on a statin medication after their CT scan. Over time (e.g., months), a plaque
may change
in Hounsfield unit density from 30 to 45 HUs. In some embodiments, this may
represent a
beneficial outcome of plaque stabilization and connote the efficacy of the
statin
medications on the plaque. Alternatively, over time, a plaque may not change
in Hounsfield
unit density, staying at 30 HU over time. In this case, in some embodiments,
this may
represent an adverse outcome wherein the statin medication is ineffective in
stabilizing the
plaque. In some embodiments, should a plaque not stabilize to medical therapy
(e.g., HU
density remains low, or is very slow to rise), then another medication (e.g.,
PCSK-9
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inhibitor) may be added as the constancy in the HU ca be a titratable
biomarker that is used
to guide medical therapy intensification and, ultimately, improve patient
outcomes (e.g., by
reducing myocardial infarction, rapid progression, ischemia, and/or other
adverse event).
[1171]
In some embodiments, densities of plaques may be influenced by a
number of factors that can include one or more of: scanner type, image
acquisition
parameters (e.g., mA, kVp, etc.), energy (e.g., single-, dual-, spectral,
photon counting,
etc.), gating (e.g., axial vs. retrospective helical, etc.), contrast, age,
patient body habitus,
surrounding cardiac structures, plaque type (e.g., calcium may cause partial
volume artifact,
etc.), and/or others. As such, in some embodiments, the system can be
configured to
normalize one or more of these factors to further standardize comparisons in
plaque types
over time.
[1172]
In some embodiments, the system can be configured to track vascular
remodeling of coronary atherosclerotic lesions over time using image analysis
techniques.
In some embodiments, the system can be configured to leverage directionality
changes in
remodeling (e.g., outward, intermediate, inward, and/or the like). In some
embodiments,
the system can be configured to evaluate directionality on a patient, vessel,
segment, lesion
and/or cross section basis. In some embodiments, the system can be configured
to leverage
directionality changes to link to risk of disease events. For example, in some
embodiments,
more outward remodeling can be indicative of a higher risk of heart attack,
and/or the like.
In some embodiments, the system can be configured to guide decision making as
to whether
to add another medication / intensify medical therapy and/or perform coronary
revascularization based upon worsening or new positive remodeling.
In some
embodiments, the system can be configured to guide decision making in the
above manner
in order to reduce adverse coronary events (e.g., acute coronary syndrome,
rapid
progression, ischemia, and/or the like).
[1173]
In some embodiments, a similar analogy for plaque composition can be
applied to measures of vascular remodeling in a specific coronary lesion
and/or across all
coronary lesions within the coronary vascular tree. In particular, in some
embodiments,
the remodeling index can be a continuous measure and can be reported by one or
more of
diameter, area, and/or volume. As positive remodeling can be associated with
lesions at
the time of acute coronary syndrome and negative remodeling may not, in some
embodiments, serial imaging (e.g., CT scans, etc.) can be followed across time
to determine
whether the plaque is causing more or less positive remodeling. In some
embodiments,
cessation and/or slowing of positive remodeling can be favorable sign that can
be used to
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prognostically update an individual or a lesion's risk of myocardial
infarction or other
adverse coronary event (e.g., is chemia, etc.).
[1174]
In some embodiments, the system can be configured to provide a "treat
to the image" continuous approach that offers clinicians and patients a method
for following
plaque changes over time to ensure that the plaque is stabilizing and the
prognosis is
improving. For example, in some embodiments, a patient may be started on a
statin
medication after their CT scan. Over time (e.g., months, etc.), a plaque may
change in
remodeling index from 1.10 to 1.08. In some embodiments, this may represent a
beneficial
outcome of plaque stabilization and connote the efficacy of the statin
medications on the
plaque. Alternatively, over time, a plaque may not change in remodeling index
over time,
staying at 1.10. In this case, in some embodiments, this may represent an
adverse outcome
wherein the statin medication is ineffective in stabilizing the plaque. In
some embodiments,
should a plaque not stabilize to medical therapy (for example if the
remodeling index
remains high or is very slow to decrease), then another medication (e.g., PCSK-
9 inhibitor,
etc.) may be added, as the constancy in the remodeling can be a titratable
biomarker that is
used to guide medical therapy intensification and, ultimately, improve patient
outcomes
(e.g., by reducing myocardial infarction, rapid progression, ischemia, and/or
other adverse
event).
[1175]
In some embodiments, remodeling indices of plaques may be influenced
by a number of factors that can include one or more of: scanner type, image
acquisition
parameters (e.g., mA, kVp, etc.), energy (e.g., single-, dual-, spectral,
photon counting,
etc.), gating (e.g., axial vs. retrospective helical, etc.), contrast, age,
patient body habitus,
surrounding cardiac structures, plaque type (e.g., calcium may cause partial
volume artifact,
etc.), and/or the like. In some embodiments, the system can be configured to
normalize to
one or more of these factors to further standardize comparisons in plaque
types over time.
[1176]
In some embodiments, the system can be configured to track location of
one or more regions of plaque over time. For example, in some embodiments, the
system
can be configured to track the location of one or more regions of plaque based
on one or
more of: myocardial facing vs. pericardial facing; at a bifurcation or
trifurcation; proximal
vs. mid vs. distal; main vessel vs. branch vessel; and/or the like. In some
embodiments,
the system can be configured to evaluate directionality on a patient, vessel,
segment, lesion
and/or cross section basis. In some embodiments, the system can be configured
to leverage
directionality changes to link to risk of disease events (e.g. more outward
remodeling,
higher risk of heart attack, and/or the like). In some embodiments, the system
can be
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configured to guide decision making as to whether to add another medication /
intensify
medical therapy or perform coronary revascularization, and/or the like. In
some
embodiments, the system can be configured to guide decision making in the
above manner
in order to reduce adverse coronary events (e.g., acute coronary syndrome,
rapid
progression, ischemia, and/or the like).
[1177]
In some embodiments, the system can be configured to identify and/or
correlate certain coronary events as being associated with increased risk over
time. For
example, in some embodiments, pericardial facing plaque may have a higher rate
of being
a culprit lesion at the time of myocardial infarction than myocardial facing
plaques. In
some embodiments, bifurcation lesions can appear to have a higher rate of
being a culprit
lesion at the time of myocardial infarction than non-bifurcation/trifurcation
lesions. In
some embodiments, proximal lesions can tend to be more common than distal
lesions and
can also be most frequently the site of myocardial infarction or other adverse
coronary
event.
[1178]
In some embodiments, the system can be configured to track each or
some one of these individual locations of plaque and, based upon their
presence, extent and
severity, assign a baseline risk. In some embodiments, after treatment with
medication,
lifestyle or intervention, serial imaging (e.g., by CT, etc.) can be performed
to determine
changes in these features, which can be used to update risk assessment.
[1179]
In some embodiments, the system can be configured to provide a -treat
to the image" continuous approach that offers clinicians and patients a method
for following
plaque changes in location over time to ensure that the plaque is stabilizing
and the
prognosis is improving. For example, in some embodiments, a patient may be
started on a
statin medication after their CT scan. Over time (e.g., months, etc.), a
plaque may regress
in the pericardial-facing region but remain in the myocardial facing region.
In some
embodiments, this may represent a beneficial outcome of plaque stabilization
and connote
the efficacy of the statin medications on the plaque. Alternatively, over
time, a plaque may
not change in location over time and remain pericardial-facing. In this case,
in some
embodiments, this may represent an adverse outcome wherein the statin
medication is
ineffective in stabilizing the plaque. In some embodiments, should a plaque
not stabilize
to medical therapy (for example if the location of plaque remains pericardial-
facing or is
very slow to change), then another medication (e.g., PCSK-9 inhibitor or
other) may be
added, as the constancy in the location of plaque can be a titratable
biomarker that is used
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to guide medical therapy intensification and, ultimately, improve patient
outcomes (e.g., by
reducing myocardial infarction, rapid progression, ischemia, or other adverse
event).
[1180]
In some embodiments, the CT appearance of plaque location may be
influenced by a number of factors that may include one or more of: scanner
type, image
acquisition parameters (e.g., mA, kVp, etc.), energy (e.g., single-, dual-,
spectral, photon
counting, etc.), gating (e.g., axial vs. retrospective helical, etc.),
contrast, age, patient body
habitus, surrounding cardiac structures, plaque type (e.g., calcium may cause
partial
volume artifact, etc.), and/or others. In some embodiments, the system can be
configured
to normalize to one or more of these factors to further standardize
comparisons in plaque
types over time.
[1181]
In some embodiments, the system can be configured to track plaque
volume and/or plaque volume as a function of vessel volume (e.g., percent
atheroma
volume or PAV, etc.). In some embodiments, plaque volume and/or PAV can be
tracked
on a per-patient, per-vessel, per-segment or per-lesion basis. In some
embodiments, the
system can be configured to evaluate directionality of plaque volume or PAV
(e.g.,
increasing, decreasing or staying the same). In some embodiments, the system
can be
configured to leverage directionality changes to link to risk of disease
events. For example,
in some embodiments, an increase in plaque volume or PAV can be indicative of
higher
risk. Similarly, in some embodiments, slowing of plaque progression can be
indicative of
lower risk and/or the like. In some embodiments, the system can be configured
to guide
decision making as to whether to add another medication / intensify medical
therapy or
perform coronary revascularization. For example, in some embodiments, in
response to
increasing plaque volume or PAV, the system can be configured to propose
increased /
intensified medical therapy, other treatment, increased medication dosage,
and/or the like.
In some embodiments, the system can be configured to guide decision making in
order to
reduce adverse coronary events (e.g., acute coronary syndrome, rapid
progression,
ischemia, and/or the like).
[1182]
In some embodiments, the system can be configured to identify and/or
correlate certain adverse coronary events as being associated with increased
risk over time.
For example, in some embodiments, higher plaque volume and/or higher PAV can
result
in high risk of CAD events.
[1183]
In some embodiments, the system can be configured to track plaque
volume and/or PAV and assign a baseline risk based at least in part on its
presence, extent,
and/or severity. In some embodiments, after treatment with medication,
lifestyle or
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intervention, serial imaging (e.g., by CT) can be performed to determine
changes in these
features, which can be used to update risk assessment.
[1184]
In some embodiments, the system can be configured to provide a "treat
to the image" continuous approach that offers clinicians and patients a method
for following
plaque changes in location over time to ensure that the plaque is stabilizing
and the
prognosis is improving. For example, in some embodiments, in a patient may be
started
on a statin medication after their CT scan. Over time (e.g., months, etc.), a
plaque may
increase in volume or PAY. In some embodiments, this may represent an adverse
outcome
and connote the inefficacy of statin medications. Alternatively, over time,
the volume of
plaque may not change. In this case, in some embodiments, this may represent a
beneficial
outcome wherein the statin medication is effective in stabilizing the plaque.
In some
embodiments, should a plaque not stabilize to medical therapy (e.g., if plaque
volume or
PAY increases), then another medication (e.g., PCSK-9 inhibitor and/or other)
may be
added, as the constancy in the plaque volume or PAV can be a titratable
biomarker that is
used to guide medical therapy intensification and, ultimately, improve patient
outcomes
(e.g., by reducing myocardial infarction, rapid progression, ischemia, and/or
other adverse
event).
[1185]
In some embodiments, the CT appearance of plaque location may be
influenced by a number of factors that may include one or more of: scanner
type, image
acquisition parameters (e.g., mA, kVp, etc.), energy (e.g., single-, dual-,
spectral, photon
counting, etc.), gating (e.g., axial vs. retrospective helical, etc.),
contrast, age, patient body
habitus, surrounding cardiac structures, plaque type (e.g., calcium may cause
partial
volume artifact, etc.), and/or others. In some embodiments, the system can be
configured
to normalize to one or more of these factors to further standardize
comparisons in plaque
types over time.
[1186]
In some embodiments, the system can be configured to analyze and/or
report one or more of the overall changes described above related to plaque
composition,
vascular remodeling, and/or other features on a per-patient, per-vessel, per-
segment, and/or
per-lesion basis, for example to provide prognostic risk stratification either
in isolation
(e.g., just composition, etc.) and/or in combination (e.g., composition +
remodeling +
location, etc.).
[1187]
In some embodiments, the system can be configured to update risk
assessment and/or guide medical therapy, lifestyle changes, and/or
interventional therapy
based on image analysis and/or disease tracking. In particular, in some
embodiments, the
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system can be configured to report in a number of ways changes to
arteries/plaques that
occur on a continuous basis as a method for tracking disease stabilization or
worsening. In
some embodiments, as a method of tracking disease, the system can be
configured to report
the risk of adverse coronary events. For example, in some embodiments, based
upon
imaging-based changes, a quantitative risk score can be updated from baseline
at follow-
up. In some embodiments, the system can be configured to utilize a 4-category
method
that analyzes: (1) progression ¨ entails worsening (e.g., lower attenuation,
greater positive
remodeling, etc.); (2) regression ¨ entails diminution (e.g., higher
attenuation, lower
positive remodeling, etc.); (3) mixed response ¨ progression, but of more
prognostically
beneficial findings (e.g., higher volume of plaque over time, but with
calcified 1K plaque
dominant) (mixed response can also include plaque remodeling and location);
and/or (4)
mixed response ¨ progression, but of more prognostically adverse findings
(higher volume
of plaque over time, but with more non-calcified low attenuation plaques)
(mixed response
can also include plaque remodeling and location). In some embodiments, for
tracking
disease as a method to guide therapy, intensification of medical therapy
and/or institution
of lifestyle changes or coronary rev ascularization may occur and be prompted
by increased
risk of adverse coronary events or being in the "progression" or "mixed
response ¨
progression of calcified plaque" categories for example. Further, in some
embodiments,
serial tracking of disease and appropriate intensification of medical therapy,
lifestyle
changes or coronary revascularization based upon composition, remodeling
and/or location
changes, can be provided as a guide to reduce adverse coronary events
[1188]
Figure 22B is a flowchart illustrating an overview of an example
embodiment(s) of a method for treating to the image. As illustrated in Figure
22B, in some
embodiments, the system is configured to access a first set of plaque and/or
vascular
parameters of a subject, such as for example relating to the coronaries, at
block 2202. In
some embodiments, one or more plaque and/or vascular parameters can be
accessed from
a plaque and/or vascular parameter database 2204. In some embodiments, one or
more
plaque and/or vascular parameters can be derived and/or analyzed from one or
more
medical images being stored in a medical image database 100.
[1189]
The one or more plaque parameters and/or vascular parameters can
include any such parameters described herein. As a non-limiting example, the
one or more
plaque parameters can include one or more of density, location, or volume of
one or more
regions of plaque. The density can be absolute density, Hounsfield unit
density, and/or the
like. The location of one or more regions of plaque can be determined as one
or more of
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myocardial facing, pericardial facing, at a bifurcation, at a trifurcation,
proximal, mid, or
distal along a vessel, or in a main vessel or branch vessel, and/or the like.
The volume can
be absolute volume, PAV, and/or the like. Further, the one or more vascular
parameters
can include vascular remodeling or any other vascular parameter described
herein. For
example, vascular remodeling can include directionality changes in remodeling,
such as
outward, intermediate, or inward. In some embodiments, vascular remodeling can
include
vascular remodeling of one or more coronary atherosclerotic lesions.
[1190]
In some embodiments, at block 2206, the subject can be treated with
some medical treatment to address a disease, such as CAD. In some embodiments,
the
treatment can include one or more medications, lifestyle changes or
conditions,
revascularization procedures, and/or the like. For example, in some
embodiments,
medication can include statins, human immunodeficiency virus (HIV)
medications,
icosapent ethyl, bempedoic acid, rivaroxaban, aspirin, proprotein convertase
subtilisin/kexin type 9 (PCSK-9) inhibitors, inclisiran, sodium-glucose
cotransporter-2
(SGLT-2) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, low-
density
lipoprotein (LDL) apheresis, and/or the like. In some embodiments, lifestyle
changes or
condition can include increased exercise, aerobic exercise, anaerobic
exercise, cessation of
smoking, change in diet, and/or the like. In some embodiments,
revascularization can
include bypass grafting, stenting, use of a bioabsorbable scaffold, and/or the
like.
[1191]
In some embodiments, at block 2208, the system can be configured to
access one or more medical images of the subject taken after the subject is
treated with the
medical treatment for some time. The medical image can include any type of
image
described herein, such as for example, CT, MRI, and/or the like. In some
embodiments, at
block 2210, the system can be configured to identify one or more regions of
plaque on the
one or more medical images, for example using one or more image analysis
techniques
described herein. In some embodiments, at block 2212, the system can be
configured to
analyze the one or more medical images to determine a second set of plaque
and/or vascular
parameters. The second set of plaque and/or vascular parameters can be stored
and/or
accessed from the plaque and/or vascular parameter database 2204 in some
embodiments.
The second set of plaque and/or vascular parameters can include any parameters
described
herein, including for example those of the first set of plaque and/or vascular
parameters.
[1192]
In some embodiments, the system at block 2214 can be configured to
normalize one or more of the first set of plaque parameters, first set of
vascular parameters,
second set of plaque parameters, and/or second set of vascular parameters. As
discussed
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herein, one or more such parameters or quantification thereof can depend on
the scanner
type or scan parameter used to obtain a medical image from which such
parameters were
derived from. As such, in some embodiments, it can be advantageous to
normalize for such
differences. To do so, in some embodiments, the system can be configured to
utilize
readings obtained from a normalization device as described herein.
[1193]
In some embodiments, the system at block 2216 can be configured to
analyze one or more changes between the first set of plaque parameters and the
second set
of plaque parameters. For example, in some embodiments, the system can be
configured
to analyze changes between a specific type of plaque parameter. In some
embodiments,
the system can be configured to generate a first weighted measure of one or
more of the
first set of plaque parameters and a second weighted measure of one or more of
the second
set of plaque parameters and analyze changes between the first weighted
measure and the
second weighted measure. The weighted measure can be generated in some
embodiments
by applying a mathematical transform or any other technique described herein.
[1194]
In some embodiments, the system at block 2218 can be configured to
analyze one or more changes between the first set of vascular parameters and
the second
set of vascular parameters. For example, in some embodiments, the system can
be
configured to analyze changes between a specific type of vascular parameter.
In some
embodiments, the system can be configured to generate a first weighted measure
of one or
more of the first set of vascular parameters and a second weighted measure of
one or more
of the second set of' vascular parameters and analyze changes between the
first weighted
measure and the second weighted measure. The weighted measure can be generated
in
some embodiments by applying a mathematical transform or any other technique
described
herein.
[1195]
In some embodiments, at block 2220, the system can be configured to
track the progression of a disease, such as CAD, based on the analyzed changes
between
one or more plaque parameters and/or vascular parameters. In some embodiments,
the
system can be configured to determine progression of a disease based on
analyzing changes
between a weighted measure of one or more plaque parameters and/or vascular
parameters
as described herein. In some embodiments, the system can be configured to
determine
progression of a disease based on analyzing changes between one or more
specific plaque
parameters and/or vascular parameters. In particular, in some embodiments, an
increase in
density of the one or more regions of plaque can be indicative of disease
stabilization. In
some embodiments, a change in location of a region of plaque from pericardial
facing to
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myocardial facing is indicative of disease stabilization. In some embodiments,
an increase
in volume of the one or more regions of plaque between the first point in time
and the
second point in time is indicative of disease stabilization. In some
embodiments, more
outward remodeling between the first point in time and the second point in
time is indicative
of disease stabilization. In some embodiments, disease progression is tracked
on one or
more of a per-subject, per-vessel, per-segment, or per-lesion basis. In some
embodiments,
disease progression can be determined by the system as one or more of
progression,
regression, mixed response ¨ progression of calcified plaque, mixed response ¨
progression
of non-calcified plaque.
111961
In some embodiments, at block 2222, the system can be configured to
determine the efficacy of the medical treatment, for example based on the
tracked disease
progression. As such, in some embodiments, changes in one or more plaque
and/or
vascular parameters as derived from one or more medical images using image
analysis
techniques can be used as a biomarker for assessing treatment. In some
embodiments, the
system can be configured to determine efficacy of a treatment based on
analyzing changes
between a weighted measure of one or more plaque parameters and/or vascular
parameters
as described herein. In some embodiments, the system can be configured to
determine
efficacy of a treatment based on analyzing changes between one or more
specific plaque
parameters and/or vascular parameters. In particular, in some embodiments, an
increase in
density of the one or more regions of plaque can be indicative of a positive
efficacy of the
medical treatment_ In some embodiments, a change in location of a region of
plaque from
pericardial facing to myocardial facing is indicative of a positive efficacy
of the medical
treatment. In some embodiments, an increase in volume of the one or more
regions of
plaque between the first point in time and the second point in time is
indicative of a negative
efficacy of the medical treatment. In some embodiments, more outward
remodeling
between the first point in time and the second point in time is indicative of
a negative
efficacy of the medical treatment.
[1197]
In some embodiments, at block 2224, the system is configured to
generate a proposed medical treatment for the subject based on the determined
efficacy of
the prior treatment. For example, if the prior treatment is determined to be
positive or
stabilizing the disease, the system can be configured to propose the same
treatment. In
some embodiments, if the prior treatment is determined to be negative or not
stabilizing the
disease, the system can be configured to propose a different treatment. The
newly proposed
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treatment can include any of the types of treatment discussed herein, for
example including
those discussed in connection with the prior treatment at block 2206.
Determining Treatment(s) for Reducing Cardiovascular Risk and/or Events
[1198]
Some embodiments of the systems, devices, and methods described
herein are configured to determine a treatment(s) for reducing cardiovascular
risk and/or
events. In particular, some embodiments of the systems and methods described
herein are
configured to automatically and/or dynamically determine or generate
lifestyle, medication
and/or interventional therapies based upon actual atherosclerotic
cardiovascular disease
(ASCVD) burden, ASCVD type, and/or and ASCVD progression. As such, some
systems
and methods described herein can provide personalized medical therapy is based
upon
CCTA-characterized ASCVD. In some embodiments, the systems and methods
described
herein are configured to dynamically and/or automatically analyze medical
image data,
such as for example non-invasive CT, MRT, and/or other medical imaging data of
the
coronary region of a patient, to generate one or more measurements indicative
or associated
with the actual ASCVD burden, ASCVD type, and/or ASCVD progression, for
example
using one or more artificial intelligence (Al) and/or machine learning (ML)
algorithms. In
some embodiments, the systems and methods described herein can further be
configured to
automatically and/or dynamically generate one or more patient-specific
treatments and/or
medications based on the actual ASCVD burden, ASCVD type, and/or ASCVD
progression, for example using one or more artificial intelligence (AI) and/or
machine
learning (ML) algorithms. In some embodiments, the system can be configured to
utilize
a normalization device, such as those described herein, to account for
differences in scan
results (such as for example density values, etc.) between different scanners,
scan
parameters, and/or the like.
[1199]
In some embodiments of cardiovascular risk assessment of
asymptomatic individuals, the system can be configured to use one or more risk
factors to
guide risk stratification and treatment. For example, some cardiovascular risk
factors can
include measurements of surrogate measures of coronary artery disease (CAD) of
clinical
states that contribute to CAD, including dyslipidemia, hypertension, diabetes,
and/or the
like. In some embodiments, such factors can form the basis of treatment
recommendations
in professional societal guidelines, which can have defined goals for medical
treatment and
lifestyle based upon these surrogate markers of CAD, such as total and LDL
cholesterol
(blood biomarkers), blood pressure (biometric) and hemoglobin Al C (blood
biomarker).
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In some embodiments, this approach can improve population-based survival and
reduces
the incidence of heart attacks and strokes. However, in some embodiments,
these methods
also suffer a lack of specificity, wherein treatment can be more effective in
populations but
may not pinpoint individual persons who harbor residual risk. As an example,
LDL has
been found in population-based studies to explain only 29% of future heart
attacks and,
even in the pivotal statin treatment trials, those individuals treated
effectively with statins
still retain 70-75% residual risk of heart attacks.
112001
As such, some embodiments described herein address such technical
shortcomings by leveraging lifestyle, medication and/or interventional
therapies based
upon actual atherosclerotic cardiovascular disease (ASCVD) burden, ASCVD type,
and/or
and ASCVD progression. Given the multitude of medications available to target
the
ASCVD process through atherosclerosis, thrombosis and inflammatory pathways,
in some
embodiments, such direct precision-medicine ASCVD diagnosis and treatment
approach
can be more effective than treating surrogate markers of ASCVD at the
individual level.
112011
In some embodiments, the systems and methods described herein are
configured to automatically and/or dynamically determine or generate
lifestyle, medication
and/or interventional therapies based upon actual atherosclerotic
cardiovascular disease
(ASCVD) burden, ASCVD type, and/or and ASCVD progression. In particular, in
some
embodiments, the systems and methods are configured to use coronary computed
tomographic angiography (CCTA) for quantitative assessment of ASCVD in one or
more
or all vascular territories, including for example coronary, carotid, aortic,
lower extremity,
cerebral, renal arteries, and/or the like. In some embodiments, the systems
and methods
are configured to analyze and/or utilize not only the amount (or burden) of
ASCVD, but
also the type of plaque in risk stratification. For example, in some
embodiments, the
systems and methods are configured to associate low attenuation plaques (LAP)
and/or
non-calcified plaques (NCP) of certain densities with future major adverse
cardiovascular
events (MACE), whilst associating calcified plaques and, in particular,
calcified plaques of
higher density as being more stable. Further, in some embodiments, the systems
and
methods are configured to generate a patient-specific treatment plan that can
include use of
medication that has been associated with a reduction in LAP or NCP of certain
densities
and/or an acceleration in calcified plaque formation in populations, i.e., a
transformation
of plaque by compositional burden. In some embodiments, the systems and
methods are
configured to generate a patient-specific treatment plan that can include use
of medications
which can be observed by CCTA to be associated with modification of ASCVD in
the
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coronary arteries, carotid arteries, and/or other arteries, such as for
example statins, PCSK9
inhibitors, GLP receptor agonists, icosapent ethyl, and/or colchicine, amongst
others.
112021
As described herein, in some embodiments, the systems and methods
are configured to leverage ASCVD burden, type, and/or progression to logically
guide
clinical decision making. In particular, in some embodiments, the systems and
methods
described herein are configured to leverage, analyze, and/or utilize ASCVD
burden, type,
and/or progression to guide medical therapy to reduce adverse ASCVD events
and/or
improve patient-specific event-free survival in a personalized fashion. For
example, in
some embodiments, the system can be configured to analyze and/or utilize ASCVD
type,
such as pen-lesion tissue atmosphere, localization, and/or the like.
112031
More specifically, in some embodiments, the systems and methods
described herein are configured to utilize one or more CCTA algorithms and/or
one or more
medical treatment algorithms that quantify the presence, extent, severity
and/or type of
ASCVD, such as for example its localization and/or pen-lesion tissues. In some
embodiments, the one or more medical treatment algorithms are configured to
analyze any
medical images obtained from any imaging modality, such as for example
computed
tomography (CT), magnetic resonance (MR), ultrasound, nuclear medicine,
molecular
imaging, and/or others. In some embodiments, the systems and methods described
herein
are configured to utilize one or more medical treatment algorithms that are
personalized
(rather than population-based), treat actual disease (rather than surrogate
markers of
disease, such as risk factors), and/or are guided by changes in CCTA-
identified ASCVD
over time (such as for example, progression, regression, transformation,
and/or
stabilization). In some embodiments, the one or more CCTA algorithms and/or
the one or
more medical treatment algorithms are computer-implemented algorithms and/or
utilize
one or more AT and/or ML algorithms.
112041
In some embodiments, the systems and methods are configured to assess
a baseline ASCVD in an individual. In some embodiments, the systems and
methods are
configured to evaluate ASCVD by utilizing coronary CT angiography (CCTA). In
some
embodiments, the systems and methods are configured to identify and/or analyze
the
presence, local, extent, severity, type of atherosclerosis, pen-lesion tissue
characteristics,
and/or the like. In some embodiments, the method of ASCVD evaluation can be
dependent
upon quantitative imaging algorithms that perform analysis of coronary,
carotid, and/or
other vascular beds (such as, for example, lower extremity, aorta, renal,
and/or the like).
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112051
In some embodiments, the systems and methods are configured to
categorize ASCVD into specific categories based upon risk. For example, some
example
of such categories can include: Stage 0, Stage I, Stage II, Stage III; or
none, minimal, mild,
moderate/severe; or primarily calcified vs. primarily non-calcified; or X
units of low
density non-calcified plaque); or X% of NCP as a function of overall volume or
burden. In
some embodiments, the systems and methods can be configured to quantify ASCVD
continuously. In some embodiments, the systems and methods can be configured
to define
categories by levels of future ASCVD risk of events, such as heart attack,
stroke,
amputation, dissection, and/or the like. In some embodiments, one or more
other non-
ASCVD measures may be included to enhance risk assessment, such as for example
cardiovascular measurements (e.g., left ventricular hypertrophy for
hypertension; atrial
volumes for atrial fibrillation; fat; etc.) and/or non-cardiovascular
measurements that may
contribute to ASCVD (e.g., emphysema, etc.). In some embodiments, these
measurements
can be quantified using one or more CCTA algorithms.
112061
In some embodiments, the systems and methods described herein can be
configured to generate a personalized or patient-specific treatment. More
specifically, in
some embodiments, the systems and methods can be configured to generate
therapeutic
recommendations based upon ASCVD presence, extent, severity, and/or type. In
some
embodiments, rather than utilizing risk factors (such as, for example,
cholesterol, diabetes),
the treatment algorithm can comprise and/or utilize a tiered approach that
intensifies
medical therapy, lifestyle, and/or interventional therapies based upon ASCVD
directly in a
personalized fashion. In some embodiments, the treatment algorithm can be
configured to
generally ignore one or more conventional markers of success (e.g., lowering
cholesterol,
hemoglobin AlC, etc.) and instead leverage ASCVD presence, extent, severity,
and/or type
of disease to guide therapeutic decisions of medical therapy intensification.
In some
embodiments, the treatment algorithm can be configured to combine one or more
conventional markers of success (e.g., lowering cholesterol, hemoglobin Al C,
etc.) with
ASCVD presence, extent, severity, and/or type of disease to guide therapeutic
decisions of
medical therapy intensification. In some embodiments, the treatment algorithm
can be
configured to combine one or more novel markers of success (e.g., such as
genetics,
transcriptomics, or other 'omics measurements, etc.) with ASCVD presence,
extent,
severity, and/or type of disease to guide therapeutic decisions of medical
therapy
intensification. In some embodiments, the treatment algorithm can be
configured to
combine one or more other imaging markers of success (e.g., such as carotid
ultrasound
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imaging, abdominal aortic ultrasound or computed tomography, lower extremity
arterial
evaluation, and/or others) with ASCVD presence, extent, severity, and/or type
of disease
to guide therapeutic decisions of medical therapy intensification.
[1207]
In some embodiments, the systems and methods are configured to
perform a response assessment. In particular, in some embodiments, the systems
and
methods are configured to perform repeat and/or serial CCTA in order to
determine the
efficacy of therapy on a personalized basis, and to determine progression,
stabilization,
transformation, and/or regression of ASCVD. In some embodiments, progression
can be
defined as rapid or non-rapid. In some embodiments, stabilization can be
defined as
transformation of ASCVD from non-calcified to calcified, or reduction of low
attenuation
plaque, or reduction of positive arterial remodeling. In some embodiments,
regression of
ASCVD can be defined as a decrease in ASCVD volume or burden or a decrease in
specific
plaque types, such as non-calcified or low attenuation plaque.
[1208]
In some embodiments, the systems and methods are configured to
update personalized treatment based upon response assessment. In particular,
in some
embodiments, based upon the change in ASCVD between the baseline and follow-up
CC TA, personalized treatment can be updated and intensified if worsening
occurs or de-
escalated / kept constant if improvement occurs. As a non-limiting example, if
stabilization
has occurred, this can be evidence of the success of the current medical
regimen.
Alternatively, as another non-limiting example, if stabilization has not
occurred and
ASCVD has progressed, this can be evidence of the failure of the current
medical regimen,
and an algorithmic approach can be taken to intensify medical therapy.
[1209]
In some embodiments, the intensification regimen employs lipid
lowering agents in a tiered fashion, and considers ASCVD presence, extent,
severity, type,
and/or progression. In some embodiments, the intensification regimen considers
local
and/or pen-lesion tissue. In some embodiments, the intensification regimen and
use of the
medications therein can be guided also by LDL cholesterol and triglyceride
(TG) and Lp(a)
and Apo(B) levels; or cholesterol particle density and size. For example,
FIGS. 23F-G
illustrate an example embodiment(s) of a treatment(s) employing lipid lowering
medication(s) and/or treatment(s) generated by an example embodiment(s) of
systems and
methods for determining treatments for reducing cardiovascular risk and/or
events.
[1210]
In some embodiments, given the multidimensional nature of MACE
contributors that include ASCVD, inflammation and thrombosis, the
intensification
regimen can incorporate anti-inflammatory medications (e.g., colchicine)
and/or anti-
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thrombotic medications (e.g., rivaroxaban and aspirin) in order to control the
ASCVD
progress. In some embodiments, new diabetic medications that have salient
effects on
reducing MACE events¨including SGLT2 inhibitors and GLP1R agonists¨can also be
incorporated. For example, FIGS. 23H-I illustrate an example embodiment(s) of
a
treatment(s) employing diabetic medication(s) and/or treatment(s) generated by
an example
embodiment(s) of systems and methods for determining treatments for reducing
cardiovascular risk and/or events.
[1211]
Figure 23A illustrates an example embodiment(s) of systems and
methods for determining treatments for reducing cardiovascular risk and/or
events. In some
embodiments, the systems and methods described herein are configured to
analyze
coronaries. In some embodiments, the systems and methods can also be applied
to other
arterial bed as well, such as the aorta, carotid, lower extremity, renal
artery, cerebral artery,
and/or the like.
[1212]
In some embodiments, the system can be configured to determine and/or
utilize in its analysis the presence of ASCVD, which can be the presence vs.
absence of
plaque, the presence vs. absence of non-calcified plaque, the presence vs.
absence of low
attenuation plaque, and/or the like.
[1213]
In some embodiments, the system can be configured to determine and/or
utilize in its analysis the extent of ASCVD, which can include the total ASCVD
volume,
percent atheroma volume (atheroma volume / vessel volume x 100), total
atheroma volume
normalized to vessel length (TAVnorm), diffuseness (% of vessel affected by
ASCVD),
and/or the like.
[1214]
In some embodiments, the system can be configured to determine and/or
utilize in its analysis severity of ASCVD. In some embodiments, ASCVD severity
can be
linked to population-based estimates normalized to age-, gender-, ethnicity-,
CAD risk
factors, and/or the like. In some embodiments, ASCVD severity can include
angiographic
stenosis >70% or >50% in none, 1-, 2-, and/or 3-VD.
[1215]
In some embodiments, the system can be configured to determine and/or
utilize in its analysis the type of ASCVD, which can include for example the
proportion
(ratio, %, etc.) of plaque that is non-calcified vs. calcified, proportion of
plaque that is low
attenuation non-calcified vs. non-calcified vs. low density calcified vs. high-
density
calcified, absolute amount of non-calcified plaque and calcified plaque,
absolute amount
of plaque that is low attenuation non-calcified -Vs. non-calcified vs. low
density calcified
vs. high-density calcified, continuous grey-scale measurement of plaques
without ordinal
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classification, radiomic features of plaque, including heterogeneity and
others, vascular
remodeling imposed by plaque as positive remodeling (>1.10 or >1.05 ratio of
vessel
diameter / normal reference diameter; or vessel area / normal reference area;
or vessel
volume / normal reference volume) vs. negative remodeling (<1.10 or <1.05),
vascular
remodeling imposed by plaque as a continuous ratio, and/or the like.
[1216]
In some embodiments, the system can be configured to determine and/or
utilize in its analysis the locality of plaque, such as for example in the
arterial bed, regarding
vessel, segment, bifurcation, and/or the like.
[1217]
In some embodiments, the system can be configured to determine and/or
utilize in its analysis the pen-lesion tissue environment, such as for example
density of the
pen-plaque tissues such as fat, amount of fat in the pen-vascular space,
radiomic features
of pen-lesion tissue, including heterogeneity and others, and/or the like.
[1218]
In some embodiments, the system can be configured to determine and/or
utilize in its analysis ASCVD progression. In some embodiments, progression
can be
defined as rapid vs. non-rapid, with thresholds to define rapid progression
(e.g., >1.0%
percent atheroma volume, >200 mm3 plaque, etc.). In some embodiments, serial
changes
in ASCVD can include rapid progression, progression with primarily calcified
plaque
formation, progression with primarily non-calcified plaque formation, and
regression.
[1219]
In some embodiments, the system can be configured to determine and/or
utilize in its analysis one or more categories of risk. In some embodiments,
the system can
be configured to utilize one or more stages, such as 0,1, II, or III based
upon plaque volumes
associated with angiographic severity (such as, for example, none, non-
obstructive, and
obstructive 1VD, 2VD and 3VD). In some embodiments, the system can be
configured to
utilize one or more percentiles, for example taking into account age, gender,
ethnicity,
and/or presence of one or more risk factors (such as, diabetes, hypertension,
etc.). In some
embodiments, the system can be configured to determine a percentage of
calcified plaque
vs. percentage of non-calcified plaque as a function of overall plaque volume.
In some
embodiments, the system can be configured to determine the number of units of
low density
non-calcified plaque. In some embodiments, the system can be configured to
generate a
continuous 3D histogram and/or geospatial map (for plaque geometry) analysis
of grey
scales of plaque by lesion, by vessel, and/or by patient. In some embodiments,
risk can be
defined in a number of ways, including for example risk of MACE, risk of
angina, risk of
ischemia, risk of rapid progression, risk of medication non-response, and/or
the like.
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112201
In some embodiments, treatment recommendations can be based upon
ASCVD presence, extent, severity type of disease, ASCVD progression, and/or
the like.
For example, FIGS. 23F-G illustrate an example embodiment(s) of a treatment(s)
employing lipid lowering medication(s) and/or treatment(s) and FIGS. 23H-I
illustrate an
example embodiment(s) of a treatment(s) employing diabetic medication(s)
and/or
treatment(s) generated by an example embodiment(s) of systems and methods for
determining treatments for reducing cardiovascular risk and/or events.
112211
In some embodiments, the generated treatment protocols are aimed (e.g.,
based upon CCTA-based ASCVD characterization) to properly treat at the right
point in
time with medications aimed at ASCVD stabilization, inflammation reduction,
and/or
reduction of thrombosis potential. In some embodiments, the rationale behind
this is that
ASCVD events can be an inflammatory atherothrombotic phenomenon, but serum
biomarkers, biometrics and conventional measures of angiographic stenosis
severity can be
inadequate to optimally define risk and guidance to clinical decision making.
As such,
some systems and methods described herein can provide personalized medical
therapy is
based upon CCTA-characterized ASCVD.
112221
In some embodiments, the system can be configured to generate a risk
score that combines one or more traditional risk factors, such as the ones
described herein,
together with one or more quantified ASCVD measures. In some embodiments, the
system
can be configured to generate a risk score that combines one or more genetics
analysis with
one or more quantified ASCVD measures, as some medications may work better on
some
people and/or people with particular genes. In addition, in some embodiments,
the system
can be configured to exclude or deduct certain plaque from the rest of
disease. For example,
in some embodiments, the system can be configured to ignore or exclude high
density
calcium that is so stable that the risk of having it can be better than having
a disease without
it, such that the existence of such plaque may impact risk negatively.
112231
FIGS. 23B-C illustrate an example embodiment(s) of definitions or
categories of atherosclerosis severity used by an example embodiment(s) of
systems and
methods for determining treatments for reducing cardiovascular risk and/or
events.
112241
Figure 23D illustrates an example embodiment(s) of definitions or
categories of disease progression, stabi 1 i zati on, and/or regression used
by an example
embodiment(s) of systems and methods for determining treatments for reducing
cardiovascular risk and/or events.
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112251
Figure 23E illustrates an example embodiment(s) of a time-to-treatment
goal(s) for an example embodiment(s) of systems and methods for determining
treatments
for reducing cardiovascular risk and/or events.
112261
Figure 23J is a flowchart illustrating an overview of an example
embodiment(s) of a method for determining treatments for reducing
cardiovascular risk
and/or events. As illustrated in Figure 23J, in some embodiments, the system
is configured
to determine a proposed personalized treatment for a subject to lower ASCVD
risk based
on CCTA analysis using one or more quantitative image analysis techniques
and/or
algorithms.
112271
In particular, in some embodiments, the system can be configured to
access one or more medical images taken from a first point in time at block
2302, for
example from a medical image database 100. The one or more medical images can
include
images obtained using any imaging modality described herein. In some
embodiments, the
one or more medical images can include one or more arteries, such as for
example coronary,
carotid, lower extremity, upper extremity, aorta, renal, and/or the like.
112281
In some embodiments, the system at block 2304 can be configured to
analyze the one or more medical images. More specifically, in some
embodiments, the
system can be configured to utilize CCTA analysis and/or quantitative imaging
algorithms
to identify and/or derive one or more parameters from the medical image. In
some
embodiments, the system can be configured to store one or more identified
and/or derived
parameters in a parameter database 2306. In some embodiments, the system can
be
configured to access one or more such parameters from a parameter database
2306. In
some embodiments, the system can be configured to analyze one or more plaque
parameters, vascular parameters, atherosclerosis parameters, and/or
perilesional tissue
parameters. The plaque parameters and/or vascular parameters can include any
one or more
such parameters discussed herein.
112291
In some embodiments, at block 2308, the system can be configured to
assess a baseline ASCVD risk of the subject based on one or more such
parameters. In
some embodiments, at block 2310, the system can be configured to categorize
the baseline
ASCVD risk of the subject. In some embodiments, the system can be configured
to
categorize the baseline ASCVD risk into one or more predetermined categories.
For
example, in some embodiments, the system can be configured to categorize the
baseline
ASCVD risk as one of Stage 0, I, II, or III. In some embodiments, the system
can be
configured to categorize the baseline ASCVD risk as one of none, minimal,
mild, or
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moderate. In some embodiments, the system can be configured to categorize the
baseline
ASCVD risk as one of primarily calcified or primarily non-calcified plaque. In
some
embodiments, the system can be configured to categorize the baseline ASCVD
risk based
on units of low density non-calcified plaque identified from the image. In
some
embodiments, the system is configured to categorize the baseline ASCVD risk on
a
continuous scale. In some embodiments, the system is configured to categorize
the baseline
ASCVD risk based on risk of future ASCVD events, such as heart attack, stroke,
amputation, dissection, and/or the like. In some embodiments, the system is
configured to
categorize the baseline ASCVD risk based on one or more non-ASCVD measures,
which
can be quantified using one or more CCTA algorithms. For example, non-ASCVD
measures can include one or more cardiovascular measurements (e.g., left
ventricular
hypertrophy for hypertension or atrial volumes for atrial fibrillation, and/or
the like) or non-
cardiovascular measurements that may contribute to ASCVD (e.g., emphysema,
etc.).
112301
In some embodiments, the system at block 2312 can be configured to
determine an initial proposed treatment for the subject. In some embodiments,
the system
can be configured to determine an initial proposed treatment with or without
analysis of
cholesterol or hemoglobin AlC. In some embodiments, the system can be
configured to
determine an initial proposed treatment with or without analysis of low-
density lipoprotein
(LDL) cholesterol or triglyceride (TG) levels of the subject.
112311
In some embodiments, the initial proposed treatment can include
medical therapy, lifestyle therapy, and/or interventional therapy. For
example, medical
therapy can include one or more medications, such as lipid-lowering
medications, anti-
inflammatory medications (e. g. , col chicine, etc.), anti-thrombotic
medications (e.g.,
rivaroxaban, aspirin, etc.), diabetic medications (e.g., sodium-glucose
cotransporter-2
(SGLT2) inhibitors, glucagon-like peptide-1 receptor (GLP IR) agonists, etc.),
and/or the
like. Lifestyle therapy and/or interventional therapy can include any one or
more such
therapies discussed herein. In some embodiments, at block 2314, the subject
can be treated
with one or more such medical treatments.
112321
In some embodiments, the system at block 2316 can be configured to
access one or more medical images taken from a second point in time after the
subject is
treated with the initial treatment, for example from a medical image database
100. The one
or more medical images can include images obtained using any imaging modality
described
herein. In some embodiments, the one or more medical images can include one or
more
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arteries, such as for example coronary, carotid, lower extremity, upper
extremity, aorta,
renal, and/or the like.
112331
In some embodiments, the system at block 2318 can be configured to
analyze the one or more medical images taken at the second point in time. More
specifically, in some embodiments, the system can be configured to utilize
CCTA analysis
and/or quantitative imaging algorithms to identify and/or derive one or more
parameters
from the medical image. In some embodiments, the system can be configured to
store one
or more identified and/or derived parameters in a parameter database 2306. In
some
embodiments, the system can be configured to access one or more such
parameters from a
parameter database 2306. In some embodiments, the system can be configured to
analyze
one or more plaque parameters, vascular parameters, atherosclerosis
parameters, and/or
perilesional tissue parameters. The plaque parameters and/or vascular
parameters can
include any one or more such parameters discussed herein.
112341
In some embodiments, at block 2320, the system can be configured to
assess an updated ASCVD risk of the subject based on one or more such
parameters. In
some embodiments, at block 2322, the system can be configured to categorize
the updated
ASCVD risk of the subject. In some embodiments, the system can be configured
to
categorize the updated ASCVD risk into one or more predetermined categories.
For
example, in some embodiments, the system can be configured to categorize the
updated
ASCVD risk as one of Stage 0, I, II, or III. In some embodiments, the system
can be
configured to categorize the updated ASCVD risk as one of none, minimal, mild,
or
moderate. In some embodiments, the system can be configured to categorize the
updated
ASCVD risk as one of primarily calcified or primarily non-calcified plaque. In
some
embodiments, the system can be configured to categorize the updated ASCVD risk
based
on units of low density non-calcified plaque identified from the image. In
some
embodiments, the system is configured to categorize the updated ASCVD risk on
a
continuous scale. In some embodiments, the system is configured to categorize
the updated
ASCVD risk based on risk of future ASCVD events, such as heart attack, stroke,
amputation, dissection, and/or the like. In some embodiments, the system is
configured to
categorize the updated ASCVD risk based on one or more non-ASCVD measures,
which
can be quantified using one or more CCTA algorithms. For example, non-ASCVD
measures can include one or more cardiovascular measurements (e.g., left
ventricular
hypertrophy for hypertension or atrial volumes for atrial fibrillation, and/or
the like) or non-
cardiovascular measurements that may contribute to ASCVD (e.g., emphysema,
etc.).
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112351
In some embodiments, the system at block 2324 can be configured to
assess the subject's response to the initial proposed treatment. For example,
in some
embodiments, the system can be configured to compare differences or changes in
ASCVD
risk and/or categorized ASCVD risk between the first point in time and the
second point in
time. In some embodiments, the subject response is assessed based on one or
more of
progression, stabilization, or regression of ASCVD. In some embodiments,
progression
can include rapid and/or non-rapid progression. In some embodiments,
stabilization can
include transformation of ASCVD from non-calcified to calcified, reduction of
low
attenuation plaque, and/or reduction of positive arterial remodeling. In some
embodiments,
regression can include decrease in ASCVD volume or burden, decrease in non-
calcified
plaque, and/or decrease in low attenuation plaque.
112361
In some embodiments, the system at block 2326 can be configured to
determine a continued proposed treatment for the subject, for example based on
the subject
response to the initial treatment. In particular, in some embodiments, if the
system
determines that there was progression in ASCVD risk in response to the initial
treatment,
the system can be configured to propose a higher tiered treatment compared to
the initial
treatment. In some embodiments, if the system determines that there was
stabilization or
regression in ASCVD risk in response to the initial treatment, the system can
be configured
to propose the same initial treatment or a same or similar tiered alternative
treatment or a
lower tiered treatment compared to the initial treatment. In some embodiments,
the system
can be configured to determine a continued proposed treatment with or without
analysis of
cholesterol or hemoglobin AlC. In some embodiments, the system can be
configured to
determine a continued proposed treatment with or without analysis of low-
density
lipoprotein (LDL) cholesterol or triglyceride (TG) levels of the subject.
112371
In some embodiments, the continued proposed treatment can include
medical therapy, lifestyle therapy, and/or interventional therapy. For
example, medical
therapy can include one or more medications, such as lipid-lowering
medications, anti-
inflammatory medications (e.g., colchicine, etc.), anti-thrombotic medications
(e.g.,
rivaroxaban, aspirin, etc.), diabetic medications (e.g., sodium-glucose
cotransporter-2
(SGLT2) inhibitors, glucagon-like peptide-1 receptor (GLPIR) agonists, etc.),
and/or the
like. Lifestyle therapy and/or interventional therapy can include any one or
more such
therapies discussed herein.
112381
In some embodiments, the system can be configured to repeat one or
more processes described in connection with Figure 23J at different points in
time. In other
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words, in some embodiments, the system can be configured to apply serial
analysis and/or
tracking of treatments to continue to monitor ASCVD of a subject and the
subject's
response to treatment for continued treatment of the subject.
Determining Treatment(s) for Reducing Cardiovascular Risk and/or Events
112391
Some embodiments of the systems, devices, and methods described
herein are configured to determine stenosis severity and/or vascular
remodeling in the
presence of atherosclerosis. In particular, some embodiments of the systems,
devices, and
methods described herein are configured to determine stenosis severity and
vascular
remodeling, for example whilst accounting for presence of plaque, natural
artery tapering,
and/or 3D volumes. In some embodiments, the systems, devices, and methods
described
herein are configured to determine % fractional blood volume, for example for
determining
of contribution of specific arteries and/or branches to important
pathophysiologic processes
(such as, risk of size of myocardial infarction; ischemia, and/or the like),
whilst accounting
for the presence of plaque in non-normal arteries. In some embodiments, the
systems,
methods, and devices described herein are configured to determine ischemia,
for example
by applying the continuity equation, whilst accounting for blood flow across a
range of
physiologically realistic ranges (e.g., ranges for rest, mild/moderate/extreme
exercise,
and/or the like).
111401
Generally speaking, coronary artery imaging can be a key component
for diagnosis, prognostication and/or clinical decision making of patients
with suspected or
known coronary artery disease (CAD). More specifically, in some embodiments,
an array
of coronary artery imaging parameters can be useful for guiding and informing
these
clinical tasks and can include such measures of arterial narrowing (stenosis)
and vascular
remodeling.
112411
In some embodiments, the system can be configured to define relative
arterial narrowing (stenosis) due to coronary artery atherosclerotic lesions.
In some
embodiments, these measures can largely rely upon (1) comparisons to diseased
regions to
normal regions of coronary vessels, and/or (2) 2D measures of diameter or area
reduction
due to coronary artery lesions. However, limitations can exist in such
embodiments.
112421
For example, in some of such embodiments, relative narrowing can be
difficult to determine in diseased vessels. Specifically, in some embodiments,
coronary
stenosis can be reported as a relative narrowing, i.e., Diameter disease /
Diameter normal
reference x 100% or Area disease/Area normal reference x 100%. However, in
some
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instances, coronary vessels are diffusely diseased, which can render
comparison of
diseased, stenotic regions to "normal" regions of the vessel problematic and
difficult when
there is no normal region of the vessel without disease to compare to.
[1243]
In addition, in some of such embodiments, stenosis measurements can
be reported in 2D, not 3D. Specifically, some embodiments rely upon imaging
methods
which are two-dimensional in nature and thus, report out stenoses as relative
% area
narrowing (2D) or relative % diameter narrowing (2D). Some of such embodiments
do not
account for the marked irregularity in coronary artery lesions that are often
present and do
not provide information about the coronary artery lesion across the length of
a vessel. In
particular, if the x-axis is considered the axial distance along a coronary
vessel, the y-axis
the width of an artery wall, and the z-axis the irregular topology of plaque
along the length
of a vessel, then it can become evident that a single % area narrowing or a
single % diameter
narrowing is inadequate to communicate the complexity of the coronary lesion.
[1244]
In some of such embodiments, because % area and % diameter stenosis
are based upon 2D measurements, certain methods that define stenosis severity
can rely
upon maximum % stenosis rather than the stenosis conferred by three-
dimensional
coronary lesions that demonstrate heterogeneity in length and degree of
narrowing across
their length (i.e., volume). As such, in some of such embodiments, tracking
over time can
be difficult (e.g., monitoring the effects of therapy) where changes in 2D
would be much
less accurate. A similar analogy can be when evaluating changes in a pulmonary
nodule
while the patient is in follow up, which can be much more accurate in 3D than
211).
[1245]
Furthermore, in some of such embodiments, the natural tapering of
arteries may not be accounted for any and/or all forms of imaging. As
illustrated in Figure
24A, the coronary arteries can naturally get smaller along their length. This
can be
problematic for % area and % diameter measurements, as these approaches may
not take
into account that a normal coronary artery tapers gradually along its length.
Hence, in some
of such embodiments, the comparison to a normal reference diameter or normal
reference
area has been to use the most normal appearing vessel segment/cross-section
proximal to a
lesion. In this case, because the proximal cross-section is naturally larger
(due to the
tapering), the actual % narrowing (by area or diameter) can be lower than it
actually is.
[1246]
As such, in some of such embodiments, there are certain limitations to
grading of coronary artery stenosis. Thus, it can be advantageous to account
for the
diffuseness of disease in a volumetric fashion, whilst accounting for natural
vessel tapering,
as in certain other embodiments described below. Instead, in some of such
embodiments
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described above, certain formulas can be used to evaluate these phenomena in 2
dimensions
rather than 3 dimensions, in which the relative degree of narrowing, also
called stenosis or
maximum diameter reduction, is determined by measuring the narrowest lumen
diameter
in the diseased segment and comparing it to the lumen diameter in the closest
adjacent
proximal disease-free section. In some of such embodiments, this is because
with plaque
present it can be no longer possible to measure directly what the lumen
diameter at that
point was originally.
112471
Similarly, in some of such embodiments, the remodeling index can be
problematic. In particular, in some of such embodiments, the remodeling index
is
determined by measuring the outer diameter of the vessel and this is compared
to the
diameter in the closest adjacent proximal disease-free section.
In some of such
embodiments, on CT imaging, the normal coronary artery wall is not resolved as
it's
thickness of ¨0.3 mm is beyond the ability of being depicted on CT due to
resolution
limitations.
112481
Some examples of these problems in some of such embodiments are
illustrated in FIGS. 24B-G and accompanying text. For example, Figure 24B
illustrates
such an embodiment(s) of determining % stenosis and remodeling index. In the
illustrated
embodiment(s), it is assumed that the diameter of the closest adjacent
proximal disease-
free section (R) accurately reflects what the diameter at the point of
stenosis or outward
remodeling would be. However, this simple formula may significantly
overestimate the
actual stenosis and underestimate the remodeling index. In particular, these
simple
formulas may not take into account that a normal coronary artery tapers
gradually along its
length as depicted in Figure 24C. As illustrated in Figure 24C, the coronary
diameter may
not be constant, but rather the vessel can taper gradually along its course.
For example, the
distal artery diameter (D2) may be less than 50% or more of the proximal
diameter (D1).
112491
Further, when there is a long atherosclerotic plaque present, the
reference diameter RO measured in a "normal" proximal part of the vessel may
have a
significantly larger diameter than the diameter that was initially present,
especially when
the measured stenosis or remodeling index is positioned far from the beginning
of the
plaque. This can introduce error into the Stenosis % equation, resulting in a
percent
diameter stenosis larger and remodeling index significantly lower than it
should be. As
illustrated in Figure 24D, when there is a long plaque positioned proximal to
the point of
maximal stenosis (Lx) or positive remodeling (Wx), in some of such
embodiments, the
reference diameter RO can be currently measured in the closest normal part of
the vessel;
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however at this point the vessel can be significantly larger than it would
have initially been
at position x, introducing error.
112501
Generally speaking, clinical decision making in cardiology is often
guideline driven and decisions often take the quantitative percent stenosis or
remodeling
index into account. For example, in the case of percent stenosis, a threshold
of 50 or 70%
can be used to determine if additional diagnostic testing or intervention is
required. As a
non-limiting example, Figure 24E depicts how an inaccurately estimated RO
could
significantly affect the resulting percent stenosis and remodeling index. As
illustrated in
Figure 24E, if the estimated RO is larger than the true lumen at the site of
stenosis or positive
remodeling, significant error can be introduced.
112511
In some embodiments, with current technology by imaging (including
but not limited to CT, MRI and others), the internal lumen (L) and outer (W)
is continuously
measurable along the entire length of a coronary artery. In some embodiments,
when the
lumen diameter is equal to the wall diameter, there is no atherosclerotic
plaque present, the
vessel is "normal.- Conversely, in some embodiments, when the wall diameter is
greater
than the lumen diameter, plaque is present. This is illustrated in Figure 24F.
As illustrated
in Figure 24F, in some embodiments, both the lumen diameter and outer wall
diameter are
continuously measured using current imaging techniques, such as CT. In some
embodiments, when L=W there is no plaque present.
112521
In some embodiments, an estimated reference diameter can be
calculated continuously at every point in the vessel where plaque is present.
For example,
by using the RO just before plaque, and a Rn just after the end of the plaque,
the degree of
tapering along the length of the plaque can be calculated. In some
embodiments, this degree
of tapering is, in most cases, linear; but may also taper in other
mathematically-predictable
fashions (log, quadratic, etc.) and hence, the measurements may be transformed
by certain
mathematical equations, as illustrated in Figure 24G. In some embodiments,
using the
formula in Figure 24G, an Rx can then be determined at any position along the
plaques
length. In some embodiments, this assumes that the "normal" vessel would have
tapered
in a linear (or other mathematically predictable fashions) manner across its
length. As
illustrated in Figure 24G, in some embodiments, the reference diameter can be
better
estimated continuously along the length of the diseased portion of the vessel
as long as the
diameter just before the plaque RO and just after the plaque Rn is known.
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[1253]
In some embodiments, once the continuous Rx reference diameter is
determined, a continuous percent stenosis and/or remodeling index across the
plaque and
be easily calculated, for example using the following.
R. % Stenosis, - X 100
Rx
Remodeling Index Rix -
[1254]
More specifically, in some embodiments, since the continuous lumen
diameter Lx and wall diameter Wx are already known, continuous values for
percent
stenosis and remodeling index and be easily calculated once the Rx values have
been
generated.
[1255]
As described above, in some embodiments, there are certain limitations
to calculating stenosis severity and remodeling index in two dimensions.
Further, even as
improved upon with the accounting of the vessel taper and presence of plaque
in some
embodiments, these approaches may still be limited in that they are reliant
upon 2D (areas,
diameters) rather than 3D measurements (e.g., volume). Thus, as described in
some
embodiments herein, an improvement to this approach may be to calculate
volumetric
stenosis, volumetric remodeling, and/or comparisons of compartments of the
coronary
artery to each other in a volumetric fashion.
[1256]
As such, in some embodiments, the systems, devices, and methods
described herein are configured to calculate volumetric stenosis, volumetric
remodeling,
and/or comparisons of compartments of the coronary artery to each other in a
volumetric
fashion, for example by utilizing one or more image analysis techniques to one
or more
medical images obtained from a subject using one or more medical imaging
scanning
modalities. In some embodiments, the system can be configured to utilize a
normalization
device, such as those described herein, to account for differences in scan
results (such as
for example density values, etc.) between different scanners, scan parameters,
and/or the
like.
[1257]
In particular, in some embodiments, volumetric stenosis is calculated as
illustrated in FIGS. 24H and 241. As illustrated in FIGS. 24H and 241, in some
embodiments, the system can be configured to analyze a medical image of a
subject to
identify one or more boundaries along a vessel. For example, in some
embodiments, the
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system can be configured to identify the theoretically or hypothetically
normal boundaries
of the artery wall in the case a plaque was not present. In some embodiments,
the system
can be configured to identify the lumen wall and, in the absence of plaque,
the vessel wall.
In some embodiments, the system can be configured to identify an area of
interrogation
(e.g., site of maximum obstruction). In some embodiments, the system can be
configured
to determine a segment with the plaque.
[1258]
Thus, in some embodiments as illustrated in Figure 241, % volumetric
stenosis can be calculated by the following equation, which accounts for the
3D irregularity
of contribution of the plaque to narrowing the lumen volume, whilst
considering the normal
vessel taper and hypothetically normal vessel wall boundary: Lumen volume
accounting
for plaque (which can be measured) / Volume of hypothetically normal vessel
(which can
be calculated) x 100% = Volumetric % stenosis.
[1259]
In some embodiments, an alternative method for % volume stenosis can
be to include the entire vessel volume (i.e., that which is measured rather
than that which
is hypothetical). This can be governed by the following equation: Lumen volume
accounting for plaque (which can be measured) / Volume of vessel (which can be
measured) x 100% = Volumetric % stenosis.
[1260]
In some embodiments, another alternative method for determining %
volumetric stenosis is to include the entire artery (i.e., that which is
before, at the site of,
and after a narrowing), as illustrated in Figure 241.
[1261]
In some embodiments, the systems, devices, and methods described
herein are configured to calculate volumetric remodeling. In particular, in
some
embodiments, volumetric remodeling can account for the natural tapering of a
vessel, the
3D nature of the lesion, and/or the comparison to a proper reference standard.
Figure 24J
is a schematic illustration of an embodiment(s) of determining volumetric
remodeling. In
the example of Figure 24J, the remodeling index of Lesion #1, that is 5.2 mm
in length, is
illustrated.
[1262]
As illustrated in Figure 24J, in some embodiments, the system can be
configured to identify from a medical image a length of Lesion #1 in which a
region of
plaque is present (note the natural 8% taper by area, diameter or volume). In
some
embodiments, the system can be configured to identify a lesion length
immediately before
Lesion #1 in a normal part of the vessel (note the natural 12% taper by area,
diameter or
volume). In some embodiments, the system can be configured to identify a
lesion length
immediately after Lesion #1 in a normal part of the vessel (note the natural
6% taper by
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area, diameter or volume). In some embodiments, the system can be configured
to identify
one or more regions of plaque. In some embodiments, the system can be
configured to
identify or determine a 3D volume of the vessel across the lesion length of
5.2mm
immediately before and/or after Lesion #1 and/or in Lesion #1.
112631
In some embodiments, the system can be configured to calculate a
Volumetric Remodeling Index by the following: (Volume within Lesion #1 had
plaque not
been present + Volume of plaque in Lesion #1 exterior to the vessel wall) /
Volume within
Lesion #1 had plaque not been present. By utilizing this formula, in some
embodiments,
the resulting volumetric remodeling index can take into account tapering, as
the volume
within lesion #1 had plaque not been present takes into account any effect of
tapering.
112641
In some embodiments, the Volumetric Remodeling Index can be
calculated using other methods, such as: Volume within Lesion #1 had plaque
not been
present / Proximal normal volume immediately proximal to Lesion #1 >< 100%,
mathematically adjusted for the natural vessel tapering. This volumetric
remodeling index
uses the proximal normal volume as the reference standard.
112651
Alternatively, in some embodiments, a method of determining
volumetric remodeling index that does not directly account for natural vessel
tapering can
be calculated by Volume within Lesion #1 had plaque not been present /
((Proximal normal
volume immediately proximal to Lesion #1 + Distal normal volume immediately
distal to
Lesion #1)) / 2 in order to account for the natural tapering.
[1266]
Further, in some embodiments, with the ability to evaluate coronary
vessels in 3D, along with the ability to determine the hypothetically-normal
boundaries of
the vessel wall even in the presence of plaque, the systems, methods, and
devices described
herein can be configured to either measure (in the absence of plaque) or
calculate the
normal coronary vessel blood volume.
112671
For example, in some embodiments, this coronary vessel blood volume
can be assessed by one or more of the following: (1) Total coronary volume
(which
represents the total volume in all coronary arteries and branches); (2)
Territory- or Artery-
specific volume, or % fractional blood volume (which represents the volume in
a specific
artery or branch); (3) Segment-specific volume (which represents the volume in
a specific
coronary segment, of which there are generally considered 18 segments); and/or
within-
artery % fractional blood volume (which represents the volume in a portion of
a vessel or
branch, i.e., in the region of the artery before a lesion, in the region of
the artery at the site
of a lesion, in the region of the artery after a lesion, etc.).
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112681
Figure 24K illustrates an embodiment(s) of coronary vessel blood
volume assessment based on total coronary volume.
Figure 24L illustrates an
embodiment(s) of coronary vessel blood volume assessment based on territory or
artery-
specific volume. For example, in the illustrated embodiment, the right the
right coronary
artery territory volume would be the volume within #1, #2, #3, #4, and #5,
while the right
coronary artery volume would be the volume within #1, 42, and #3. As an
example of
segment-specific volume-based assessment of coronary vessel blood volume, a
segment-
specific volume (e.g., mid-right coronary artery) can be the volume in #2.
Figure 24M
illustrates an embodiment(s) of coronary vessel blood volume assessment based
on within-
artery % fractional blood volume, where the proximal and distal regions
comprise portions
of the artery fractional blood volume.
112691
Numerous advantages exist for assessing fractional blood volume. In
some embodiments, because this method allows for determination of coronary
volume
hypothetically-normal boundaries of the vessel wall even in the presence of
plaque, these
approaches allow for calculation of the % blood volume conferring potential
risk to
myocardium¨comes the ability to either measure (in the absence of plaque) or
calculate
the normal coronary vessel blood volume. Figure 24N illustrates an
embodiment(s) of
assessment of coronary vessel blood volume.
112701
In some embodiments, based on one or more metrics described above,
as well as the ability to determine the hypothetically normal boundaries of
the vessel, the
systems, devices, and methods described herein can be configured to determine
the
ischemia-causing nature of a vessel by a number of different methods.
112711
In particular, in some embodiments, the system can be configured to
determine % vessel volume stenosis, for example by: Measured lumen volume /
Hypothetically normal vessel volume x 100%. This is depicted in Figure 240.
112721
In some embodiments, the system can be configured to determine
pressure difference across a lesion using hypothetically normal artery,
continuity equation
and naturally occurring coronary flow rate ranges and/or other physiologic
parameters.
This is illustrated in Figure 24P. In the embodiment illustrated in Figure
24P, there is a
plaque that extends into the lumen and narrows the lumen (at the maximum
narrowing, it
is RO). in some embodiments, the system can compare RO to R-1, R-2, R-3 or any
cross-
section before the lesion.
112731
In some embodiments, using this comparison, the system can apply the
continuity equation either using actual measurements (e.g., at lines in Figure
24P) or the
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hypothetically normal diameter of the vessel. The continuity equation applied
to the
coronary arteries is illustrated in Figure 24Q.
[1274]
As illustrated in Figure 24Q, in some embodiments, the system, by using
imaging (CT, MR1, etc.), can be configured to determine the cross-sectional
area of artery
at a defined point before the site of maximum narrowing (Al) and the cross-
sectional area
of artery at the site of maximum narrowing (A2) with high accuracy. However,
in some
embodiments, velocity and velocity time integral are unknown. Thus, in some
embodiments, the velocity time integral (VTI) at a defined point before the
site of
maximum narrowing (V1) and the VTI at a defined point after the site of
maximum
narrowing (V2) are provided, for example in categorical outputs based upon
what has been
empirically measured for people at rest and during exertion (mild, moderate
and extreme).
[1275]
As a non-limiting example, at rest, the total coronary blood flow can be
about ¨250 ml/min (-0.8 ml/min*g of heart muscle), which represents ¨5% of
cardiac
output. At increasing levels of exertion. the coronary blood flow can increase
up to 5 times
its amount (-1250 ml/min). Thus, in some embodiments, the system can
categorize the
flow into about 250 ml/min, about 250-500 ml/min, about 500-750 ml/min, about
750-1000
ml/min, and/or about 1250 ml/min. Other categorizations can exist, and these
numbers can
be reported in continuous, categorical, and/or binary expressions. Further,
based upon the
observations of blood flow, these relationships may not necessarily be linear,
and can be
transformed by mathematical operations (such as log transform, quadratic
transform, etc.).
[1276]
Further, in some embodiments, other factors can be calculated based
upon ranges, binary expressions, and/or continuous values, such as for example
heart rate,
aortic blood pressure and downstream myocardial resistance, arterial wall /
plaque
resistance, blood viscosity, and/or the like. Empirical measurements of fluid
behavior in
these differing conditions can allow for putting together a titratable input
for the continuity
equation.
[1277]
Further, in some embodiments, because imaging allows for evaluation
of the artery across the entire cardiac cycle, measured (or assumed) coronary
vasodilation
can allow for time-averaged Al and A2 measurements.
[1278]
As such, in some embodiments, the system can be configured to utilize
one or more of the following equations: (1) Q = area x velocity A site of
maximum
obstruction (across a range of flows observed in empirical measurements); and
(2) Q = area
velocity A, site proximal to maximum obstruction (across a range of flows
observed in
empirical measurements).
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112791
From the assumed flows and measured areas, in some embodiments, the
system can then back-calculate the velocity. Then, the system can apply the
simplified or
full Bernoulli's equations to equal: Pressure change = 4(V2-V1)2. From this,
in some
embodiments, the system can calculate the pressure drop across a lesion and,
of equal
import, can assess this pressure change across physiologically-realistic
parameters that a
patient will face in real life (e.g., rest, mild/moderate/extreme exertion).
112801
Further, in some embodiments, the system can apply a volumetric
continuity equation to account for a volume of blood before and after a lesion
narrowing,
such as for example: (1) Q = volume >< velocity ',d) site of maximum
obstruction (across a
range of flows observed in empirical measurements); and (2) Q = volume ><
velocity @ site
proximal to maximum obstruction (across a range of flows observed in empirical
measurements). From the assumed flows and measured volumes, in some
embodiments,
the system can then back-calculate the velocity and, if assuming or measuring
heart rate,
the system can then back-calculate the velocity time integral.
112811
Figure 24R is a flowchart illustrating an overview of an example
embodiment(s) of a method for determining volumetric stenosis and/or
volumetric vascular
remodeling. As illustrated in Figure 24R, in some embodiments, at block 2402
the system
is configured to access one or more medical images, for example from a medical
image
database 100. The one or more medical images can be obtained using any one or
more of
the imaging modalities discussed herein. In some embodiments, at block 2404,
the system
can be configured to identify one or more segments of arteries and/or regions
of plaque by
analyzing the medical image.
112821
In some embodiments, the system at block 2406 can be configured to
determine a lumen wall boundary in the one or more segments where plaque is
present. In
some embodiments, the system at block 2406 can be configured to determine a
hypothetical
normal artery boundary if plaque were not present. In some embodiments, the
system at
block 2408 can be configured to quantify the lumen volume with plaque and/or a
hypothetical normal vessel volume had plaque not been present. In some
embodiments,
using the foregoing, the system at block 2410 can be configured to determine
volumetric
stenosis of the one or more segments, taking into account tapering and true
assessment of
the vessel morphology based on image analysis.
112831
In some embodiments, the system at block 2412 can be configured to
quantify the volume of one or mor eregions of plaque. For example, in some
embodiments,
the system can be configured to quantify for a segment or lesion the total
volume of plaque,
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volume of plaque inside the hypothetical normal artery boundary, volume of
plaque outside
the hypothetical normal artery boundary, and/or the like. In some embodiments,
the system
at block 2414 can be configured to utilize the foregoing to determine a
volumetric
remodeling index. For example, in some embodiments, the system can be
configured to
determine a volumetric remodeling index by dividing the sum of the
hypothetical normal
vessel volume and the plaque volume outside the hypothetical normal artery
boundary by
the hypothetical normal vessel volume.
112841
In some embodiments, the system at block 2416 can be configured to
determine a risk of CAD for the subject, for example based on one or more of
the
determined volumetric stenosis and/or volumetric vascular remodeling index.
112851
Figure 24S is a flowchart illustrating an overview of an example
embodiment(s) of a method for determining ischemia. As illustrated in Figure
24S, in some
embodiments, the system can access a medical image at block 2402, identify one
or more
segments of arteries and/or region of plaque at block 2404, and/or determine
the lumen wall
boundary while taking into account the present plaque and/or a hypothetical
normal artery
boundary if plaque were not present at block 2406. In some embodiments, at
block 2418,
the system can be configured to quantify a proximal and/or distal cross-
sectional area
and/or volume along an artery. For example, in some embodiments, the system
can be
configured to quantify a proximal cross-sectional area and/or volume at a
lesion that is
proximal to a lesion of interest. In some embodiments, the lesion of interest
can include
plaque and/or a maximum narrowing of a vessel. In some embodiments, the system
can be
configured to quantify a distal cross-sectional area and/or volume of the
lesion of interest.
112861
In some embodiments, the system can be configured to apply an
assumed velocity of blood flow at the proximal section at block 2420. In some
embodiments, the assumed velocity of blood flow can be prestored or
predetermined, for
example based on different states, such as at rest, during mild exertion,
during moderate
exertion, during extreme exertion, and/or the like.
112871
In some embodiments, at block 2422, the system can be configured to
quantify the velocity of blood flow at the distal section, for example at the
lesion that
includes plaque and/or maximum narrowing of the vessel. In some embodiments,
the
system is configured to quantify the velocity of blood flow at the distal
section by utilizing
the continuity equation. In some embodiments, the system is configured to
quantify the
velocity of blood flow at the distal section by utilizing one or more of the
quantified
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proximal cross-sectional area or volume, quantified distal cross-sectional
area or volume,
and/or assumed velocity of blood flow at the proximal section.
112881
In some embodiments, the system at block 2424 is configured to
determine a change in pressure between the proximal and distal sections, for
example based
on the assumed velocity of blood flow at the proximal section, the quantified
velocity of
blood flow at the distal section, the cross-sectional area at the proximal
section, and/or the
cross-sectional area at the distal section. In some embodiments, at block
2426, the system
is configured to determine a velocity time integral (VTI) at the distal
section, for example
based on the quantified velocity of blood flow at the distal section. In some
embodiments,
the system at block 2428 is configured to determine ischemia for the subject,
for example
based on one or more of the determined change in pressure between the proximal
and distal
sections and/or VTI at the distal section.
Determining myocardial infarction risk and severity
112891
The systems and methods described herein can also be used for
determining myocardial infarction risk and severity from image-based
quantification and
characterization of coronary atherosclerosis. For example, various embodiments
described
herein relate to systems, methods, and devices for determining patient-
specific indications
of myocardial infarction risk and severity risk from image-based
quantification and
characterization of coronary atherosclerosis burden, type, and/or rate of
progression.
112901
One innovation includes a computer-implemented method of
determining a myocardial risk factor via an algorithm-based medical imaging
analysis is
provided, the method comprising performing a comprehensive atherosclerosis and
vascular
morphology characterization of a portion of the coronary vasculature of a
patient using
information extracted from medical images of the portion of the coronary
vasculature of
the patient, performing a characterization of the myocardium of the patient
using
information extracted from medical images of the myocardium of the patient,
correlating
the characterized vascular morphology of the patient with the characterized
myocardium
of the patient, and determining a myocardial risk factor indicative of a
degree of myocardial
risk from at least one atherosclerotic lesion.
112911
Performing the comprehensive atherosclerosis and vascular morphology
characterization of the portion of the coronary vasculature of the patient can
include
identifying the location of the at least one atherosclerotic lesion.
Determining the
myocardial risk factor indicative of the degree of myocardial risk from the at
least one
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atherosclerotic lesion can include determining a percentage of the myocardium
at risk from
the at least one atherosclerotic lesion. Determining a percentage of the
myocardium at risk
from the at least one atherosclerotic lesion can include determining the
percentage of the
myocardium subtended by the at least one atherosclerotic lesion. Determining
the
myocardial risk factor indicative of the degree of myocardial risk from the at
least one
atherosclerotic lesion can include determining an indicator reflective of a
likelihood that
the at least one atherosclerotic lesion will contribute to a myocardial
infarction.
[1292]
Performing the characterization of the myocardium of the patient can
include performing a characterization of the left ventricular myocardium of
the patient.
The method can further include correlating the determined myocardial risk
factor to at least
one risk of a severe clinical event. The method can further include comparing
the
determined myocardial risk factor to a second myocardial risk factor
indicative of a degree
of myocardial risk to the patient at a previous point in time.
[1293]
Another innovation includes a computer-implemented method of
determining a segmental myocardial risk factor via an algorithm-based medical
imaging
analysis is provided, the method comprising characterizing vascular morphology
of the
coronary vasculature of a patient using information extracted from medical
images of the
coronary vasculature of the patient, identifying at least one atherosclerotic
lesion within the
coronary vasculature of the patient using information extracted from medical
images of the
portion of the coronary vasculature of the patient, characterizing a plurality
of segments of
the myocardium of the patient to generate a segmented myocardial
characterization using
information extracted from medical images of the myocardium of the patient,
correlating
the characterized vascular morphology of the patient with the segmented
myocardial
characterization of the patient, and generating an indicator of segmented
myocardial risk
from the at least one atherosclerotic lesion.
[1294]
Generating an indicator of segmented myocardial risk can include
generating a discrete indicator of myocardial risk for at least a subset of
the plurality of
segments of the myocardium. Generating an indicator of segmented myocardial
risk can
include generating a discrete indicator of myocardial risk for each of the
plurality of
segments of the myocardium.
[1295]
Correlating the characterized vascular morphology of the patient with
the segmented myocardial characterization of the patient can include
identifying for each
of the myocardial segments a coronary artery primarily responsible for
supplying
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oxygenated blood to that myocardial segment. The segmented myocardial
characterization
can be segmented into 17 segments according to a standard AHA 17-segment
model.
112961
In another innovation, a computer-implemented method of determining
a segmental myocardial risk factor via an algorithm-based medical imaging
analysis is
provided, the method comprising applying at least a first algorithm to a first
plurality of
images of the coronary vasculature of a patient obtained using a first imaging
technology
to characterize the vascular morphology of the coronary vasculature of the
patient and to
identify a plurality of atherosclerotic plaque lesions, applying at least a
second algorithm
to a first plurality of images of the myocardium of the patient obtained using
a second
imaging technology to characterize the myocardium of the patient, applying at
least a third
algorithm to relate the characterized vascular morphology of the patient with
the
characterized myocardium of the patient, and calculating a percentage of
subtended
myocardium at risk from at least one of the plurality of identified
atherosclerotic plaque
lesions.
112971
The method can additionally include applying an algorithm to a second
plurality of images of the coronary vasculature of the patient obtained using
a third imaging
technology to characterize the vascular morphology of the coronary vasculature
of the
patient and to identify a plurality of atherosclerotic plaque lesions.
Applying an algorithm
to a second plurality of images of the coronary vasculature of the patient can
include
applying the first algorithm to the second plurality of images of the coronary
vasculature
of the patient The method can additionally include applying an algorithm to a
second
plurality of images of the myocardium of the patient obtained using a third
imaging
technology to characterize the myocardium of the patient.
112981
Applying at least the first algorithm to the first plurality of images of
the
coronary vasculature of a patient obtained using the first imaging technology
can
additionally include determining characteristics of the identified plurality
of atherosclerotic
plaque lesions. The can additionally include determining a risk of the
identified plurality
of atherosclerotic plaque lesions contributing to a myocardial infarction, and
determining
an overall risk indicator based on the determined risk of the identified
plurality of
atherosclerotic plaque lesions contributing to a myocardial infarction and the
calculated
percentage of subtended myocardium at risk from the identified plurality of
atherosclerotic
plaque lesions.
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[1299]
The method can additionally include relating the calculated percentage
of subtended myocardium at risk from at least one of the plurality of
identified
atherosclerotic plaque lesions to a risk of at least one adverse clinical
events.
Overview
1113001
Various embodiments described herein relate to systems, methods, and
devices for determining patient-specific myocardial infarction (MI) risk
indicators from
image-based analysis of arterial atherosclerotic lesion(s).
113011
The heart includes epicardial coronary arteries, vessels which transmit
oxygenated blood from the aorta to the myocardium of the heart. Within these
epicardial
coronary arteries, atherosclerotic lesions can build up due to plaque
accumulation. These
atherosclerotic lesions can erode or rupture, dislodging plaque and leading to
thrombotic
occlusion of a blood vessel at a location distal of the atherosclerotic lesion
location, leading
to a myocardial infarction (MI) or major adverse cardiovascular events (MACE),
al so
known as a heart attack. During a heart attack, flow of oxygenated blood to
the
myocardium is impeded by the thrombotic occlusion of the blood vessel, leading
to
damage, including irreversible damage, of the myocardium.
113021
Myocardial damage may directly impact the ability of the heart muscle
to contract and/or relax normally, a condition which may lead to clinically
manifest heart
failure. Heart failure is a complex syndrome which may affect a patient in a
number of
ways. The quality of life may be impaired, due to shortness of breath or other
symptoms,
and mortality may be accelerated. The contractile function of the heart may be
impaired in
one or more aspects, including reduced ejection fraction, elevated left
ventricular volumes,
left ventricular non-viability, and myocardial stunning, as well as abnormal
heart rhythms,
such as ventricular tachyarrhythmias. Surgical intervention, including
coronary artery
bypass surgery and heart transplants, may be needed, along with other invasive
procedures,
such as stent procedures.
[1303]
The likelihood that a given atherosclerotic lesion may lead to a
myocardial infarction or other major adverse cardiovascular event may be
dependent, at
least in part, on the properties of the lesion, including the nature of the
accumulated plaque.
The presence of fatty plaque buildup can inhibit blood flow therethrough to a
greater extent
than calcified plaque build-up. When an artery contains "good" or stable
plaque, or plaque
comprising hardened calcified content, the lesion may be less likely to result
in a life-
threatening condition such as a myocardial infarction. In contrast,
atherosclerotic lesions
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containing "bad- or unstable plaque or plaque comprising fatty material can be
more likely
to rupture within arteries, releasing the fatty material into the arteries.
Such a fatty material
release in the blood stream can cause inflammation that may result in a blood
clot. A blood
clot in the artery can cause a stoppage of blood flow to the heart tissue,
which can result in
a heart attack or other cardiac event.
113041
Evaluation of the nature of a given atherosclerotic lesion may be used to
make a determination as to whether a lesion contains "high-risk plaque- or
"vulnerable
plaque" which is likely to contribute to a future myocardial infarction.
Although such
predictions are not exact, evaluation of various characteristics of a given
atherosclerotic
lesion may be used to classify the atherosclerotic lesion as being a high-risk
plaque. These
characteristics include, but are not limited to, atherosclerosis burden,
composition, vascular
remodeling, diffuseness, location, direction, and napkin-ring sign, among
other
characteristics. The evaluation may be based on medical imagery indicative of
the
cardiovascular system of a patient.
113051
Various medical imaging processes may be used in the analyses
described herein. In some embodiments, invasive medical imaging may be used to
gather
information regarding a given atherosclerotic lesion. In other embodiments,
however, non-
invasive medical imaging may be used, such as coronary computed tomographic
angiography (CCTA), which allows direct visualization of coronary arteries in
a non-
invasive fashion.
[1306]
In some embodiments, the characterization of atherosclerosis and
vascular morphology may include the analysis of a series of CCTA images or any
other
suitable images, and the generation of a three-dimensional model of a portion
of the
patient's cardiovascular system. This analysis can include the generation of
one or more
quantified measurements of vessels from the raw medical image, such as for
example
diameter, volume, morphology, and/or the like. This analysis may segment the
vessels in
a predetermined manner, or in a dynamic manner, in order to provide more
detailed
overview of the vascular morphology of the patient.
113071
In particular, in some embodiments, the system can be configured to
utilize one or more AI and/or ML algorithms to automatically and/or
dynamically identify
one or more arteries, including for example coronary arteries, although other
portions of a
patient's cardiovascular system may also be generated. In some embodiments,
one or more
AT and/or ML algorithms use a neural network (CNN) that is trained with a set
of medical
images (e.g., CT scans) on which arteries and features (e.g., plaque, lumen,
perivascular
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tissue, and/or vessel walls) have been identified, thereby allowing the Al
and/or ML
algorithm to automatically identify arteries directly from a medical image. In
some
embodiments, the arteries are identified by size and/or location.
[1308]
This analysis can also include the identification and classification of
plaque within the cardiovascular system of the patient. In some embodiments,
the system
can be configured to identify a vessel wall and a lumen wall for each of the
identified
coronary arteries in the medical image. In some embodiments, the system is
then
configured to determine the volume in between the vessel wall and the lumen
wall as
plaque. In some embodiments, the system can be configured to identify regions
of plaque
based on the radiodensity values typically associated with plaque, for example
by setting a
predetermined threshold or range of radiodensity values that are typically
associated with
plaque with or without normalizing using a normalization device.
[1309]
In some embodiments, the characterization of atherosclerosis may
include the generation of one or more quantified measurements from a raw
medical image,
such as for example radiodensity of one or more regions of plaque,
identification of stable
plaque and/or unstable plaque, volumes thereof, surface areas thereof,
geometric shapes,
heterogeneity thereof, and/or the like. Using this plaque identification and
classification,
the overall plaque volume may be determined, as well as the amount of
calcified stable
plaque and the amount of uncalcified plaque. In some embodiments, more
detailed
classification of atherosclerosis than a binary assessment of calcified vs.
non-calcified
plaque may be made. For example, the plaque may be classified ordinally, with
plaque
classified as dense calcified plaque, calcified plaque, fibrous plaque,
fibrofatty plaque,
necrotic core, or admixtures of plaque types. The plaque may also be
classified
continuously, by attenuation density on a scale such as a Hounsfield unit
scale or a similar
classification system.
[1310]
The information which can be obtained in the characterization of
atherosclerosis may be dependent upon the type of imaging being performed. For
example,
when the CCTA images are creating using a single-energy CT process, the
relative material
density of the plaque relative to the surrounding tissue can be determined,
but the absolute
material density may be unknown. In contrast, when the CCTA images are
creating using
a multi-energy CT process, the absolute material density of the plaque and
other
surrounding tissue can be measured.
[1311]
The characterization of atherosclerosis and vascular morphology may
include in particular the identification and classification of atherosclerotic
lesion within the
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cardiovascular system of the patient, and in certain embodiments within the
coronary
arteries of the patient. This may include the calculation or determination of
a binary or
numerical indicator regarding one or more parameters of an atherosclerotic
lesion, based
on the quantified and/or classified atherosclerosis derived from the medical
image. The
system may be configured to calculate such indicators regarding one or more
parameters
of an atherosclerotic lesion using the one or more vascular morphology
parameters and/or
quantified plaque parameters derived from the medical image of a coronary
region of the
patient. In some embodiments, the system is configured to dynamically identify
an
atherosclerotic lesion within an artery, and calculate information regarding
the
atherosclerotic lesion and the adjacent section of the vessel, such as vessel
parameters
including diameter, curvature, local vascular morphology, and the shape of the
vessel wall
and the lumen wall in the area of the atherosclerotic lesion.
Calculation of Myocardial Risk
[1312]
Figure 25A is a flowchart illustrating a process 2500 for determining an
indicator of risk that an atherosclerotic lesion will contribute to a
myocardial infarction or
other major adverse cardiovascular event. At block 2505, a system can access a
plurality
of images indicative of a portion of a cardiovascular system of a patient.
These images can
be, for example, CCTA images or any other suitable images, and can be
generated at a
medical facility. These images can be reflective of, for example a portion of
the
cardiovascular system of a patient including the coronary arteries, and can be
representative
of at least an entire cardiac cycle. In some embodiments, these CCTA images
may be
reflective of the portion of the cardiovascular system of a patient both prior
to and after
exposure of the patient to a vasodilatory substance, such as nitroglycerin or
iodinated
contrast. These CCTA images can be reflective of one or more known physiologic
condition of the patient, such as an at rest state or a hyperemic state.
[1313]
At block 2510, the system can analyze the images to identify at least one
atherosclerotic lesion (e.g., artery abnormalities) within the portion of the
cardiovascular
system of the patient. Atherosclerotic lesions may develop predominantly at
branches,
bends, and bifurcations in the arterial tree. Identifying the at least one
atherosclerotic lesion
within the portion of the cardiovascular system can include determining
information on
characteristics and parameters of the atherosclerotic lesion using any of the
functionality
described herein, for example, information on plaque and it
characteristics/parameters,
lesion size, lesion location, vessel and/or lumen size and shape information,
etc. This
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identification may be, for example, part of a broader characterization of
atherosclerosis and
vascular morphology based on the plurality of images. A characterization of
atherosclerosis
can include the identification of the location, volume and/or type of plaque
throughout the
portion of the cardiovascular system of the patient.
[1314]
At block 2515, the system can apply an algorithm that analyzes
characteristics/parameters of the identified atherosclerotic lesion to
determining an
indicator of risk that an atherosclerotic lesion will contribute to a
myocardial infarction or
other major adverse cardiovascular event. This analysis can include, for
example, any of
atherosclerosis burden, composition, vascular remodeling, diffuseness,
location, direction,
and napkin-ring sign, among other characteristics, as well as any combination
thereof The
napkin-ring sign refers to a rupture-prone plaque in a coronary artery,
comprising a necrotic
core covered by a thin cap fibro-atheroma.
[1315]
In some embodiments, the indicator of risk may be a binary indicator,
and the system may designate one or more analyzed atherosclerotic lesions as
either being
high-risk for a heart attack (myocardial infarction (MI)) or other major
cardiac event, or
not being a high-risk for an MI or other major cardiac event. In other
embodiments, the
indicator may be a numerical indicator providing a more granular indication of
the decree
of risk presented by a given atherosclerotic lesion. For example, a number
from 1.0 (low)
to 10.0 (high), or in another example, from 1(10w) to 100 (high).
[1316]
In some embodiments, multiple analyses may be used, using different
combinations of parameters and/or different weightings of parameters, and
multiple
analyses of the same atherosclerotic lesion may be used in making an aggregate
assessment
of risk. For example, if any of the multiple analyses classify an
atherosclerotic lesion as
being high risk, the atherosclerotic lesion may be designated as high risk out
of caution. In
other embodiments, the indicators of risk from the various analyses may be
averaged or
otherwise combined into an aggregate indicator of risk.
[1317]
While such an analysis may be used to provide a binary or numerical
indication of a risk that a given atherosclerotic lesion may contribute to a
myocardial
infarction or other major adverse cardiovascular event, such an indicator, in
isolation, may
not provide an indication of a level of risk associated with a myocardial
infarction or other
major adverse cardiovascular event which would be caused by that
atherosclerotic lesion.
An important factor in the overall level of risk to the health of a patient
presented by a given
atherosclerotic lesion is the location of that atherosclerotic lesion relative
to the surrounding
portions of the cardiovascular.
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[1318]
Figure 25B is schematic illustration of a human heart, illustrating
certain coronary arteries. The heart muscle 2520 is supplied with oxygenated
blood from
the aorta 2521 by the coronary vasculature, which includes a complex network
of vessels
ranging from large arteries to arterioles, capillaries, venules, veins, etc.
Like all other
tissues in the body, the heart muscle 2520 needs oxygen-rich blood to
function. Also,
oxygen-depleted blood must be carried away. The coronary arteries wrap around
the
outside of the heart muscle 2520. Small branches extend into the heart muscle
2520 to bring
it blood.
[1319]
The coronary arteries include the right coronary artery (RCA) 2525
which extends from the aorta 2521 downward along the right side of the heart
2520, and
the left main coronary artery (LMCA) 2522 which extends from the aorta 2521
downward
on the left side of the heart 2520. The RCA 2525 supplies blood to the right
ventricle, the
right atrium, and the SA (sinoatrial) and AV (atrioventricular) nodes, which
regulate the
heart rhythm. The RCA 2525 divides into smaller branches, including the right
posterior
descending artery and the acute marginal artery. Together with the left
anterior descending
artery 2524, the RCA 2525 helps supply blood to the middle or septum of the
heart.
[1320]
The LMCA 2522 branches into two arteries, the anterior interventricular
branch of the left coronary artery, also known as the left anterior descending
(LAD) artery
2524 and the circumflex branch of the left coronary artery 2523. The LAD
artery 2524
supplies blood to the front of the left side of the heart 2520. The circumflex
branch of the
left coronary artery 2523 encircles the heart muscle. The circumflex branch of
the left
coronary artery 2523 supplies blood to the outer side and back of the heart,
following the
left part of the coronary sulcus, running first to the left and then to the
right, reaching nearly
as far as the posterior longitudinal sulcus.
[1321]
Because the various coronary arteries supply blood to particular regions
of the heart, the impact of an interruption in the amount of oxygenated blood
passing
through a given vessel caused by stenosis or occlusion is dependent upon the
location of
the vessel at which the stenosis or occlusion occurs. A stenosis or occlusion
proximal the
aorta and/or located in a larger vessel can impact a larger percentage of the
heart muscle
2520, and in particular the myocardium, than a stenosis or occlusion distal
the aorta and/or
located in a smaller vessel.
[1322]
In some embodiments, the ischemic impact of a stenosis in a coronary
artery can be evaluated by relating blood flow within the coronary arteries of
a patient to
the corresponding myocardium that the coronary arteries subtend. In such
ischemia
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imaging processes, coronary stenosis can be evaluated to identify regions that
may impede
blood flow within the epicardial coronary arteries, and relate that impediment
to blood flow
to the percentage of myocardium that is at risk of becoming ischemic, or
otherwise
impacted by reduced blood supply.
113231
The evaluation of impacted myocardium can be combined with the
evaluation of the risk that a given lesion may cause a myocardial infarction
or other major
adverse cardiovascular event in order to provide an indictor of the risk to
the broader
cardiac health of a patient posed by a given atherosclerotic lesion. In some
embodiments,
this can be expressed in terms of a percentage of subtended myocardium at risk
(referred
to herein as %SMAR), linking a given coronary atherosclerotic plaque lesion
location
within a coronary artery to the myocardium subtended by the coronary artery
distal of the
lesion location.
113241
Figure 25C is a flowchart illustrating a process 2530 for determining an
indicator of a myocardial risk posed by an atherosclerotic lesion. At block
2531, a system
can access a plurality of images indicative of a portion of a cardiovascular
system of a
patient. These images can be, for example, the result of a contrast-enhanced
CT scan
performed of the patient's heart and cardiac arteries. In other embodiments,
however, these
images may be generated using a wide variety of other imaging techniques,
including but
not limited to ultrasound, magnetic resonance imaging, or nuclear testing. In
addition,
multiple imaging modalities can be used to enhance the analysis, as discussed
in greater
detail herein.
113251
At block 2532, the system can determine a characterization of
atherosclerosis and vascular morphology based on the plurality of accessed
images. The
characterization of the vascular morphology can include, for example, the
automated
extraction and labeling of the coronary arteries, including the various
branches and
segments thereof As in example, this labeling can include the identification
and labeling
of the centerlines of the various vessel segments to facilitate the extraction
and labeling of
the various segments. As in example, this labeling can include the
identification and
labeling of the lumen and vessel walls of the various vessel segments to
facilitate the
extraction and labeling of the various segments. This characterization of the
vascular
morphology provides a patient-specific characterization of the vascular
morphology of the
patient. In particular, it can be used to provide a patient-specific
characterization of the
coronary artery tree.
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113261
The system can also determine a characterization of atherosclerosis
within the coronary vessels. In particular, the characterization of
atherosclerosis can
include the automated identification of atherosclerotic plaque lesions within
the vasculature
of the patient. A number of characteristic parameters of the identified
atherosclerotic
plaque lesions can be automatically calculated by the system, including but
not limited to
their volume, their composition, their remodeling, their location, and their
relation to the
myocardium of the patient. The use, for example, of a contrast enhanced CT
scan allows
the identification by the system of the composition of the various identified
atherosclerotic
plaque lesions, such as by identifying them as primarily fatty plaque build-up
or primarily
calcified plaque build-up, as well as an indication of the density of the
plaque build-up.
Positive remodeling of the surrounding vessel in the location of the
identified
atherosclerotic plaque lesions can also provide an indication of the risk
posed by the
identified atherosclerotic plaque lesions.
113271
Although illustrated as a single block 2532, the characterization of the
vascular morphology can be performed in separate steps and in any suitable
order. For
example, in some embodiments, further characterization of the vascular
morphology may
be performed based at least in part on the characterization of
atherosclerosis, with
additional analysis applied to portions of the vasculature of the patient
affected by the
identified atherosclerotic plaque lesions.
113281
At block 2533, the system can determine a characterization of the
myocardium of the patient based on a plurality of accessed images. In some
embodiments,
the myocardium of the patient may be characterized using one or more of the
same accessed
images used for the characterization of atherosclerosis and vascular
morphology, while in
other embodiments, medical imagery obtained via a different imaging technique
may be
used in the characterization of the myocardium. In some embodiments, a cardiac
MM or
other imaging technique may be used to generate images used for
characterization of the
myocardium.
113291
In some embodiments, the characterization of the myocardium may be
a characterization of only a portion of the myocardium of the patient, or may
be a
characterization which focuses primarily on certain regions of the myocardium,
such as the
left ventricular myocardium, due to the increased thickness of the myocardium
in the left
ventricle. This characterization may include, for example, the relative and
absolute size of
the ventricular mass, as vell as the overall shape of the ventricular mass.
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113301
At block 2534, the system relates the characterization of the vascular
morphology to the characterization of the myocardium to provide a patient-
specific
characterization of the relationship between the patient-specific vascular
morphology
characterization and the patient specific myocardium characterization. Because
there can
be significant differences between patients in terms of the blood supply from
specific
coronary arteries to various portions of the myocardium, the relation of the
patient-specific
vascular morphology characterization to the characterization of the myocardium
can be
used to more accurately predict the impact on the myocardium of an occlusion
or other
stenosis at a given location within the patient-specific vasculature.
113311
This relation between the patient-specific vascular morphology
characterization and the patient specific myocardium characterization can
include relating
the identified atherosclerotic plaque lesions within the vasculature of the
patient to the
characterization of the myocardium. The relation can include, for example, one
or more
atherosclerosis metrics in this relation, including the volume and
composition, of the
atherosclerotic plaque lesions, as well as the percent atheroma volume, the
percentage of
total vessel wall occupied by the atherosclerotic plaque. The remodeling of
the surrounding
vessel wall may also be taken into account in this analysis.
113321
At block 2535, the system determines an indicator of the amount of the
myocardium at risk for a given atherosclerotic plaque lesion. This indicator
may be, for
example, a measure of the subtended myocardium at risk from that
atherosclerotic plaque
lesion. The myocardium at risk may be calculated or estimated based on the
percentage of
the myocardium that is subtended by the coronary artery at and distal the
point of the
atherosclerotic plaque lesion. In other embodiments, this indicator may be a
binary or
numerical indicator which may be based on the percentage of the subtended
myocardium
at risk, but may also take into account other factors, such as a likelihood
that a given
atherosclerotic plaque lesion will lead to an MI or similar event. By
determining an
indicator based at least in part on the subtended myocardium percentage, a
broader
indication of the risk to a patient's cardiovascular health can be provided.
113331
The use of such an indicator allows further tailoring of patient diagnosis
and treatment based upon a patient-specific indication of the degree of risk
posed by an MI
or other major cardiac event caused by a given atherosclerotic lesion. If a
given
atherosclerotic lesion may represent a high risk to result in an MI or other
major cardiac
event, but only a small percentage of the myocardium, such as 2% of the
myocardium (or
e.g., less than 5%), is subtended by the lesion and at risk, less drastic
medical treatment,
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such as medical therapy, may be prescribed to the patient, rather than
invasive percutaneous
procedures such as s tent placement or bypass surgery. In contrast, if a given
atherosclerotic
lesion subtends a comparatively high percentage of the myocardium, such as 20%
of the
myocardium (or e.g., more than 20%), percutaneous intervention to seal or
bypass the
legion may be prescribed, as the intervention would be expected to result in a
significant
reduction of risk of adverse consequences associated with an MI or other
severe event. This
may be the case even when the risk of such an MI or other severe event is
comparatively
low, due to the danger to a substantial percentage of the myocardium posed by
that
atherosclerotic lesion.
113341
In some embodiments, the characterization of the myocardium may
include a segmented analysis of specific segments of the myocardium. Figure
25D is a
flowchart illustrating a process 2540 for determining an indicator of a
segmental
myocardial risk posed by an atherosclerotic lesion. At block 2541, a system
can access a
plurality of images indicative of a portion of a cardiovascular system of a
patient. These
images can be, for example, the result of a contrast-enhanced CT scan
performed of the
patient's heart and cardiac arteries, but may also include images generated by
another
imaging technique.
113351
At block 2542, the system can determine a characterization of
atherosclerosis and vascular morphology based on the plurality of accessed
images. The
characterization of the vascular morphology can include, for example, the
automated
extraction and labeling of the coronary arteries, including the various
branches and
segments thereof, as well as the automatic identification of atherosclerotic
plaque lesions
within the vascular morphology.
113361
At block 2543, the system can determine a characterization of one or
more segments of the myocardium of the patient based on a plurality of
accessed images.
In some embodiments, the myocardium of the patient may be characterized using
the same
accessed images used for the characterization of atherosclerosis and vascular
morphology,
while in other embodiments, medical imagery obtained via a different imaging
technique
may be used in the characterization of the myocardium segments, such as a
cardiac MR1 or
intracardiac echocardiography.
[1337]
In some embodiments, the myocardium may be segmented according to
a standard AHA 17-segment model. The AMA 17-segment model divides the left
ventricle
vertically into a basal section, a mid-cavity section, and an apical section,
each of which is
radially subdivided into additional segments. The basal segment is divided
into six radial
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segments, the basal anterior, basal anteroseptal, basal inferoseptal, basal
inferior, basal
inferolateral, and basal anterolateral. The mid-cavity is similarly divided
into six radial
segments, the mid-anterior, mid-anteroseptal, mid-inferoseptal, mid inferior,
mid-
inferolateral, and mid-anterolateral. The tapered apical segment is divided
into four radial
segments, the apical anterior, apical septal, apical inferior, and apical
lateral. The apical
cap, or apex, is analyzed as a single contiguous segment. The AHA 17-segment
model is
one example of a segmentation model which can be used to characterize the
myocardium,
although any other suitable segmentation model may also be used.
113381
Due to the symmetrical radial segmentation, segmental characterization
of the myocardium according to the AHA 17-segment model can provide a
reproducible
segmentation which can be used to monitor changes in the myocardium of a
patient over
time, compare the myocardial characteristics in various states for a given
patent, and
compare patients to one another. The regular segmentation can also facilitate
the analysis
of prior myocardial characterizations, even if not generated using the same
system.
113391
Under the standard AHA model, certain segments of the myocardium
can be considered to generally be provided with blood by a specific coronary
artery of the
left anterior descending artery, right coronary artery, and left circumflex
artery, with a
larger percentage of the segments being considered to be provided with blood
by the left
anterior descending artery. For example, occlusion of the left anterior
descending is often
called the widow-maker infarction, due to the severe impact it can have on the
operation of
the heart However, there can be significant variation on a patient-by-patient
basis due to
the specific cardiovascular anatomy of each patient. For example, the apex
segment can
be provided with blood by any of the left anterior descending, right coronary
artery, and
left circumflex artery. Other segments can be primarily provided with
oxygenated blood by
different coronary arteries in different patients.
113401
In other embodiments, alternative segmentation patterns may be used,
and in some embodiments, the myocardium may be dynamically segmented for the
purposes of characterization. Such dynamic segmentation may, for example, take
into
account the patient-specific vasculature characterization to identify segments
of the
myocardium within which a given vessel is likely to supply the majority of the
oxygenated
blood. Such dynamic segmentation can also be used as part of an iterative
process once the
vasculature characterization is related to an initial myocardial
characterization.
113411
At block 2544, the system relates the characterization of the vascular
morphology to the segmented characterization of the myocardium to provide a
patient-
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specific characterization of the relationship between the patient-specific
vascular
morphology characterization and the patient-specific characterization of at
least one
segment of the myocardium. In some embodiments, the characterization of all
segments of
the myocardium are related to the characterization of the vascular morphology.
By
providing a patient-specific relation of the characterization of the vascular
morphology to
the segmented characterization of the myocardium, the system may be able to
more
accurately model the impacted regions of the myocardium of a given patient
than would be
possible using a standardized association between the myocardial segments and
the
coronary vessels.
113421
At block 2545, the system determines an indicator of the segmental
myocardial risk for a given atherosclerotic plaque lesion. In some
embodiments, the
indicator of the segmental myocardial risk may include an identification of
the myocardial
segments which are at least partially subtended by the atherosclerotic plaque
lesion, and at
risk from an MI or other severe cardiac event caused by the atherosclerotic
plaque lesion.
In some embodiments, a percentage of subtended myocardium at risk (e.g.,
"%SMAR-) for
each of the analyzed myocardial segments may be generated, which may provide a
more
precise indication of the risks posed by a given atherosclerotic plaque
lesion.
113431
In addition to or in place of risk indicators relating to the risks posed
by
given atherosclerotic plaque lesions, overall risk factors may also be
determined which are
indicative of the risks posed by a plurality of atherosclerotic plaque
lesions, or by all
identified atherosclerotic plaque lesions, in some embodiments, such an
overall risk factor
may include a cumulative %SMAR value for all identified atherosclerotic plaque
lesions.
In some embodiments, risk indicators associated with the various identified
atherosclerotic
plaque lesions may be weighted or otherwise used in the calculation of a
cumulative risk
indicator.
113441
In some embodiments, the %SMAR or other risk indicator based thereon
may be related to a risk of adverse clinical events. Figure 25E is a flowchart
illustrating a
process 2550 for determining a risk of adverse clinical events caused by an
atherosclerotic
lesion. At block 2551, a system determines characterizations of
atherosclerosis, vascular
morphology, and myocardium of a patient based on one or more pluralities of
accessed
images. The characterization of atherosclerosis can include the identification
of at least one
atherosclerotic lesion within the vasculature of a patient. The
characterization of the
myocardium may include the characterization of discrete sections of the
myocardium.
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113451
At block 2552, the system correlates the characterization of the vascular
morphology to the characterization of the myocardium to provide a patient-
specific
characterization of the relationship between the vascular morphology
characterization and
the myocardial characterization.
113461
At block 2553, the system calculates a percentage of myocardium at risk
from at least one atherosclerotic plaque lesion. In some embodiments, the
calculated
percentage is reflective of the percentage of the entire myocardium at risk.
In some
embodiments, the calculated percentage is reflective of the percentage of one
or more
segments of the myocardium.
113471
At block 2554, the system can relate the calculated percentage of
myocardium at risk to a risk of one or more adverse clinical events. In some
embodiments,
the risk may be calculated for each of a plurality of adverse clinical events.
In some
embodiments, the adverse clinical events may include reductions in quality of
life, such as
shortness of breath. In some embodiments, these adverse clinical events may
include severe
clinical events such as accelerated mortality, a need for percutaneous
coronary
revascularization such as a stent procedure, or a need for heart transplant or
coronary artery
bypass surgery. In some embodiments, these adverse clinical effects may relate
to reduced
contractile function, such as low ejection fraction, elevated left ventricular
volumes, left
ventricular non-viability, and myocardial stunning. In some embodiments, these
adverse
clinical events may include abnormal heart rhythms such as ventricular
tachyarrhythmias.
113481
In some embodiments, a risk indicator based on percentage of
myocardium at risk may be reevaluated after some time has elapsed, or after
treatment has
been carried out. Figure 25F is a flowchart illustrating a process 2560 for
updating a risk
of adverse clinical events caused by an atherosclerotic lesion. At block 2561,
a system
accesses information indicative of the state of a portion of a cardiovascular
system of a
patient at a first point in time, as well as a plurality of images indicative
of the state of the
portion of the cardiovascular system of the patient at a second point in time
after the first
point in time. In some embodiments, the second point in time may be a recent
point in time,
such that the reevaluated risk indicator will be indicative of the risk of the
patient at the
current point in time.
[1349]
In some embodiments, the information indicative of the state of the
portion of the cardiovascular system of the patient at the first point in time
can include a
previously calculated risk indicator. In such embodiments, the in other
embodiments, the
information indicative of the state of the portion of the cardiovascular
system of the patient
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at the first point in time can include a plurality of images indicative of the
state of the
portion of the cardiovascular system of the patient at the first point in
time, and a risk factor
indicative of the state of the portion of the cardiovascular system of the
patient at the first
point in time can be determined at the same time as the updated risk factor
reflective of the
state of the patient at the second point in time.
113501 At block 2562, the system determines characterizations of
atherosclerosis, vascular morphology, and myocardium of the patient based on
the plurality
of accessed images indicative of the state of the patient at the second point
in time. If the
information indicative of the state of the portion of the cardiovascular
system of the patient
at the first point in time includes a plurality of images indicative of the
state of the portion
of the cardiovascular system of the patient at the first point in time, the
system may also
determine characterizations of atherosclerosis, vascular morphology, and
myocardium of
the patient based on the plurality of accessed images indicative of the state
of the patient at
the first point in time. In an embodiment in which the system uses an Al or ML
algorithm
to determine these characterizations, redetermination of the characteristics
of the patient at
the first point in time can ensure consistency between these determinations,
in the event
that the AI or ML algorithm has been updated or otherwise altered, such as due
to the
analysis of additional data, in the intervening time.
113511
At block 2563, the system correlates the characterization of the vascular
morphology to the characterization of the myocardium to provide a patient-
specific
characterization of the relationship between the vascular morphology
characterization and
the myocardial characterization at the second point in time. If the
information indicative of
the state of the portion of the cardiovascular system of the patient at the
first point in time
includes a plurality of images indicative of the state of the portion of the
cardiovascular
system of the patient at the first point in time, the system may also
correlate the
characterization of the vascular morphology to the characterization of the
myocardium to
provide a patient-specific characterization of the relationship between the
vascular
morphology characterization and the myocardial characterization at the first
point in time.
113521
In an embodiment in which two such correlations are made at
substantially the same point in time, or in which the information indicative
of the state of
the portion of the cardiovascular system of the patient at the first point in
time includes an
indication of a previously determined correlation, the system may compare the
correlation
at the first point in time to the correlation at the second point in time, to
determine if the
vasculature or myocardium of the patent has significantly changed if If so,
additional
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analysis regarding the cause for such a change may be performed, either by the
system
itself, or by a clinical practitioner evaluating the patient who can be
alerted to this
discrepancy by the system.
113531
At block 2564, the system calculates a percentage of myocardium at risk
from at least one atherosclerotic plaque lesion at the second point in time.
If the information
indicative of the state of the portion of the cardiovascular system of the
patient at the first
point in time includes a plurality of images indicative of the state of the
portion of the
cardiovascular system of the patient at the first point in time, the system
may also calculate
a percentage of myocardium at risk from at least one atherosclerotic plaque
lesion at the
first point in time.
113541
At block 2565, the system compares the percentage of the myocardium
at risk at the first point in time to the calculated percentage of the
myocardium at risk at the
second point of time. In some embodiments, this comparison may provide a
practitioner
with information regarding the efficacy of an intervening treatment of the
patient, such as
a stent procedure or the use of statins which can solidify previously fatty
plaque deposits.
In some embodiments, this comparison may provide a practitioner with
information
regarding an updated prognosis for the patient based upon more recent
characterizations of
the atherosclerosis, vascular morphology, and/or myocardium of the patient.
113551
In some embodiments, the process may proceed to an additional step
where the risk of one or more adverse clinical events can be updated based
upon the updated
calculated percentage of the myocardium subtended by a given lesion or a
plurality of
lesions. In addition, where prior imaging information is available, images
from different
points in time may be fused together or otherwise used to generate a composite
image or
other representation indicative of changes over time. These changes can in
some
embodiments be due to interventions such as medication, exercise, or other
medical
procedures.
113561
In some embodiments, as discussed herein, different imaging techniques
may be used to characterize the atherosclerosis and vascular morphology than
those used
to characterize the myocardium of the patient. However, in other embodiments,
multiple
imaging techniques may be used in any of these individual characterizations,
as well. For
example, the system may analyze CT imagery to extract information indicative
of
atherosclerosis, while the system may analyzed information extracted from
positron
emission tomography (PET) imagery to extract information indicative of
inflammation. By
synthesizing information from multiple imaging modalities, the disclosed
technology can
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be used to enhance the phenotypic richness of the particular potion of the
body being
characterized.
113571
Although described herein primarily in the context of imaging and
analysis of the coronary arteries, the systems, methods and devices of the
disclosed
technology can also be used in the context of other portions of the body,
including other
arterial beds. For example, the disclosed technology can be used with
ultrasound imagery
of the carotid arterial bed, the aorta, and the arterial beds of the lower
extremities, among
other portions of the cardiovascular system of the patient. The disclosed
technology may
be used with any suitable imaging technology or combination of imaging
technologies,
including but not limited to CT, ultrasound, MRI, PET, and nuclear testing.
Computer System
113581
In some embodiments, the systems, processes, and methods described
herein are implemented using a computing system, such as the one illustrated
in Figure
25G. The example computer system 2572 is in communication with one or more
computing
systems 2590 and/or one or more data sources 2592 via one or more networks
2586. While
Figure 25G illustrates an embodiment of a computing system 2572, it is
recognized that the
functionality provided for in the components and modules of computer system
2572 can be
combined into fewer components and modules, or further separated into
additional
components and modules.
113591
The computer system 2572 can comprise a Patient-Specific Myocardial
Risk Determination Module 2584 that carries out the functions, methods, acts,
and/or
processes described herein. The patient-Specific Myocardial Risk Determination
Module
2584 is executed on the computer system 2572 by a central processing unit
(e.g., one or
more hardware processors) 2576 discussed further below.
113601
In general the word "module,- as used herein, refers to logic embodied
in hardware or firmware or to a collection of software instructions, having
entry and exit
points. Modules are written in a program language, such as JAVA, C, or C++, or
the like.
Software modules can be compiled or linked into an executable program,
installed in a
dynamic link library, or can be written in an interpreted language such as
BASIC, PERL,
LAU, PHP or Python and any such languages. Software modules can be called from
other
modules or from themselves, and/or can be invoked in response to detected
events or
interruptions. Modules implemented in hardware include connected logic units
such as
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gates and flip-flops, and/or can include programmable units, such as
programmable gate
arrays or processors.
[1361]
Generally, the modules described herein refer to logical modules that
can be combined with other modules or divided into sub-modules despite their
physical
organization or storage. The modules are executed by one or more computing
systems, and
can be stored on or within any suitable computer readable medium, or
implemented in-
whole or in-part within special designed hardware or firmware. Not all
calculations,
analysis, and/or optimization require the use of computer systems, though any
of the above-
described methods, calculations, processes, or analyses can be facilitated
through the use
of computers. Further, in some embodiments, process blocks described herein
can be
altered, rearranged, combined, and/or omitted.
[1362]
The computer system 2572 includes one or more processing units (CPU)
706, which can comprise a microprocessor. The computer system 2572 further
includes a
physical memory 2580, such as random access memory (RAM) for temporary storage
of
information, a read only memory (ROM) for permanent storage of information,
and a mass
storage device 2574, such as a backing store, hard drive, rotating magnetic
disks, solid state
disks (S SD), flash memory, phase-change memory (PCM), 3D XPoint memory,
diskette,
or optical media storage device. Alternatively, the mass storage device can be
implemented
in an array of servers. Typically, the components of the computer system 2572
are
connected to the computer using a standards based bus system. The bus system
can be
implemented using various protocols, such as Peripheral Component Interconnect
(PCI),
Micro Channel, SCSI, Industrial Standard Architecture (ISA) and Extended ISA
(EISA)
architectures.
113631
The computer system 2572 includes one or more input/output (I/O)
devices and interfaces 2582, such as a keyboard, mouse, touch pad, and
printer. The I/O
devices and interfaces 2582 can include one or more display devices, such as a
monitor,
that allows the visual presentation of data to a user. More particularly, a
display device
provides for the presentation of GUIs as application software data, and multi-
media
presentations, for example. The I/O devices and interfaces 2582 can also
provide a
communications interface to various external devices. The computer system 2572
can
comprise one or more multi-media devices 2578, such as speakers, video cards,
graphics
accelerators, and microphones, for example.
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Computing System Device / Operating System
113641
The computer system 2572 can run on a variety of computing devices,
such as a server, a Windows server, a Structure Query Language server, a Unix
Server, a
personal computer, a laptop computer, and so forth. In other embodiments, the
computer
system 2572 can run on a cluster computer system, a mainframe computer system
and/or
other computing system suitable for controlling and/or communicating with
large
databases, performing high volume transaction processing, and generating
reports from
large databases. The computing system 2572 is generally controlled and
coordinated by an
operating system software, such as z/OS, Windows, Linux, UNIX, BSD, PHP,
SunOS,
Solaris, MacOS, ICloud services or other compatible operating systems,
including
proprietary operating systems. Operating systems control and schedule computer
processes
for execution, perform memory management, provide file system, networking, and
I/O
services, and provide a user interface, such as a graphical user interface
(GUI), among other
things.
Network
113651
The computer system 2572 illustrated in Figure 25G is coupled to a
network 2588, such as a LAN, WAN, or the Internet via a communication link
2586 (wired,
wireless, or a combination thereof). Network 2588 communicates with various
computing
devices and/or other electronic devices. Network 2588 is in communication with
one or
more computing systems 2590 and one or more data sources 2592. The Patient-
Specific
Myocardial Risk Determination Module 2584 can access or can be accessed by
computing
systems 2590 and/or data sources 2592 through a web-enabled user access point.
Connections can be a direct physical connection, a virtual connection, and
other connection
type. The web-enabled user access point can comprise a browser module that
uses text,
graphics, audio, video, and other media to present data and to allow
interaction with data
via the network 2588.
[1366]
The output module can be implemented as a combination of an all-points
addressable display such as a cathode ray tube (CRT), a liquid crystal display
(LCD), a
plasma display, or other types and/or combinations of displays. The output
module can be
implemented to communicate with input devices 2582 and they also include
software with
the appropriate interfaces which allow a user to access data through the use
of stylized
screen elements, such as menus, windows, dialogue boxes, tool bars, and
controls (for
example, radio buttons, check boxes, sliding scales, and so forth).
Furthermore, the output
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module can communicate with a set of input and output devices to receive
signals from the
user.
Other Systems
113671
The computing system 2572 can include one or more internal and/or
external data sources (for example, data sources 2592). In some embodiments,
one or more
of the data repositories and the data sources described above can be
implemented using a
relational database, such as DB2, Sybase, Oracle, CodeBase, and Microsoft SQL
Server
as well as other types of databases such as a flat-file database, an entity
relationship
database, and object-oriented database, and/or a record-based database.
113681
The computer system 2572 can also access one or more data sources (or
databases) 2592. The databases 2592 can be stored in a database or data
repository. The
computer system 2572 can access the one or more databases 2592 through a
network 2588
or can directly access the database or data repository through 110 devices and
interfaces
2582. The data repository storing the one or more databases 2592 can reside
within the
computer system 2572.
Examples of embodiments relating to myocardial infarction risk and severity
from
image-based quantification and characterization of corontuy atherosclerosis:
113691
The following are non-limiting examples of certain embodiments of
systems and methods for determining myocardial infarction risk and severity
and/or other
related features. Other embodiments may include one or more other features, or
different
features, that are discussed herein.
113701
Embodiment 1: A computer-implemented method of determining a
myocardial risk factor via an algorithm-based medical imaging analysis,
comprising:
performing a atherosclerosis and vascular morphology characterization of a
portion of the
coronary vasculature of a patient using information extracted from medical
images of the
portion of the coronary vasculature of the patient; performing a
characterization of the
myocardium of the patient using information extracted from medical images of
the
myocardium of the patient; correlating the characterized vascular morphology
of the patient
with the characterized myocardium of the patient; and determining a myocardial
risk factor
indicative of a degree of myocardial risk from at least one atherosclerotic
lesion.
113711
Embodiment 2: The method of embodiment 1, wherein performing the
atherosclerosis and vascular morphology characterization of the portion of the
coronary
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vasculature of the patient comprises identifying the location of the at least
one
atherosclerotic lesion.
113721
Embodiment 3: The method of embodiment 1 or 2, wherein determining
the myocardial risk factor indicative of the degree of myocardial risk from
the at least one
atherosclerotic lesion comprises determining a percentage of the myocardium at
risk from
the at least one atherosclerotic lesion.
113731
Embodiment 4: The method of embodiment 3, wherein determining a
percentage of the myocardium at risk from the at least one atherosclerotic
lesion comprises
determining the percentage of the myocardium subtended by the at least one
atherosclerotic
lesion.
113741
Embodiment 5: The method of embodiment 3 or 4, determining the
myocardial risk factor indicative of the degree of myocardial risk from the at
least one
atherosclerotic lesion comprises determining an indicator reflective of a
likelihood that the
at least one atherosclerotic lesion will contribute to a myocardial
infarction.
113751
Embodiment 6: The method of any one of embodiments 1-5, wherein
performing the characterization of the myocardium of the patient comprises
performing a
characterization of the left ventricular myocardium of the patient.
113761
Embodiment 7: The method of any one of embodiments 1-6, further
comprising correlating the determined myocardial risk factor to at least one
risk of a severe
clinical event, and/or correlating the determined myocardial risk factor to
the severity of an
event (for example, st-el evati on myocardial infarction, non-ST elevation
myocardial infarction, unstable angina, stable angina, and the like).
113771
Embodiment 8: The method of any one of embodiments 1-7, further
comprising comparing the determined myocardial risk factor to a second
myocardial risk
factor indicative of a degree of myocardial risk to the patient at a previous
point in time.
113781
Embodiment 9: A computer-implemented method of determining a
segmental myocardial risk factor via an algorithm-based medical imaging
analysis,
comprising: characterizing vascular morphology of the coronary vasculature of
a patient
using information extracted from medical images of the coronary vasculature of
the patient;
identifying at least one atherosclerotic lesion within the coronary
vasculature of the patient
using information extracted from medical images of the portion of the coronary
vasculature
of the patient; characterizing a plurality of segments of the myocardium of
the patient to
generate a segmented myocardial characterization using information extracted
from
medical images of the myocardium of the patient; correlating the characterized
vascular
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morphology of the patient with the segmented myocardial characterization of
the patient;
and generating an indicator of segmented myocardial risk from the at least one
atherosclerotic lesion.
113791
Embodiment 10: The method of embodiment 9, wherein generating an
indicator of segmented myocardial risk comprises generating a discrete
indicator of
myocardial risk for at least a subset of the plurality of segments of the
myocardium.
113801
Embodiment 11: The method of embodiment 9 or 10, wherein
generating an indicator of segmented myocardial risk comprises generating a
discrete
indicator of myocardial risk for each of the plurality of segments of the
myocardium.
113811
Embodiment 12: The method of embodiment 9 or 11, wherein
correlating the characterized vascular morphology of the patient with the
segmented
myocardial characterization of the patient comprises identifying for each of
the myocardial
segments a coronary artery primarily responsible for supplying oxygenated
blood to that
myocardial segment.
113821
Embodiment 13: The method of any one of embodiments 9-12, wherein
the segmented myocardial characterization is segmented into 17 segments
according to a
standard AHA 17-segment model.
113831
Embodiment 14: A computer-implemented method of determining a
segmental myocardial risk factor via an algorithm-based medical imaging
analysis,
comprising: applying at least a first algorithm to a first plurality of images
of the coronary
vasculature of' a patient obtained using a first imaging technology to
characterize the
vascular morphology of the coronary vasculature of the patient and to identify
a plurality
of atherosclerotic plaque lesions; applying at least a second algorithm to a
first plurality of
images of the myocardium of the patient obtained using a second imaging
technology to
characterize the myocardium of the patient; applying at least a third
algorithm to relate the
characterized vascular morphology of the patient with the characterized
myocardium of the
patient; and calculating a percentage of subtended myocardium at risk from at
least one of
the plurality of identified atherosclerotic plaque lesions.
113841
Embodiment 15: The method of embodiment 14, additionally
comprising applying an algorithm to a second plurality of images of the
coronary
vasculature of the patient obtained using a third imaging technology to
characterize the
vascular morphology of the coronary vasculature of the patient and to identify
a plurality
of atherosclerotic plaque lesions. The third imaging technology can be, for
example,
intracardiac echocardiography, MR1, and any other suitable technology that can
generate
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images that depict the vascular morphology of the coronary vasculature of the
patient and
to identify a plurality of atherosclerotic plaque lesions.
113851
Embodiment 16: The method of embodiment 15, wherein applying an
algorithm to a second plurality of images of the coronary vasculature of the
patient
comprises applying the first algorithm to the second plurality of images of
the coronary
vasculature of the patient.
[1386]
Embodiment 17: The method of embodiment 14, additionally
comprising applying an algorithm to a second plurality of images of the
myocardium of the
patient obtained using a third imaging technology to characterize the
myocardium of the
patient.
[1387]
Embodiment 18: The method of any one of embodiments 14-17,
wherein applying at least the first algorithm to the first plurality of images
of the coronary
vasculature of a patient obtained using the first imaging technology
additionally comprises
determining characteristics of the identified plurality of atherosclerotic
plaque lesions.
[1388]
Embodiment 19: The method of embodiment 18, additionally
comprising determining a risk of the identified plurality of atherosclerotic
plaque lesions
contributing to a myocardial infarction, and determining an overall risk
indicator based on
the determined risk of the identified plurality of atherosclerotic plaque
lesions contributing
to a myocardial infarction and the calculated percentage of subtended
myocardium at risk
from the identified plurality of atherosclerotic plaque lesions.
[1389]
Embodiment 20: The method of any one of embodiments 14-19,
additionally comprising relating the calculated percentage of subtended
myocardium at risk
from at least one of the plurality of identified atherosclerotic plaque
lesions to a risk of at
least one adverse clinical events.
Combining CFD -based evaluation with atherosclerosis and vascular morphology
[1390]
Various embodiments described herein relate to systems, methods, and
devices for medical image analysis, diagnosis, risk stratification, decision
making and/or
disease tracking. One innovation includes a computer-implemented method of
identifying
a presence and/or degree of ischemia via an algorithm-based medical imaging
analysis is
provided, the method including performing a computational fluid dynamics (CFD)
analysis
of a portion of the coronary vasculature of a patient using imaging data of
the portion of
the coronary vasculature of the patient, performing a comprehensive
atherosclerosis and
vascular morphology characterization of the portion of the coronary
vasculature of the
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patient using coronary computed tomographic angiography (CCTA) of the portion
of the
coronary vasculature of the patient, applying an algorithm that integrates the
CFD analysis
and the atherosclerosis and vascular morphology characterization to provide an
indication
of the presence and/or degree of ischemia within the portion of the coronary
vasculature of
the patient on a pixel-by-pixel basis, the algorithm providing an indication
of the presence
and/or degree of ischemia for a given pixel based upon an analysis of the
given pixel, the
surrounding pixels, and a vessel of the portion of the coronary vasculature of
the patient
with which the pixel is associated.
113911
Performing a computational fluid dynamics (CFD) analysis can include
generating a model of the portion of the coronary vasculature of the patient
based at least
in part on coronary computed tomographic angiography (CCTA) of the portion of
the
coronary vasculature of the patient. Performing a CFD analysis can include
generating a
model of the portion of the coronary vasculature of the patient based at least
in part on the
atherosclerosis and vascular morphology characterization of the portion of the
coronary
vasculature of the patient. Performing a CFD analysis can include computing a
fractional
flow reserve model of the portion of the coronary vasculature of the patient.
113921
Performing a comprehensive atherosclerosis and vascular morphology
characterization of the portion of the coronary vasculature of the patient can
include
determining one or more vascular morphology parameters and a set of quantified
plaque
parameters. Performing a CFD analysis of a portion of the coronary vasculature
of a patient
can include generating a CFD-based indication of the presence and/or degree of
ischemia
within the portion of the coronary vasculature of the patient on a pixel-by-
pixel basis.
Applying the algorithm that integrates the CFD analysis and the
atherosclerosis and
vascular morphology characterization to provide an indication of the presence
and/or
degree of ischemia within the portion of the coronary vasculature of the
patient on a pixel-
by-pixel basis can include providing an indication of agreement with the CFD-
based
indication of the presence and/or degree of ischemia within the portion of the
coronary
vasculature of the patient on a pixel-by-pixel basis. In some embodiments,
information
generated from the CFD analysis and information related to one or more
vascular
morphology parameters and/or a set of quantified plaque parameters can be
input into a ML
algorithm to assess the risk of CAD or MI. In an example, the ML algorithm
compares
information from the CFD analysis and/or the information related to one or
more vascular
morphology parameters and/or a set of quantified plaque parameters to a
database of patient
information to assess or determine a risk of CAD or MI. In an example, the ML
algorithm
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compares information from the CFD analysis and/or the information related to
one or more
vascular morphology parameters and/or a set of quantified plaque parameters to
a database
of patient information to assess or determine the presence and/or severity of
ischemia. In
an example, the ML algorithm can also use patient specific information that
can include
age, gender, race, BMI, medication, blood pressure, heart rate, weight,
height, body habitus,
smoking, diabetes, hypertension, prior CAD, family history, and/or lab test
results to
compare CFD and one or more vascular morphology parameters and/or a set of
quantified
plaque parameters of the patient being evaluated to patients in a database to
assess or
determine the presence and/or severity of ischemia, and/or to assess or
determine a risk of
CAD or MI.
113931
Applying an algorithm that integrates the CFD analysis and the
atherosclerosis and vascular morphology characterization to provide an
indication of the
presence and/or degree of ischemia within the portion of the coronary
vasculature of the
patient on a pixel-by-pixel basis can include analyzing variation in coronary
volume, area,
and/or diameter over the entirety of a cardiac cycle. Analyzing variation in
coronary
volume, area, and/or diameter over the entirety of a cardiac cycle can include
analyzing an
effect of identified atherosclerotic plaque within a wall of an artery on the
deformation of
the artery.
113941
In one aspect, a computer implemented method for non-invasively
estimating blood flow characteristics to assess the severity of plaque and/or
stenotic lesions
using contrast distribution predictions and measurements is provided, the
method including
generating and outputting an initial indicia of a severity of the plaque or
stenotic lesion
using one or more calculated blood flow characteristics, where generating and
outputting
the initial indicia of a severity of the plaque or stenotic lesion includes
receiving one or
more patient-specific images and/or anatomical characteristics of at least a
portion of a
patient's vasculature, receiving images reflecting a measured distribution of
a contrast agent
delivered through the patient's vasculature, projecting one or more contrast
values of the
measured distribution of the contrast agent to one or more points of a patient-
specific
anatomic model of the patient's vasculature generated using the received
patient-specific
images and/or the received anatomical thereby creating a patient-specific
measured model
indicative of the measured distribution, defining one or more physiological
and boundary
conditions of a blood flow to non-invasively simulate a distribution of the
contrast agent
through the patient-specific anatomic model of the patient's vasculature,
simulating, using
a processor, the distribution of the contrast agent through the one or more
points of the
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patient-specific anatomic model using the defined one or more physiological
and boundary
conditions and the received patient-specific images and/or anatomical
characteristics,
thereby creating a patient-specific simulated model indicative of the
simulated distribution,
comparing, using a processor, the patient-specific measured model and the
patient-specific
simulated model to determine whether a similarity condition is satisfied,
updating the
defined physiological and boundary conditions and re-simulating the
distribution of the
contrast agent through the one or more points of the patient-specific anatomic
model until
the similarity condition is satisfied, calculating, using a processor, one or
more blood flow
characteristics of blood flow through the patient-specific anatomic model
using the updated
physiological and boundary conditions, and generating and outputting the
initial indicia of
a severity of the plaque or stenotic lesion using the one or more blood flow
characteristics
of blood flow that were calculated using the updated physiological and
boundary
conditions, performing a comprehensive atherosclerosis and vascular morphology
characterization of the portion of the patient's vasculature using coronary
computed
tomographic angiography (CCTA) of the portion of the patient's vasculature,
and applying
an algorithm that integrates the initial indicia of a severity of the plaque
or stenotic lesion
and the atherosclerosis and vascular morphology characterization to provide an
indication
of the presence and/or degree of ischemia within the portion of the patient's
vasculature on
a pixel-by-pixel basis.
113951
The algorithm can provide an indication of the presence and/or degree
of ischemia for a given pixel based upon an analysis of the given pixel, the
surrounding
pixels, and a vessel of the portion of the coronary vasculature of the patient
with which the
pixel is associated. Prior to simulating the distribution of the contrast
agent in the patient-
specific anatomic model for the first time, defining one or more physiological
and boundary
conditions can include finding form or functional relationships between the
vasculature
represented by the anatomic model and physiological characteristics found in
populations
of patients with a similar vascular anatomy. Prior to simulating the
distribution of the
contrast agent in the patient-specific anatomic model for the first time,
defining one or more
physiological and boundary conditions can include one or more of assigning an
initial
contrast distribution, or assigning boundary conditions related to a flux of
the contrast agent
(i) at one or more of vessel walls, outlet boundaries, or inlet boundaries, or
(ii) near plaque
and/or stenotic lesions.
113961
The blood flow characteristics can include one or more of, a blood flow
velocity, a blood pressure, a heart rate, a fractional flow reserve (FFR)
value, a coronary
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flow reserve (CFR) value, a shear stress, or an axial plaque stress. Receiving
one or more
patient-specific images can include receiving one or more images from coronary
angiography, biplane angiography, 3D rotational angiography, computed
tomography (CT)
imaging, magnetic resonance (MR) imaging, ultrasound imaging, or a combination
thereof
113971
The patient-specific anatomic model can be a reduced-order mode in the
two-dimensional anatomical domain, and projecting the one or more contrast
values can
include averaging one or more contrast values over one or more cross sectional
areas of a
vessel. The patient-specific anatomic model can include information related to
the
vasculature, including one or more of a geometrical description of a vessel,
including the
length or diameter, a branching pattern of a vessel, one or more locations of
any stenotic
lesions, plaque, occlusions, or diseased segments, or one or more
characteristics of diseases
on or within vessels, including material properties of stenotic lesions,
plaque, occlusions,
or diseased segments. The physiological conditions can be measured, obtained,
or derived
from computational fluid dynamics or the patient-specific anatomic model, and
can include
one or more of, blood pressure flux, blood velocity flux, the flux of the
contrast agent,
baseline heart rate, geometrical and material characteristics of the
vasculature, or
geometrical and material characteristics of plaque and/or stenotic lesions,
and where the
boundary conditions define physiological relationships between variables at
boundaries of
a region of interest, where the boundaries can include one or more of, inflow
boundaries,
outflow boundaries, vessel wall boundaries, or boundaries of plaque and/or
stenotic lesions.
113981
The simulating, using the processor, of the distribution of the contrast
agent for the one or more points in the patient-specific anatomic model using
the defined
one or more physiological and boundary conditions can include one or more of
determining
scalar advection-diffusion equations governing the transport of the contrast
agent in the
patient-specific anatomic model, the equations governing the transport of the
contrast agent
reflecting any changes in a ratio of flow to lumen area at or near a stenotic
lesion or plaque,
or computing a concentration of the contrast agent for the one or more points
of the patient-
specific anatomic model, where computing the concentration requires assignment
of an
initial contrast distribution and initial physiological and boundary
conditions. Satisfying a
similarity condition can include specifying a tolerance that can measure
differences
between the measured distribution of the contrast agent and the simulated
distribution of
the contrast agent, prior to simulating the distribution of the contrast agent
and determining
whether the difference between the measured distribution of the contrast agent
and the
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simulated distribution of the contrast agent falls within the specified
tolerance, the
similarity condition being satisfied if the difference falls within the
specified tolerance.
113991
Updating the defined physiological and boundary conditions and re-
simulating the distribution of the contrast agent can include mapping a
concentration of the
contrast agent along vessels with one or more of features derived from an
analytic
approximation of an advection-diffusion equation describing the transport of
fluid in one
or more vessels of the patient-specific anatomic model, features describing
the geometry
of the patient-specific anatomic model, including, one or more of, a lumen
diameter of a
plaque or stenotic lesion, a length of a segment afflicted with a plaque or
stenotic lesion, a
vessel length, or the area of a plaque or stenotic lesion, or features
describing a patient-
specific dispersivity of the contrast agent. Updating the defined
physiological and
boundary conditions and re-simulating the distribution of the contrast agent
can include
using one or more of a derivative-free optimization based on nonlinear
ensemble filtering,
or a gradient-based optimization that uses finite difference or adjoint
approximation.
114001
The method can further include, upon a determination that the measured
distribution of the contrast agent and the simulated distribution of the
contrast agent satisfy
the similarity condition, enhancing the received patient-specific images using
the simulated
distribution of the contrast agent, and outputting the enhanced images as one
or more
medical images to an electronic storage medium or display. Enhancing the
received
patient-specific images can include one or more of replacing pixel values with
the simulated
distribution of the contrast agent, or using the simulated distribution of the
contrast agent
to de-noise the received patient-specific images via a conditional random
field.
114011
The method can further include, upon a determination that the measured
distribution of the contrast agent and the simulated distribution of the
contrast agent
satisfies the similarity condition, using the calculated blood flow
characteristics associated
with the simulated distribution of the contrast agent to simulate perfusion of
blood in one
or more areas of the patient-specific anatomic model, generating a model or
medical image
representing the perfusion of blood in one or more areas of the patient-
specific anatomic
model, and outputting the model or medical image representing the perfusion of
blood in
one or more areas of the patient-specific anatomic model to an electronic
storage medium
or display.
114021
The patient-specific anatomic model can be represented in a three-
dimensional anatomical domain, and projecting the one or more contrast values
can include
assigning contrast values for each point of a three-dimensional finite element
mesh.
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114031
Performing a comprehensive atherosclerosis and vascular morphology
characterization of the portion of the patient's vasculature using coronary
computed
tomographic angiography (CCTA) of the portion of the patient's vasculature can
include
generating image information for the patient, the image information including
image data
of computed tomography (CT) scans along a vessel of the patient, and
radiodensity values
of coronary plaque and radiodensity values of perivascular tissue located
adjacent to the
coronary plaque, and determining, using the image information of the patient,
coronary
plaque information of the patient, where determining the coronary plaque
information can
include quantifying, using the image information, radiodensity values in a
region of
coronary plaque of the patient, quantifying, using the image information,
radiodensity
values in a region of perivascular tissue adjacent to the region of coronary
plaque of the
patient, and generating metrics of coronary plaque of the patient using the
quantified
radiodensity values in the region of coronary plaque and the quantified
radiodensity values
in the region of perivascular tissue adjacent to the region of coronary
plaque.
114041
The method can further include accessing a database of coronary plaque
information and characteristics of other people, the coronary plaque
information in the
database including metrics generated from radiodensity values of a region of
coronary
plaque in the other people and radiodensity values of perivascular tissue
adjacent to the
region of coronary plaque in the other people, and the characteristics of the
other people
including information at least of age, sex, race, diabetes, smoking, and prior
coronary artery
disease, and characterizing the coronary plaque information of the patient by
comparing
the metrics of the coronary plaque information and characteristics of the
patient to the
metrics of the coronary plaque information of other people in the database
having one or
more of the same characteristics, where characterizing the coronary plaque
information can
include identifying the coronary plaque as a high risk plaque. Characterizing
the coronary
plaque can include identifying the coronary plaque as a high risk plaque if it
is likely to
cause ischemia based on a comparison of the coronary plaque information and
characteristics of the patient to the coronary plaque information and
characteristics of the
other people in the database. The characterization of coronary plaque as high
risk plaque
can be used to provide an indication of the presence and/or degree of ischemia
within a
portion of the patient's vasculature in at least one pixel adjacent the
coronary plaque.
Characterizing the coronary plaque can include identifying the coronary plaque
as a high
risk plaque if it is likely to cause vasospasm based on a comparison of the
coronary plaque
information and characteristics of the patient to the coronary plaque
information and
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characteristics of the other people in the database. Characterizing the
coronary plaque can
include identifying the coronary plaque as a high risk plaque if it is likely
to rapidly progress
based on a comparison of the coronary plaque information and characteristics
of the patient
to the coronary plaque information and characteristics of the other people in
the database.
114051
Generating metrics using the quantified radiodensity values in the region
of coronary plaque and the quantified radiodensity values in a region of
perivascular tissue
adjacent to the region of the patient can include determining, along a line, a
slope value of
the radiodensity values of the coronary plaque and a slope value of the
radiodensity values
of the perivascular tissue adjacent to the coronary plaque. Generating metrics
can further
include determining a ratio of the slope value of the radiodensity values of
the coronary
plaque and a slope value of the radiodensity values of the perivascular tissue
adjacent to
the coronary plaque.
114061
Generating metrics using the quantified radiodensity values in the region
of coronary plaque and the quantified radiodensity values in a region of
perivascular tissue
adjacent to the region of the patient can include generating, using the image
information, a
ratio between quantified radiodensity values of the coronary plaque and
quantified
radiodensity values of the corresponding perivascular tissue.
114071
The perivascular tissue can be perivascular fat, and generating metrics
using the quantified radiodensity values in the region of coronary plaque and
the quantified
radiodensity values in the region of perivascular tissue adjacent to the
region of coronary
plaque of the patient can include generating a ratio of a density of the
coronary plaque and
a density of the perivascular fat. The perivascular tissue can be a coronary
artery, and
generating metrics using the quantified radiodensity values in the region of
coronary plaque
and the quantified radiodensity values in the region of perivascular tissue
adjacent to the
region of coronary plaque of the patient can include generating a ratio of a
density of the
coronary plaque and a density of the coronary artery. Generating the ratio can
include
generating the ratio of a maximum radiodensity value of the coronary plaque
and a
maximum radiodensity value of the perivascular fat. Generating the ratio can
include
generating a ratio of a minimum radiodensity value of the coronary plaque and
a minimum
radiodensity value of the perivascular fat. Generating the ratio can include
generating a
ratio of a maximum radiodensity value of the coronary plaque and a minimum
radiodensity
value of the perivascular fat. Generating the ratio can include generating a
ratio of a
minimum radiodensity value of the coronary plaque and a maximum radiodensity
value of
the perivascular fat.
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114081
Various examples described elsewhere herein are directed to systems,
methods, and devices for medical image analysis, diagnosis, risk
stratification, decision
making and/or disease tracking. In some embodiments, the systems, devices, and
methods
described are configured to utilize non-invasive medical imaging technologies,
such as a
CT image for example, which can be inputted into a computer system configured
to
automatically and/or dynamically analyze the medical image to identify one or
more
coronary arteries and/or plaque within the same. For example, in some
embodiments, the
system can be configured to utilize one or more machine learning and/or
artificial
intelligence algorithms to automatically and/or dynamically analyze a medical
image to
identify, quantify, and/or classify one or more coronary arteries and/or
plaque. In some
embodiments, the system can be further configured to utilize the identified,
quantified,
and/or classified one or more coronary arteries and/or plaque to generate a
treatment plan,
track disease progression, and/or a patient-specific medical report, for
example using one
or more artificial intelligence and/or machine learning algorithms. In some
embodiments,
the system can be further configured to dynamically and/or automatically
generate a
visualization of the identified, quantified, and/or classified one or more
coronary arteries
and/or plaque, for example in the form of a graphical user interface. Further,
in some
embodiments, to calibrate medical images obtained from different medical
imaging
scanners and/or different scan parameters or environments, the system can be
configured
to utilize a normalization device comprising one or more compartments of one
or more
materials.
114091
As will be discussed in further detail, the systems, devices, and methods
described allow for automatic and/or dynamic quantified analysis of various
parameters
relating to plaque, cardiovascular arteries, and/or other structures. More
specifically, in
some embodiments described herein, a medical image of a patient, such as a
coronary CT
image, can be taken at a medical facility. Rather than having a physician
eyeball or make
a general assessment of the patient, the medical image is transmitted to a
backend main
server in some embodiments that is configured to conduct one or more analyses
thereof in
a reproducible manner. As such, in some embodiments, the systems, methods, and
devices
described herein can provide a quantified measurement of one or more features
of a
coronary CT image using automated and/or dynamic processes. For example, in
some
embodiments, the main server system can be configured to identify one or more
vessels,
plaque, and/or fat from a medical image. Based on the identified features, in
some
embodiments, the system can be configured to generate one or more quantified
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measurements from a raw medical image, such as for example radiodensity of one
or more
regions of plaque, identification of stable plaque and/or unstable plaque,
volumes thereof,
surface areas thereof, geometric shapes, heterogeneity thereof, and/or the
like. In some
embodiments, the system can also generate one or more quantified measurements
of vessels
from the raw medical image, such as for example diameter, volume, morphology,
and/or
the like. Based on the identified features and/or quantified measurements, in
some
embodiments, the system can be configured to generate a risk assessment and/or
track the
progression of a plaque-based disease or condition, such as for example
atherosclerosis,
stenosis, and/or ischemia, using raw medical images. Further, in some
embodiments, the
system can be configured to generate a visualization of GUI of one or more
identified
features and/or quantified measurements, such as a quantized color mapping of
different
features. In some embodiments, the systems, devices, and methods described
herein are
configured to utilize medical image-based processing to assess for a subject
his or her risk
of a cardiovascular event, major adverse cardiovascular event (MACE), rapid
plaque
progression, and/or non-response to medication. In particular, in some
embodiments, the
system can be configured to automatically and/or dynamically assess such
health risk of a
subject by analyzing only non-invasively obtained medical images. In some
embodiments,
one or more of the processes can be automated using an Al and/or ML algorithm.
In some
embodiments, one or more of the processes described herein can be performed
within
minutes in a reproducible manner. This is stark contrast to existing measures
today which
do not produce reproducible prognosis or assessment, take extensive amounts of
time,
and/or require invasive procedures.
[1410]
As such, in some embodiments, the systems, devices, and methods
described are able to provide physicians and/or patients specific quantified
and/or measured
data relating to a patient's plaque that do not exist today. For example, in
some
embodiments, the system can provide a specific numerical value for the volume
of stable
and/or unstable plaque, the ratio thereof against the total vessel volume,
percentage of
stenosis, and/or the like, using for example radiodensity values of pixels
and/or regions
within a medical image. In some embodiments, such detailed level of quantified
plaque
parameters from image processing and downstream analytical results can provide
more
accurate and useful tools for assessing the health and/or risk of patients in
completely novel
ways. Additional information regarding the quantification of detailed plaque
data
information is described in U.S. Patent No. 10,813,612 (for example, including
but not
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limited to description relating to Figures 6, 7, and 9-12) which is
incorporated by reference
herein.
[1411]
The characterization of atherosclerosis and vascular morphology, and
other data indicative of the state of the vessels of the patient and the
behavior of those
vessels, can be combined with or otherwise used to augment or improve various
types of
cardiovascular analysis or monitoring. By providing a detailed level of
quantified plaque
parameters, a more precise patient-specific model can be generated and used in
conjunction
with computational fluid dynamics (CFD) and/or fluid-structure interaction
(FSI) analysis
to evaluate patient-specific coronary pressure and flow.
Overview of Ischemia Identification
[1412]
Patients with coronary artery disease (CAD) are susceptible to coronary
ischemia, in which a coronary vessel exhibits reduced coronary pressure and/or
flow. In
patients with symptoms suggestive of-coronary artery disease, the
identification of coronary
ischemia, or the exclusion of coronary ischemia, can be helpful in evaluating
the coronary
artery disease and determining a recommended treatment. In particular, the
identification
of coronary ischemia can indicate a need for invasive treatment, such as
invasive coronary
angiography with intended coronary revascularization.
[1413]
Historically, the presence, extent, and severity of ischemia has been
determined through stress testing. This stress testing can be performed
without imaging,
or can be performed in conjunction with imaging of the patient. In this stress
testing,
surrogate or actual measures of myocardial blood taken when the patient is at
rest are
compared to measures taken when the patient is in a 'stress' states. These
stress states can
be achieved through exercise, or can be brought about by pharmacologic
vasodilation.
[1414]
Recently, coronary computed tomographic angiography (CCTA) has
been introduced as an alternative to stress testing. CCTA allows direct
visualization of
coronary arteries in a non-invasive fashion. CCTA demonstrates high diagnostic
performance for the detection or exclusion of high-grade coronary legions,
such as coronary
stenoses where the vessel is abnormally narrowed, which may be the cause of
ischemia.
[1415]
Using diagnostic catheterization, the fractional flow reserve (FFR) of an
observed lesion can be directly measured. Prior studies have demonstrated a
high rate of
"false positives- when severe lesions are detected by CCTA and used as an
indicator of
coronary ischemia. In such cases, these detected lesions are not functionally
significant,
and do not, in fact, cause ischemia by invasive fractional flow reserve.
Because these -false
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positives- may result in the use of invasive and unnecessary procedures for
the purposes of
confirming and treating these lesions, it is desirable to improve the
diagnostic performance
of CCTA-based analysis in the detection of coronary ischemia and other
conditions.
[1416]
A variety of techniques have been introduced which leverage CCTA
findings for the determination of coronary ischemia. In some techniques, CCTA
can be
used in conjunction with stress testing. In some techniques, CCTA can be used
to calculate
a transarterial attenuation gradient, which can be used to determine an
estimate of a pressure
gradient or fractional flow reserve for the patient. In some techniques,
computational fluid
dynamics can be applied to CCTA in order to provide a three-dimensional
evaluation of
coronary pressure and/or flow in a patient-specific fashion.
Computational Fluid Dynamics (FD) Analysis
[1417]
CFD can be used to evaluate coronary pressure and/or flow for a given
vessel geometry and boundary conditions based on the solving of the Navier-
Stokes
equations, or similar analysis. This information can be used, for example, to
determine the
functional significance of a coronary lesion, such as whether the lesion
impacts blood flow,
and the degree to which the blood flow is impacted by the lesion. In addition,
this
information can be used in a predictive manner, such as to predict changes in
coronary
blood flow, pressure, or myocardial perfusion under other states such as
during exercise or
when the patient is otherwise under a stress state. This information can also
be used to
predict the outcome of treatments or other interventions.
[1418]
Early CFD-based analysis of the cardiovascular system was used to
model complex cerebral vasculature. An overview of the early development of
computerized fluid dynamics analysis as applied to the evaluation of cerebral
circulation is
described in U.S. Patent No. 7,191,110, which is incorporated by reference
herein in its
entirety.
[1419]
In addition to the fluid dynamics modules that were used to model
vasculature, including cerebral vasculature, electrical models were also built
based on the
similarity of the governing equations of electrical circuits and one-
dimensional linear flow,
due to the suitability of electrical networks for simulating networks with
capacitance and
resistance. Transmission line equations similar to the linearized Navier-
Stokes equation
and vessel wall deformation were used to simulate the pulsatile flow and
flexible vessel
wall.
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114201
The limitations of computing capacity during early use of CFD-based
analysis placed significant restrictions on the detail that could be included
in a practical
implementation of CFD-based analysis for a given patient. As a result, early
CFD-based
analysis of portions of the cardiovascular system of a patient included
assumptions which
simplified the overall model, such as treating the vessel walls as a rigid
tube, and treating
the blood as a non-compressible Newtonian fluid.
114211
Similar methods were applied to the modeling and evaluation of blood
flow in the coronary arteries and adjacent portions of the cardiovascular
system. For
example, U.S. Patent No. 8,386,188, which is incorporated by reference herein
in its
entirety, describes methods for modeling portions of the cardiovascular system
of a patient
using patient-specific imaging data (for example, including but not limited
to, as described
in reference to Figure 2) and generating a three-dimensional model
representing at least a
portion of the patient's cardiovascular system using the patient-specific data
(for example,
including but not limited to, as described in reference to Figure 3-24).
114221
The CFD analysis can be based at least in part on a three-dimensional
model of a portion of the cardiovascular system of the patient, such as a
portion of the
patient's heart. For example, the three-dimensional model can include the
aorta, some or
all of the main coronary arteries, and/or other vessels downstream of the main
coronary
arteries. In some embodiments, the three-dimensional model can include, or can
be used
to generate, a volumetric mesh such as a finite element mesh or a finite
volume mesh. In
some embodiments, this model can be generated using information obtained from
a CCTA,
although other imaging techniques, such as magnetic resonance imaging or
ultrasound can
also be used. The model can be dynamic, indicative of the changes in vessel
shape over a
cardiac cycle.
114231
The geometric dimensions of the model can be used to determine the
boundary conditions of the vessel walls. In addition, the boundary conditions
at the inlet
and the outlet of the section(s) to be analyzed can also be assigned in any
suitable manner,
such as by coupling a model to the boundary. Noninvasive measurements such as
cardiac
output, blood pressure, and myocardial mass can be used in assigning the inlet
or outlet
boundary conditions. As described in U.S. Patent No. 7,191,110 and U.S. Patent
No.
8,386,188, reduced order models of portions of the patient's vasculature may
be generated
and used in the CFD analysis, to reduce computing load and to determine
boundary
conditions for more robustly modeled portions of the patient's vasculature.
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114241
The CFD analysis can be used to determine blood flow characteristics
for the entire modeled portion of the cardiovascular system of the patient, or
for one or
more sections within the modeled portion. In some embodiments, the determined
blood
flow characteristics can include some or all of the blood flow velocity,
pressure, flow rate,
or FFR at various locations throughout the modeled portion of the
cardiovascular system
of the patient. Other conditions and parameters may also be calculated, such
as shear
stresses throughout the modeled portion of the cardiovascular system of the
patient.
114251
The inlet and outlet boundary conditions may be assigned and/or varied
based on a variety of physiologic conditions, including for a state of rest,
or a state of
maximum stress or maximum hyperemia, to determine blood flow characteristics
under a
variety of physiologic conditions.
114261
In some embodiments, a simulated blood pressure model can be
generated, where the simulated blood pressure model provides information
regarding the
pressure at various locations along the modeled portion of the cardiovascular
system of the
patient. Such a simulated blood pressure model can be used, in turn, to
generate an FFR
model of the modeled portion of the cardiovascular system of the patient,
where the FFR
model can be calculated as the ratio of the blood pressure at a given location
in the
cardiovascular system divided by the blood pressure in the aorta under
conditions of
maximum stress, or hyperemia, resulting in increased coronary blood flow.
114271
The CFD model may be segmented based upon the geometry of the
various segments of the modeled portion of the cardiovascular system of the
patient,
including both the overall vessel shape and arrangement, as well as any local
variations in
geometry. For example, a diseased portion which has a narrow cross-section, a
lesion,
and/or a stenosis may be modeled in one or more distinct segments. The cross-
sectional
area and local minimum of the cross-sectional area of the diseased portions
and stenoses
may be measured and used in the CFD analysis.
114281
The determined blood flow characteristics, and in particular the local
values of the calculated FFR model, can be used to provide an indication of
the presence
of a functionally significant lesion or other feature which may require
treatment. In
particular, if the calculated FFR at a given location is below a threshold
level, the local drop
in FFR is indicative of the presence of a functionally significant lesion
located upstream of
the low FFR point. In some embodiments, an indication of the calculated FFR
throughout
the modeled portion of the cardiovascular system can be provided as a result,
and the
location of any functionally significant lesions can be identified by a user.
In other
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embodiments, the upstream geometry of the modeled position of the
cardiovascular system
of the patient can be analyzed and the location of any functionally
significant lesions can
be identified by a computer system as part of the CFD analysis or as a
separate analysis.
114291
U.S. Patent No. 10,433,740, which is incorporated by reference herein
in its entirety, broadly describes an example of machine learning as part of
the analysis of
a geometric model of a patient in addition to one or more measured or
estimated
physiological parameters. The described parameters may include global
parameters, such
as blood pressure, blood viscosity, patient age, patient gender, mass of the
supplied tissue,
or may be local, such as an estimated density of the vessel wall at a
particular location. The
system described in U.S. Patent No. 10,433,740, and other similar systems, may
create, for
each point at which there is a value of a blood flow characteristic, a feature
vector
describing the patient specific geometry at that point and estimating of
physiological or
phenotypic parameters of the patient. Such systems as described in U.S. Patent
No.
10,433,740 may train a machine learning algorithm to predict the blood flow
characteristics, such as FFR, at the various points from the feature vectors.
The system
may then, in turn, use the estimate of FFR to classify a vessel or patient as
ischemia positive
or negative based on the estimation of FFR.
114301
U.S. Patent No. 10,307,131, which is incorporated by reference herein
in its entirety, describes systems which may utilize more accurate estimations
of boundary
conditions to improve the accuracy of FFR computed tomography used to
noninvasively
determine FFR. The computed blood flow characteristics may be determined in an
iterative
fashion, by comparing a predicted contrast distribution and a measured
contrast distribution
until the solution converges, and the computed blood flow characteristics may
then be used
to generate a model used in a biochemical analysis.
114311
However, systems such as those described in U.S. Patent Nos.
8,386,188, 10,433,740, and 10,307,131, are directed primarily to the use of
additional
analysis to improve the accuracy of the calculation of blood flow
characteristics such as
FFR, and to use those FFR calculations or estimations to provide more accurate
predictions
of the functional severity of stenoses or the presence of ischemia.
114321
In some embodiments, further analysis may be performed based on a
CFD model of at least a portion of the cardiovascular system of a patient. In
some
embodiments, the CFD model described may be updated as described in U.S.
Patent Nos.
8,386,188, to reflect possible treatments, such as the insertion of a stent,
and the CFD
analysis performed based on the updated model to determine blood flow
characteristics for
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at least a portion of the updated CFD model. Such a system can attempt to
reduce the
likelihood of a false positive by improving the FFR analysis, but does not,
for example,
provide an independent assessment of the presence or degree of a condition
such as
ischemia as a check against potential false positives generated using FFR
analysis.
114331
In some embodiments, this CFD analysis can model the coronary artery
and/or other vessels or portions of the cardiovascular system as a rigid tube.
In other
embodiments, this CFD analysis can model the cardiovascular system as a
compliant tube,
and the elastodynamic equations for wall dynamics may be solved together with
the Navier-
Stokes equations. This CFD analysis can model the blood as a non-compressible
Newtonian fluid, although the blood may also be modeled as a non-Newtonian or
multiphase fluid. In addition, this CFD analysis also requires certain
assumptions in
modeling both the boundary conditions and the vessel behavior, such as
coronary
vasodilation under hyperemia.
114341
In some embodiments, the model used for the CFD analysis can be
developed using or based at least in part on a characterization of
atherosclerosis and
vascular morphology as described in U.S. Patent No. 7,191,110. The detail and
precision
with respect to the atherosclerosis and vascular morphology information which
can be
determined using the described analysis can increase the accuracy of the CFD
analysis by
more precisely modeling the modeled portion of the cardiovascular system of
the patient.
In some embodiments, the information regarding atherosclerosis and vascular
morphology
can be used to provide a model more indicative of the physical parameters of
the modeled
portion of the cardiovascular system of the patient, particularly the physical
parameters
which are affected by the presence, type, and volume of plaque.
114351
Similarly, U.S. Patent No. 10,052,031, which is incorporated by
reference herein in its entirety, describes the computation of hemodynamic
qualities
indicative of the functional severity of stenosis, which can be used in the
treatment and/or
assessment of coronary artery disease. The system can be used to identify
lesion specific
ischemia using a combination of perfusion scanning data, anatomical imaging of
coronary
vessels, and computational fluid dynamics. Like the system described in U.S.
Patent No.
10,307,131, however, the system described in U.S. Patent No. 10,052,031, is
directed to
improving the computed hemodynamic quantity indicative of the functional
severity of the
stenosis through iterative comparison of a simulated perfusion map to a
measured perfusion
map obtained by perfusion scanning of a patient.
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114361
U.S. Patent No. 10,888,234, which is incorporated by reference herein
in its entirety, describes a system for machine learning based non-invasive
functional
assessment of coronary artery stenosis from medical image data. Like other
systems in the
references incorporated herein, the system described in U.S. Patent No.
10,888,234 is
directed towards improvement of the determination of an FFR value or other
hemodynamic
index value. The system of U.S. Patent No. 10,888,234, utilizes machine
learning as an
alternative to more computationally-intensive physics-based modeling of
portions of the
cardiovascular system of the patient, although mechanistic modeling may also
be used to
compute an FFR value for used in the analysis.
114371
In some embodiments, Fluid-Surface Interaction (FSI) analysis may be
performed in addition to or in conjunction with the CFD analysis. The
characterization of
atherosclerosis and vascular morphology provided by the technology disclosed
in U.S.
Patent No. 7,191,110 can allow a more accurate model of the portion of the
cardiovascular
system of the patient. By modeling the portion of the cardiovascular system of
the patient
as a deformable structure, greater accuracy can be obtained in the output
models generated
by the CFD analysis.
Atherosclerosis and Vascular Morphology Characterization
114381
In some embodiments, the characterization of atherosclerosis and
vascular morphology provided by the technology disclosed in U.S. Patent No.
7,191,110
can be performed either before or after the performance of the CFD analysis
discussed
above. This process may include taking one or more medical images of a
patient, such as
a CCTA, at a medical facility. These images may be transmitted to a backend
main server
in some embodiments that is configured to conduct one or more analyses thereof
in a
reproducible manner. This analysis may include the use of artificial
intelligence (Al),
machine learning (ML) and/or other algorithms. In some embodiments, the
systems,
methods, and devices described herein can provide a quantified measurement of
one or
more features of a coronary CT image using automated and/or dynamic processes.
114391
In certain embodiments, the characterization of atherosclerosis and
vascular morphology may be performed prior to the performance of the CFD
analysis, and
the resulting characterization, or information derived therefrom, may be used
as part of the
generation of a model of a portion of the cardiovascular system of the
patient.
114401
In some embodiments, the characterization of atherosclerosis and
vascular morphology may include the analysis of a series of CCTA images or any
other
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suitable images, and the generation of a three-dimensional model of the
patient's
cardiovascular system. This analysis can include the generation of one or more
quantified
measurements of vessels from the raw medical image, such as for example
diameter,
volume, morphology, and/or the like. This analysis may segment the vessels in
a
predetermined manner, or in a dynamic manner, in order to provide more
detailed overview
of the vascular morphology of the patient.
114411
In particular, in some embodiments, the system can be configured to
utilize one or more AI and/or ML algorithms to automatically and/or
dynamically identify
one or more arteries, including for example coronary arteries, carotid
arteries, aorta, renal
artery, lower extremity artery, and/or cerebral artery. In some embodiments,
one or more
AT and/or ML algorithms use a neural network (CNN) that is trained with a set
of medical
images (e.g., CT scans) on which arteries and features (e.g., plaque, lumen,
perivascular
tissue, and/or vessel walls) have been identified, thereby allowing the Al
and/or ML
algorithm to automatically identify arteries directly from a medical image. In
some
embodiments, the arteries are identified by size and/or location.
114421
This analysis can also include the identification and classification of
plaque within the cardiovascular system of the patient. In some embodiments,
the system
can be configured to identify a vessel wall and a lumen wall for each of the
identified
coronary arteries in the medical image. In some embodiments, the system is
then
configured to determine the volume in between the vessel wall and the lumen
wall as
plaque_ In some embodiments, the system can be configured to identify regions
of plaque
based on the radiodensity values typically associated with plaque, for example
by setting a
predetermined threshold or range of radiodensity values that are typically
associated with
plaque with or without normalizing using a normalization device.
114431
In some embodiments, the characterization of atherosclerosis may
include the generation of one or more quantified measurements from a raw
medical image,
such as for example radiodensity of one or more regions of plaque,
identification of stable
plaque and/or unstable plaque, volumes thereof, surface areas thereof,
geometric shapes,
heterogeneity thereof, and/or the like. Using this plaque identification and
classification,
the overall plaque volume may be determined, as well as the amount of
calcified stable
plaque and the amount of uncalcified plaque. In some embodiments, more
detailed
classification of atherosclerosis than a binary assessment of calcified vs.
non-calcified
plaque may be made. For example, the plaque may be classified ordinally, with
plaque
classified as dense calcified plaque, calcified plaque, fibrous plaque,
fibrofatty plaque,
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necrotic core, or admixtures of plaque types. The plaque may also be
classified
continuously, by attenuation density on a scale such as a Hounsfield unit
scale or a similar
classification system.
114441
The information which can be obtained in the characterization of
atherosclerosis may be dependent upon the type of imaging being performed. For
example,
when the CCTA images are creating using a single-energy CT process, the
relative material
density of the plaque relative to the surrounding tissue can be determined,
but the absolute
material density may be unknown. In contrast, when the CCTA images are
creating using
a multi-energy CT process, the absolute material density of the plaque and
other
surrounding tissue can be measured.
114451
The characterization of atherosclerosis and vascular morphology may
include in particular the identification and classification of stenoses within
the
cardiovascular system of the patient. This may include the calculation or
determination of
a numerical calculation or representation of coronary stenosis based on the
quantified
and/or classified atherosclerosis derived from the medical image. The system
may be
configured to calculate stenosis using the one or more vascular morphology
parameters
and/or quantified plaque parameters derived from the medical image of a
coronary region
of the patient. In some embodiments, the system is configured to dynamically
identify an
area of stenosis within an artery, and calculate information regarding the
area of stenosis,
such as vessel parameters including diameter, curvature, local vascular
morphology, and
the shape of the vessel wall and the lumen wall in the area of stenosis.
114461
The identified stenoses may be used in the generation of a model of a
portion of the cardiovascular system of the patient. The use of the quantified
stenosis
information may include the modeling of the vessel boundary conditions. The
use of the
quantified stenosis information may also include the use of the quantified
stenosis
information to determine a segmentation of the model for use in the CFD
analysis or
subsequent processing, or to alter the relative density of the nodes of a
three-dimensional
mesh used as a CFD model, with increased node density at and around identified
stenoses.
114471
In an embodiment in which the CFD analysis is primarily focused on the
identification of functionally significant stenoses, providing additional
detail in a calculated
FFR in regions expected to be of particular interest can improve the CFD
analysis while
without significantly increasing the overall computational load. This may be
of particular
utility when the CFD analysis is augmented or replaced with a more
computationally-
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intensive FSI analysis of at least a portion of the modeled portion of the
cardiovascular
system of the patient.
[1448]
In other embodiments, the CFD modeling and CFD analysis may be
performed partially or wholly independent of the characterization of
atherosclerosis and
vascular morphology. In such an embodiment, the CFD modeling and analysis may
be
performed prior to, or in parallel with, the characterization of
atherosclerosis and vascular
morphology.
CFD Analysis Verification Using Atherosclerosis and Vascular Morphology
Characterization
[1449]
In addition to the identification of functionally significant stenoses or
legions using CFD analysis, the characterization of atherosclerosis and
vascular
morphology can be used to provide an independent assessment of functionally
significant
stenoses and whether a given vessel is ischemic.
[1450]
In particular, the determined data and calculations resulting from the
atherosclerosis characterization can be analyzed to detect characteristics of
atherosclerosis
and vascular morphology which increase the likelihood of a vessel being
ischemic. These
characteristics indicative of vessel ischemia include, but are not limited to,
the presence
and/or volume of non-calcified plaque, and in particular low-density non-
calcified plaque.
Other characteristics which can be analyzed to provide an indication of vessel
ischemia
include lumen volume and positive remodeling of vessels in the area of lesions
or stenoses.
[1451]
The analysis of these characteristics can be combined with the CFD
analysis to improve the discrimination of vessels as ischemic or not ischemic.
The analysis
of these characteristics can also be used to augment the information used to
generate the
CFD model and perform the CFD analysis.
[1452]
Because CCTA images can be acquired over the entire cardiac cycle,
differences in coronary volume, area, and/or diameter can be observed and
measured as the
coronary arteries dilate and/or constrict. The relationship of the
atherosclerotic plaque
within the wall of the artery, coupled to its relative ability to dilate
and/or constrict, can
also provide information on the effects of the atherosclerotic plaque on
normal coronary
vasomotor function, Even when the absolute material density of the identified
plaque is
unknown, such as due to the use of a single-energy CT process to acquire the
CCTA,
information regarding the structural properties of the identified plaque can
be determined
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by observation of the ability of a given portion of a vessel to dilate and/or
constrict in
comparison to surrounding portions of the vessel.
114531
In addition to analyzing variance in vessel dimensions over the course
of a cardiac cycle, the physiologic condition of the patient may vary over the
course of a
CCTA acquisition process. For example, nitroglycerin may often be administered
immediately before CCFA acquisition, and may also be administered after non-
contrast
CCTA acquisitions. Because both nitroglycerin and iodinated contrast are known
to have
vasodilatory properties, the coronary lumen value will increase after
administration, to a
volume larger than the coronary lumen value in the absence of administration.
114541
Nitroglycerin-dependent coronary vasodilation is an endothelial-
dependent process. Because ischemia is preceded by endothelial dysfunction,
areas of non-
dilation may be an anatomic / physiologic indicator of coronary health. Areas
of non-
dilation may be identified, such as by comparison of the vascular morphology
pre-dilation
and post-dilation, and can be analyzed in conjunction with the atherosclerosis
characterization of the plaque in the identified areas of poor dilation.
114551
Figure 26 is a flowchart illustrating a process 2600 for analyzing a CFD-
based indication of ischemia using a characterization of atherosclerosis and
vascular
morphology. At block 2605, a system can access a plurality of images obtained
of a patient
at a medical facility. These images can be CCTA images or any other suitable
images
generated using any other suitable imaging method discussed herein or in the
attached
appendices. These images can be reflective of a portion of the cardiovascular
system of a
patient, and can be representative of at least an entire cardiac cycle. In
some embodiments,
these CCTA images may be reflective of the portion of the cardiovascular
system of a
patient both prior to and after exposure of the patient to a vasodilatory-
substance, such as
nitroglycerin or iodinated contrast. These CCTA images can be reflective of
one or more
known physiologic condition of the patient, such as an at rest state or a
hyperemic state.
114561
At block 2610, the system can perform a computational fluid dynamics
(CFD) analysis based on the plurality of CCTA images. This CFD analysis can
include the
evaluation of the CCTA images to generate a model of a portion of the
cardiovascular
system of a patient shown in the images, and can include the generation of a
three-
dimensional mesh. This CFD analysis can include assigning boundary conditions
to the
CFD model indicative of the input flow and output flow(s) at the edges of the
modeled
portion of the cardiovascular system. These boundary conditions can be
assigned at least
in part on the basis of non-invasive measurements of the patient, such as
myocardial mass,
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cardiac output, and blood pressure. These boundary conditions can also be
assigned based
on an analysis of the CCTA images,
114571
This CFD analysis may result in the determination of blood flow
characteristics of some or all of the modeled portion of the cardiovascular
system of the
patient. In some embodiments, the determined blood flow characteristics can
include some
or all of the blood flow velocity, pressure, or flow rate at various locations
throughout the
modeled portion of the cardiovascular system of the patient. In addition,
models may be
calculated based on the determined blood flow characteristics, such as an FFR
model
indicative of FFR at various locations throughout the modeled portion of the
cardiovascular
system of the patient, or shear stresses throughout the modeled portion of the
cardiovascular
system of the patient.
114581
At block 2615, the system can determine a CFD-based indication of
ischemia-causing stenosis based on the CFD analysis. This CFD-based indication
of
ischemia-causing stenosis may include, for example, the comparison of an FFR
model to a
predetermined threshold to identify regions of the FFR model at which the
calculated FFR
is below the threshold. Such an area of low FFR is indicative of a
functionally significant
lesion or stenosis upstream of the low FFR area, and can be used to identify a
severe
stenosis or otherwise diseased portion of a blood vessel as likely causing the
vessel to be
ischemic.
114591
At block 2620, the system can determine a characterization of
atherosclerosis and vascular morphology based on a plurality of CCTA images.
These
CCTA images may be the images used to perform the CFD analysis, or may be a
different
set of images. The characterization of atherosclerosis can include the
identification of the
location, volume and/or type of plaque throughout the portion of the
cardiovascular system
of the patient. In some embodiments, the determination of the characterization
of
atherosclerosis and vascular morphology can be determined prior to the CFD
analysis, and
at least some of the determined information can be used as part of the CFD
analysis, such
as in generating a geometric model of the portion of the cardiovascular system
of the
patient.
114601
At block 2525, the system can apply an algorithm that integrates the
CFD analysis and the characterization of atherosclerosis and vascular
morphology to
provide an indication of the presence and/or degree of ischemia within the
portion of the
coronary vasculature of the patient on a pixel-by-pixel basis. For example,
the algorithm
may map both the CFD-based indication of ischemia-causing stenosis and the
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characterization of atherosclerosis and vascular morphology to a common image.
As a
result, some or all of the pixels in the vessels of the analyzed portion of
the cardiovascular
system of the patient can be designated as depicting or not depicting a
functionally
significant ischemia-causing stenosis.
114611
In some embodiments, only certain of the pixels of the blood vessels
may be assigned such an indication. For example, rather than assigning a
negative
indication to certain pixels, the pixels depicting a functionally significant
ischemia-causing
stenosis, or a representative subset thereof, may be designated with such an
indicator, while
other pixels which do not depict a functionally significant ischemia-causing
stenosis are
not assigned an indication.
114621
The algorithm may make a determination, on a pixel-by-pixel basis, of
the accuracy of the CFD-based indication of the presence of an ischemia-
causing stenosis.
This determination may be made, for example, by analyzing characteristics of
the
characterized atherosclerosis and vascular morphology mapped to that pixel and
adjacent
pixels, such as those mapped to a common vessel. A determination can be made
as to
whether those characteristics are consistent with the likelihood of the
associated vessel to
be ischemic.
114631
Depending on the data available from the CCTA, additional
comparisons may be made as part of this determination. For example, where the
CCTA is
reflective of at least one complete cardiac cycle, the relative ability of a
portion of a vessel
wall to dilate and/or constrict can be analyzed in conjunction with the
atherosclerosis
characterization to provide information on the effects of the atherosclerotic
plaque on
normal coronary vasomotor function. As another example, where the CCTA is
reflective
of the cardiovascular system of the patient both before and after exposure to
a vas odilating
substance, the CCTA images can be compared to identify areas of non-dilation
or other
features, responses, or behaviors indicative of endothelial dysfunction.
114641
In some embodiments, the algorithm may make a binary yes/no
determination as to whether the CFD-based indication of the presence of an
ischemia-
causing stenosis is accurate. In other embodiments, one or both of the CFD-
based
indication of the presence of an ischemia-causing stenosis and the algorithmic
determination of agreement with that CFD-based determination may not be a
binary yes/no
decision. In some particular embodiments, one or both of the CFD-based
indication of the
presence of an ischemia-causing stenosis and the algorithmic determination of
agreement
with that CFD-based determination may be a probability assigned to a given
pixel, or a
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probabilistic modeling applied to all of the pixels that comprise a given
vessel, and to all
of the pixels that comprise the analyzed portion of the cardiovascular system
of the patient.
114651
In some embodiments, the algorithm may apply this analysis only to
those pixels for which the CFD analysis indicated the presence of an ischemia-
causing
stenosis, such that the analysis of the characterization of atherosclerosis
and vascular
morphology is performed only to filter out potential false positives. In other
embodiments,
however, the algorithm may apply this analysis to some or all of the pixels
for which there
is no indication of the presence of an ischemia-causing stenosis, to identify
potentially
ischemic vessels which might not be identified by the CFD analysis.
114661
Although described primarily with respect to coronary vessels, the
disclosed technology may be used in the analysis of other vessels elsewhere in
the body of
a patient.
Examples of embodiments relating to combining CFD-based evaluation with
atherosclerosis and vascular morphology:
114671
The following are non-limiting examples of certain embodiments of
systems and methods for CFD-based evaluation with atherosclerosis and vascular
morphology and/or other related features. Other embodiments may include one or
more
other features, or different features, that are discussed herein. Various
embodiments
described herein relate to systems, methods, and devices for medical image
analysis,
diagnosis, risk stratification, decision making and/or disease tracking. In
the embodiments
illustrated below, in some examples of other embodiments, instead of being
performed on
a "pixel" or a pixel-by-pixel basis as indicated, the embodiments relate to
analysis per
lesion, stenosis, per segment, per vessel, and/or per patient, that is, on a
lesion-by-lesion
basis, a stenosis-by-stenosis basis, a segment-by-segment basis, a vessel-by-
vessel basis,
or a patient-by-patient basis.
114681
Embodiment 1: A computer-implemented method of identifying a
presence and/or degree of ischemia via an algorithm-based medical imaging
analysis,
comprising: performing a computational fluid dynamics (CFD) analysis of a
portion of the
coronary vasculature of a patient using imaging data of the portion of the
coronary
vasculature of the patient; performing a comprehensive atherosclerosis and
vascular
morphology characterization of the portion of the coronary vasculature of the
patient using
coronary computed tomographic angiography (CCTA) of the portion of the
coronary
vasculature of the patient; and applying an algorithm that integrates the CFD
analysis and
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the atherosclerosis and vascular morphology characterization to provide an
indication of
the presence and/or degree of ischemia within the portion of the coronary
vasculature of
the patient on a pixel-by-pixel basis, the algorithm providing an indication
of the presence
and/or degree of ischemia for a given pixel based upon an analysis of the
given pixel, the
surrounding pixels, and a vessel of the portion of the coronary vasculature of
the patient
with which the pixel is associated. In other examples, instead of and/or in
addition to a
pixel-by-pixel basis, an indication of the presence and/or degree of ischemia
within the
portion of the coronary vasculature of the patient can be on a lesion-by-
lesion basis, a
stenosis-by-stenosis basis, a segment-by-segment basis, a vessel-by-vessel
basis, or a
patient-by-patient basis.
114691
Embodiment 2: The method of embodiment 1, wherein performing a
computational fluid dynamics (CFD) analysis comprises generating a model of
the portion
of the coronary vasculature of the patient based at least in part on coronary
computed
tomographic angiography (CCTA) of the portion of the coronary vasculature of
the patient.
114701
Embodiment 3: The method of embodiment 1, wherein performing a
computational fluid dynamics (CFD) analysis comprises generating a model of
the portion
of the coronary vasculature of the patient based at least in part on the
atherosclerosis and
vascular morphology characterization of the portion of the coronary
vasculature of the
patient.
114711
Embodiment 4: The method of embodiment 1, wherein performing a
computational fluid dynamics (CFD) analysis comprises computing a fractional
flow
reserve model of the portion of the coronary vasculature of the patient.
114721
Embodiment 5: The method of embodiment 1, wherein performing a
comprehensive atherosclerosis and vascular morphology characterization of the
portion of
the coronary vasculature of the patient comprises determining one or more
vascular
morphology parameters and a set of quantified plaque parameters.
114731
Embodiment 6: The method of embodiment 1, wherein performing a
computational fluid dynamics (CFD) analysis of a portion of the coronary
vasculature of a
patient comprises (i) generating a CFD-based indication of the presence and/or
degree of
ischemia within the portion of the coronary vasculature of the patient on a
pixel-by-pixel
basis, (and/or on a lesion-by-lesion basis, a stenosis-by-stenosis basis, a
segment-by-
segment basis, a vessel-by-vessel basis, or a patient-by-patient basis).
114741
Embodiment 7: The method of any one of embodiments 1-6, wherein
applying the algorithm that integrates the CFD analysis and the
atherosclerosis and vascular
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morphology characterization to provide an indication of the presence and/or
degree of
ischemia within the portion of the coronary vasculature of the patient on a
pixel-by-pixel
basis comprises providing an indication of agreement with the CFD-based
indication of the
presence and/or degree of ischemia within the portion of the coronary
vasculature of the
patient on a pixel-by-pixel basis. Instead of, or in addition to, a pixel-by-
pixel basis, this
process can be performed on a lesion-by-lesion basis, a stenosis-by-stenosis
basis, a
segment-by-segment basis, a vessel-by-vessel basis, or a patient-by-patient
basis.
[1475]
Embodiment 8: The method of any one of embodiments 1-7, wherein
applying the algorithm that integrates the CFD analysis and the
atherosclerosis and vascular
morphology characterization to provide an indication of the presence and/or
degree of
ischemia within the portion of the coronary vasculature of the patient on a
pixel-by-pixel
basis comprises analyzing variation in coronary volume, area, and/or diameter
over the
entirety of a cardiac cycle. Instead of, or in addition to, a pixel-by-pixel
basis, this process
can be performed on a lesion-by-lesion basis, a stenosis-by-stenosis basis, a
segment-by-
segment basis, a vessel-by-vessel basis, or a patient-by-patient basis.
[1476]
Embodiment 9: The method of embodiment 8, wherein analyzing
variation in coronary volume, area, and/or diameter over the entirety of a
cardiac cycle
comprises analyzing an effect of identified atherosclerotic plaque within a
wall of an artery
on the deformation of the artery.
[1477]
Embodiment 10: A computer implemented method for non-invasively
estimating blood flow characteristics to assess the severity of plaque and/or
stenotic lesions
using blood flow predictions and measurements, the method comprising:
generating and
outputting an initial indicia of a severity of the plaque or stenotic lesion
using one or more
calculated blood flow characteristics, where generating and outputting the
initial indicia of
a severity of the plaque or stenotic lesion comprises: receiving one or more
patient-specific
images and/or anatomical characteristics of at least a portion of a patient's
vasculature;
receiving images reflecting a measured blood distribution the patient's
vasculature;
projecting one or more values of the measured distribution to one or more
points of a
patient-specific anatomic model of the patient's vasculature generated using
the received
patient-specific images and/or the received anatomical thereby creating a
patient-specific
measured model indicative of the measured distribution; defining one or more
physiological and boundary conditions of a blood flow to non-invasively
simulate a
distribution of the blood flow through the patient-specific anatomic model of
the patient's
vasculature; simulating, using a processor, the distribution of the blood flow
through the
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one or more points of the patient-specific anatomic model using the defined
one or more
physiological and boundary conditions and the received patient-specific images
and/or
anatomical characteristics, thereby creating a patient-specific simulated
model indicative
of the simulated distribution; comparing, using a processor, the patient-
specific measured
model and the patient-specific simulated model to determine whether a
similarity condition
is satisfied; updating the defined physiological and boundary conditions and
re-simulating
the distribution of the blood flow through the one or more points of the
patient-specific
anatomic model until the similarity condition is satisfied; calculating, using
a processor,
one or more blood flow characteristics of blood flow through the patient-
specific anatomic
model using the updated physiological and boundary conditions; and generating
and
outputting the initial indicia of a severity of the plaque or stenotic lesion
using the one or
more blood flow characteristics of blood flow that were calculated using the
updated
physiological and boundary conditions; performing a comprehensive
atherosclerosis and
vascular morphology characterization of the portion of the patient's
vasculature using
coronary computed tomographic angiography (CCTA) of the portion of the
patient's
vasculature; and applying an algorithm that integrates the initial indicia of
a severity of the
plaque or stenotic lesion and the atherosclerosis and vascular morphology
characterization
to provide an indication of the presence and/or degree of ischemi a within the
portion of the
patient's vasculature on a pixel-by-pixel basis.
114781
Alternate Embodiment 10 using contrast agent (note: any of the
embodiments listed below that refer to "Embodiment 10" or reference Embodiment
10 are
intended to be practiced with Embodiment 10 and/or Alternate Embodiment 10): A
computer implemented method for non-invasively estimating blood flow
characteristics to
assess the severity of plaque and/or stenotic lesions using contrast
distribution predictions
and measurements, the method comprising: generating and outputting an initial
indicia of
a severity of the plaque or stenotic lesion using one or more calculated blood
flow
characteristics, where generating and outputting the initial indicia of a
severity of the plaque
or stenotic lesion comprises: receiving one or more patient-specific images
and/or
anatomical characteristics of at least a portion of a patient's vasculature;
receiving images
reflecting a measured distribution of a contrast agent delivered through the
patient's
vasculature; projecting one or more contrast values of the measured
distribution of the
contrast agent to one or more points of a patient-specific anatomic model of
the patient's
vasculature generated using the received patient-specific images and/or the
received
anatomical thereby creating a patient-specific measured model indicative of
the measured
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distribution; defining one or more physiological and boundary conditions of a
blood flow
to non-invasively simulate a distribution of the contrast agent through the
patient-specific
anatomic model of the patient's vasculature; simulating, using a processor,
the distribution
of the contrast agent through the one or more points of the patient-specific
anatomic model
using the defined one or more physiological and boundary conditions and the
received
patient-specific images and/or anatomical characteristics, thereby creating a
patient-
specific simulated model indicative of the simulated distribution; comparing,
using a
processor, the patient-specific measured model and the patient-specific
simulated model to
determine whether a similarity condition is satisfied; updating the defined
physiological
and boundary conditions and re-simulating the distribution of the contrast
agent through
the one or more points of the patient-specific anatomic model until the
similarity condition
is satisfied; calculating, using a processor, one or more blood flow
characteristics of blood
flow through the patient-specific anatomic model using the updated
physiological and
boundary conditions; and generating and outputting the initial indicia of a
severity of the
plaque or stenotic lesion using the one or more blood flow characteristics of
blood flow
that were calculated using the updated physiological and boundary conditions;
performing
a comprehensive atherosclerosis and vascular morphology characterization of
the portion
of the patient's vasculature using coronary computed tomographic angiography
(CCTA) of
the portion of the patient's vasculature; and applying an algorithm that
integrates the initial
indicia of a severity of the plaque or stenotic lesion and the atherosclerosis
and vascular
morphology characterization to provide an indication of the presence and/or
degree of
ischemia within the portion of the patient's vasculature on a pixel-by-pixel
basis.
114791
Embodiment 11: The method of embodiment 10, wherein the algorithm
provides an indication of the presence and/or degree of ischemia for a given
pixel based
upon an analysis of the given pixel, the surrounding pixels; and a vessel of
the portion of
the coronary vasculature of the patient with which the pixel is associated.
Instead of, or in
addition to, a pixel basis, this process can be performed on a lesion basis, a
stenosis basis,
a segment basis, a vessel basis, or a patient basis.
114801
Embodiment 12: The computer method of embodiments 10 or 11,
wherein; prior to simulating the distribution of the contrast agent in the
patient-specific
anatomic model for the first time, defining one or more physiological and
boundary
conditions includes finding form or functional relationships between the
vasculature
represented by the anatomic model and physiological characteristics found in
populations
of patients with a similar vascular anatomy.
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[1481]
Embodiment 13: The method of embodiments 10 or 11, wherein, prior
to simulating the distribution of the contrast agent in the patient-specific
anatomic model
for the first time, defining one or more physiological and boundary conditions
includes,
one or more of: assigning an initial contrast distribution; or assigning
boundary conditions
related to a flux of the contrast agent (i) at one or more of vessel walls,
outlet boundaries,
or inlet boundaries, or (ii) near plaque and/or stenotic lesions.
[1482]
Embodiment 14: The method of any one of embodiments 10-13,
wherein the blood flow characteristics include one or more of, a blood flow
velocity, a
blood pressure, a heart rate, a fractional flow reserve (FFR) value, a
coronary flow reserve
(CFR) value, a shear stress, or an axial plaque stress.
[1483]
Embodiment 15: The method of any one of embodiments 10-14,
wherein receiving one or more patient-specific images includes receiving one
or more
images from coronary angiography, biplane angiography, 3D rotational
angiography,
computed tomography (CT) imaging, magnetic resonance (MR) imaging, ultrasound
imaging, or a combination thereof
[1484]
Embodiment 16: The method of any one of embodiments 10-15,
wherein the patient-specific anatomic model is a reduced-order mode in the two-
dimensional anatomical domain, and wherein projecting the one or more contrast
values
includes averaging one or more contrast values over one or more cross
sectional areas of a
vessel.
[1485]
Embodiment 17: The method of any one of embodiments 10-16,
wherein the patient-specific anatomic model includes information related to
the
vasculature, including one or more of: a geometrical description of a vessel,
including the
length or diameter; a branching pattern of a vessel; one or more locations of
any stenotic
lesions, plaque, occlusions, or diseased segments; or one or more
characteristics of diseases
on or within vessels, including material properties of stenotic lesions,
plaque, occlusions,
or diseased segments.
114861
Embodiment 18: The method of any one of embodiments 10-17,
wherein the physiological conditions are measured, obtained, or derived from
computational fluid dynamics or the patient-specific anatomic model,
including, one or
more of, blood pressure flux, blood velocity flux, the flux of the contrast
agent, baseline
heart rate, geometrical and material characteristics of the vasculature, or
geometrical and
material characteristics of plaque and/or stenotic lesions; and wherein the
boundary
conditions define physiological relationships between variables at boundaries
of a region
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of interest, the boundaries including, one or more of, inflow boundaries,
outflow
boundaries, vessel wall boundaries, or boundaries of plaque and/or stenotic
lesions.
[1487]
Embodiment 19: The method of any one of embodiments 10-18,
wherein simulating, using the processor, the distribution of the contrast
agent for the one
or more points in the patient-specific anatomic model using the defined one or
more
physiological and boundary conditions includes one or more of: determining
scalar
advection-diffusion equations governing the transport of the contrast agent in
the patient-
specific anatomic model, the equations governing the transport of the contrast
agent
reflecting any changes in a ratio of flow to lumen area at or near a stenotic
lesion or plaque;
or computing a concentration of the contrast agent for the one or more points
of the patient-
specific anatomic model, wherein computing the concentration requires
assignment of an
initial contrast distribution and initial physiological and boundary
conditions.
[1488]
Embodiment 20: The method of any one of embodiments 10-19,
wherein satisfying a similarity condition comprises: specifying a tolerance
that can measure
differences between the measured distribution of the contrast agent and the
simulated
distribution of the contrast agent, prior to simulating the distribution of
the contrast agent;
and determining whether the difference between the measured distribution of
the contrast
agent and the simulated distribution of the contrast agent falls within the
specified
tolerance, the similarity condition being satisfied if the difference falls
within the specified
tolerance.
[1489]
Embodiment 21: The method of any one of embodiments 10-20,
wherein updating the defined physiological and boundary conditions and re-
simulating the
distribution of the contrast agent includes mapping a concentration of the
contrast agent
along vessels with one or more of: features derived from an analytic
approximation of an
advection-diffusion equation describing the transport of fluid in one or more
vessels of the
patient-specific anatomic model; features describing the geometry of the
patient-specific
anatomic model, including, one or more of, a lumen diameter of a plaque or
stenotic lesion,
a length of a segment afflicted with a plaque or stenotic lesion, a vessel
length, or the area
of a plaque or stenotic lesion; or features describing a patient-specific
dispersivity of the
contrast agent.
[1490]
Embodiment 22: The method of any one of embodiments 10-21,
wherein updating the defined physiological and boundary conditions and re-
simulating the
distribution of the contrast agent includes using one or more of a derivative-
free
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optimization based on nonlinear ensemble filtering, or a gradient-based
optimization that
uses finite difference or adjoint approximation.
114911
Embodiment 23: The method of any one of embodiments 10-22, further
comprising: upon a determination that the measured distribution of the
contrast agent and
the simulated distribution of the contrast agent satisfy the similarity
condition, enhancing
the received patient-specific images using the simulated distribution of the
contrast agent;
and outputting the enhanced images as one or more medical images to an
electronic storage
medium or display.
114921
Embodiment 24: The method of embodiment 23, wherein enhancing the
received patient-specific images comprises one or more of: replacing pixel
values with the
simulated distribution of the contrast agent; or using the simulated
distribution of the
contrast agent to de-noise the received patient-specific images via a
conditional random
field.
114931
Embodiment 25: The method of any one of embodiments 10-24, further
comprising: upon a determination that the measured distribution of the
contrast agent and
the simulated distribution of the contrast agent satisfies the similarity
condition, using the
calculated blood flow characteristics associated with the simulated
distribution of the
contrast agent to simulate perfusion of blood in one or more areas of the
patient-specific
anatomic model; generating a model or medical image representing the perfusion
of blood
in one or more areas of the patient-specific anatomic model; and outputting
the model or
medical image representing the perfusion of blood in one or more areas of the
patient-
specific anatomic model to an electronic storage medium or display.
114941
Embodiment 26: The method of any one of embodiments 10-25,
wherein the patient-specific anatomic model is represented in a three-
dimensional
anatomical domain, and wherein projecting the one or more contrast values
includes
assigning contrast values for each point of a three-dimensional finite element
mesh.
114951
Embodiment 27: The method of any one of embodiments 10-26,
wherein performing a comprehensive atherosclerosis and vascular morphology
characterization of the portion of the patient's vasculature using coronary
computed
tomographic angiography (CCTA) of the portion of the patient's vasculature
comprises:
generating image information for the patient, the image information including
image data
of computed tomography (CT) scans along a vessel of the patient, and
radiodensity values
of coronary plaque and radiodensity values of perivascular tissue located
adjacent to the
coronary plaque; and determining, using the image information of the patient,
coronary
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plaque information of the patient, wherein determining the coronary plaque
information
comprises quantifying, using the image information, radiodensity values in a
region of
coronary plaque of the patient, quantifying, using the image information,
radiodensity
values in a region of perivascular tissue adj acent to the region of coronary
plaque of the
patient, and generating metrics of coronary plaque of the patient using the
quantified
radiodensity values in the region of coronary plaque and the quantified
radiodensity values
in the region of perivascular tissue adjacent to the region of coronary
plaque.
114961
Embodiment 28: The method of embodiment 27, further comprising:
accessing a database of coronary plaque information and characteristics of
other people,
the coronary plaque information in the database including metrics generated
from
radiodensity values of a region of coronary plaque in the other people and
radiodensity
values of perivascular tissue adjacent to the region of coronary plaque in the
other people,
and the characteristics of the other people including information at least of
age, sex, race,
diabetes, smoking, and prior coronary artery disease; and characterizing the
coronary
plaque information of the patient by comparing the metrics of the coronary
plaque
information and characteristics of the patient to the metrics of the coronary
plaque
information of other people in the database having one or more of the same
characteristics,
wherein characterizing the coronary plaque information includes identifying
the coronary
plaque as a high risk plaque.
114971
Embodiment 29: The method of embodiment 28, wherein characterizing
the coronary plaque comprises identifying the coronary plaque as a high risk
plaque if it is
likely to cause ischemia based on a comparison of the coronary plaque
information and
characteristics of the patient to the coronary plaque information and
characteristics of the
other people in the database.
114981
Embodiment 30: The method of embodiment 29, wherein the
characterization of coronary plaque as high risk plaque is used to provide an
indication of
the presence and/or degree of ischemia within a portion of the patient's
vasculature in at
least one pixel adjacent the coronary plaque.
114991
Embodiment 31: The method of embodiment 28, wherein characterizing
the coronary plaque comprises identifying the coronary plaque as a high risk
plaque if it is
likely to cause vasospasm based on a comparison of the coronary plaque
information and
characteristics of the patient to the coronary plaque information and
characteristics of the
other people in the database.
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115001
Embodiment 32: The method of embodiment 28, wherein characterizing
the coronary plaque comprises identifying the coronary plaque as a high risk
plaque if it is
likely to rapidly progress based on a comparison of the coronary plaque
information and
characteristics of the patient to the coronary plaque information and
characteristics of the
other people in the database.
115011
Embodiment 33: The method of any one of embodiments 10-32,
wherein generating metrics using the quantified radiodensity values in the
region of
coronary plaque and the quantified radiodensity values in a region of
perivascular tissue
adjacent to the region of the patient comprises determining, along a line, a
slope value of
the radiodensity values of the coronary plaque and a slope value of the
radiodensity values
of the perivascular tissue adjacent to the coronary plaque.
115021
Embodiment 34: The method of embodiment 33, wherein generating
metrics further comprises determining a ratio of the slope value of the
radiodensity values
of the coronary plaque and a slope value of the radiodensity values of the
perivascular tissue
adjacent to the coronary plaque.
115031
Embodiment 35: The method of any one of embodiments 10-34,
wherein generating metrics using the quantified radiodensity values in the
region of
coronary plaque and the quantified radiodensity values in a region of
perivascular tissue
adjacent to the region of the patient comprises generating, using the image
information, a
ratio between quantified radiodensity values of the coronary plaque and
quantified
radiodensity values of the corresponding perivascular tissue.
115041
Embodiment 36: The method of any one of embodiments 10-35,
wherein the perivascular tissue is perivascular fat, and generating metrics
using the
quantified radiodensity values in the region of coronary plaque and the
quantified
radiodensity values in the region of perivascular tissue adjacent to the
region of coronary
plaque of the patient comprises generating a ratio of a density of the
coronary plaque and a
density of the perivascular fat.
115051
Embodiment 37: The method of any one of embodiments 10-35,
wherein the perivascular tissue is a coronary artery, and generating metrics
using the
quantified radiodensity values in the region of coronary plaque and the
quantified
radiodensity values in the region of perivascular tissue adjacent to the
region of coronary
plaque of the patient comprises generating a ratio of a density of the
coronary plaque and a
density of the coronary artery.
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[1506]
Embodiment 38: The method of embodiment 37, wherein generating the
ratio comprises generating the ratio of a maximum radiodensity value of the
coronary
plaque and a maximum radiodensity value of the perivascular fat.
[1507]
Embodiment 39: The method of embodiment 37, wherein generating the
ratio comprises generating a ratio of a minimum radiodensity value of the
coronary plaque
and a minimum radiodensity value of the perivascular fat.
[1508]
Embodiment 40: The method of embodiment 37, wherein generating the
ratio comprises generating a ratio of a maximum radiodensity value of the
coronary plaque
and a minimum radiodensity value of the perivascular fat.
[1509]
Embodiment 41: The method of embodiment 37, wherein generating the
ratio comprises generating a ratio of a minimum radiodensity value of the
coronary plaque
and a maximum radiodensity value of the perivascular fat.
Individualized / Subject-Specific CAD Risk Factor Goals
[1510]
Various embodiments described herein relate to systems, methods, and
devices for determining individualized and/or patient or subject-specific
coronary artery
disease (CAD) risk factor goals from image-based phenotyping of
atherosclerosis. In
particular, in some embodiments, the systems, methods, and devices are
configured to
analyze a medical image of a subject comprising one or more arteries and
analyze the same
to perform quantitative phenotyping of atherosclerosis or plaque. For example,
quantitative
phenotyping can comprise determination of atherosclerosis burden or volume,
type,
composition, rate of progression or stabilization, and/or the like. In some
embodiments,
the systems, methods, and devices described herein can be configured to
correlate the
phenotyping of atherosclerosis to a CAD risk factor level of the subject to
determine an
individualized and/or subject or patient-specific CAD risk factor goal for
that particular
subject. For example, a CAD risk factor goal can be based on LDL or other
cholesterol
level, blood pressure, diabetes, tobacco usage, inflammation level, and/or the
like. As such,
in some embodiments this approach of personalized phenotyping for risk factor
goals can
allow for development of specific treatment targets on a person-by-person
basis in a manner
that can reduce ASCVD events that has not been done to date.
[1511]
Traditionally, coronary artery disease (CAD) prevention has relied upon
the use of surrogate markers of CAD that have, in population-based studies,
generally been
associated with increased CAD events, such as myocardial infarction and sudden
coronary
death. These surrogate markers of CAD can include cholesterol, blood pressure,
diabetes
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mellitus, tobacco use, and family history of premature CAD, amongst others.
However,
while these approaches can be somewhat effective in discriminating different
populations
at risk, they tend to show significantly reduced efficacy for pinpointing
individuals who
will experience future heart attacks and other atherosclerotic cardiovascular
disease
(ASCVD) events. Indeed, certain prior studies have demonstrated that the
coronary lesions
that are responsible for heart attacks can be missed by sole reliance of
elevated cholesterol
levels in up to 80% of individuals who will suffer heart attack. Further,
tracking of risk
factors, e.g., cholesterol levels, following administration of medical therapy
with such
agents as statin medications can miss 75% of individuals who retain -residual
risk" despite
effective cholesterol lowering and medical treatment. These findings highlight
the need
for more effective measures of CAD that can be effectively tracked and used to
determine
personalized goals of treatment on an individual, patient-by-patient, or
subject-by-subject
basis.
[1512]
An additional limitation to traditional CAD risk factors is that it can be
more than the presence or absence of a risk factor that connotes risk of
future ASCVD
events. Indeed, the presence, extent, severity, duration, treatment, and
treatment response
can all contribute together to whether a specific CAD risk factor may
influence the coronary
arteries in a deleterious manner, either alone or in combination with other
CAD risk factors.
Finally, there are likely an array of unobserved (and heretofore unknown
variables) that
may contribute to CAD events, including psychosocial, metabolic, inflammatory,
environmental, and/or genetic causes.
[1513]
Thus, there is an urgent unmet need to identify more precise and/or
individualized measures of CAD risk, particularly one that can integrate the
lifetime
exposure and treatment effects to the overall manifestation of CAD. To date,
there has not
been a singular metric that incorporates all of these factors into a single
disease metric that
can be used to diagnose, prognosticate risk, guide therapy selection and most
importantly,
provide goals for determining need of additional therapy or adequacy of
current therapies.
[1514]
As such, in some embodiments, the systems, devices, and methods
described herein are configured to address one or more of the shortcomings
described
above. In particular, in some embodiments, the systems, devices, and methods
described
herein are configured to incorporate one or more of such CAD risk factors
described above
to generate a metric or measure of patient-specific CAD risk. In some
embodiments, the
systems, methods, and devices described herein are configured to correlate one
or more
such CAD risk factors with a current disease or plaque state of a subject to
determine a
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personalized CAD risk factor goal. For example, rather than setting the same
cholesterol
or other CAD risk factor goal for everyone, which may not be an accurate
measure of
plaque, atherosclerosis, or disease, some embodiments described herein are
configured to
determine a patient or subject-specific, personalized CAD risk factor goal,
such as a
cholesterol level goal, that more accurately tracks the state of plaque,
atherosclerosis, or
disease. More specifically, in some embodiments, the systems, methods, and
devices
described herein can be configured to analyze the state of plaque,
atherosclerosis, or disease
of a subject and correlate the same to one or more CAD risk factors, such as
cholesterol,
which can then be used to determine a personalized CAD risk factor goal for
the subject
which is specifically derived for that subject and has more meaningful
correlation to the
state of disease for that individual. Further, based on one or more such
analyses, in some
embodiments, the systems, devices, and methods described herein can be used to
diagnose,
prognosticate risk, guide therapy selection, and provide goals for determining
need of
additional therapy or adequacy of current therapies.
[1515]
As discussed herein, in some embodiments, the systems, devices, and
methods are configured to determine patient-specific coronary artery disease
(CAD) risk
factor goals from image-based quantified phenotyping of atherosclerosis of
plaque, which
can include for example quantification and characterization of coronary
atherosclerosis
burden, type, and/or rate of progression. In particular, in some embodiments,
systems,
methods, and devices described herein allow for determining individualized
therapeutic
goals for CAD risk factor control that are disease phenotype-based (e.g.,
burden, type,
and/or rate of progression of disease). In some embodiments, this approach of
personalized
phenotyping for risk factor goals allows for development of specific treatment
targets on a
person-by-person basis in a manner that can reduce ASCVD events that has not
been done
to date.
[1516]
Figure 27 is a block diagram illustrating an example embodiment(s) of
systems, devices, and methods for determining patient-specific and/or subject-
specific
coronary artery disease (CAD) risk factor goals from image-based quantified
pheno-typing
of atherosclerosis.
[1517]
As illustrated in Figure 27, in some embodiments, the system can be
configured to access and/or determine the level of a CAD risk factor of an
individual,
subject, or patient at block 2702. For example, in some embodiments, the CAD
risk factor
can comprise low-density lipoprotein (LDL) cholesterol, high-density
lipoprotein (HDL)
cholesterol level, cholesterol particle size and fluffiness, other measures
and/or types of
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cholesterol, inflammation, glycosylated hemoglobin, blood pressure, and/or the
like. In
some embodiments, the CAD risk factor can include any other factor that is
used to
diagnose and/or correlate with CAD.
[1518]
In some embodiments, the system can be configured to access a medical
image of the individual, subject, or patient at block 2704. In some
embodiments, the
medical image can include one or more arteries, such as coronary, carotid,
aorta, lower
extremity, and/or other arteries of the subject. In some embodiments, the
medical image
can be stored in a medical image database 2706. In some embodiments, the
medical image
database 2706 can be locally accessible by the system and/or can be located
remotely and
accessible through a network connection. The medical image can comprise an
image
obtained by one or more modalities, such as computed tomography (CT), contrast-
enhanced CT, non-contrast CT, x-ray, ultrasound, echocardiography,
intravascular
ultrasound (IVUS), MR imaging, optical coherence tomography (OCT), nuclear
medicine
imaging, positron-emission tomography (PET), single photon emission computed
tomography (SPECT), and/or near-field infrared spectroscopy (NIRS). In some
embodiments, the medical image comprises one or more of a contrast-enhanced CT
image,
non-contrast CT image, MR image, and/or an image obtained using any of the
modalities
described above.
[1519]
In some embodiments, at block 2708, the system can be configured to
perform quantitative phenotyping of atherosclerosis for the individual,
subject, or patient.
For example, in some embodiments, the quantitative phenotyping can be of
atherosclerosis
burden, volume, type, composition, and/or rate of progression for the
individual or patient.
In some embodiments, the system can be configured to utilize one or more image
processing, artificial intelligence (Al), and/or machine learning (ML)
algorithms to
automatically and/or dynamically perform quantitative phenotyping of
atherosclerosis. For
example, in some embodiments, the system can be configured to automatically
and/or
dynamically identify one or more arteries, vessels, and/or a portion thereof
on the medical
image, identify one or more regions of plaque, and/or perform quantitative
phenotyping of
plaque.
[1520]
In some embodiments, as part of quantitative phenotyping, the system
can be configured to identify and/or characterize different types and/or
regions of plaque,
for example based on density, absolute density, material density, relative
density, and/or
radiodensity. For example, in some embodiments, the system can be configured
to
characterize a region of plaque into one or more sub-types of plaque. For
example, in some
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embodiments, the system can be configured to characterize a region of plaque
as one or
more of low density non-calcified plaque, non-calcified plaque, or calcified
plaque. In
some embodiments, calcified plaque can correspond to plaque having a highest
density
range, low density non-calcified plaque can correspond to plaque having a
lowest density
range, and non-calcified plaque can correspond to plaque having a density
range between
calcified plaque and low density non-calcified plaque. For example, in some
embodiments,
the system can be configured to characterize a particular region of plaque as
low density
non-calcified plaque when the radiodensity of an image pixel or voxel
corresponding to
that region of plaque is between about -189 and about 30 Hounsfield units
(HU). In some
embodiments, the system can be configured to characterize a particular region
of plaque as
non-calcified plaque when the radiodensity of an image pixel or voxel
corresponding to
that region of plaque is between about 31 and about 350 HU. In some
embodiments, the
system can be configured to characterize a particular region of plaque as
calcified plaque
when the radiodensitv of an image pixel or voxel corresponding to that region
of plaque is
between about 351 and about 2500 HU.
115211
In some embodiments, the lower and/or upper Hounsfield unit boundary
threshold for determining whether a plaque corresponds to one or more of low
density non-
calcified plaque, non-calcified plaque, and/or calcified plaque can be about -
1000 HU,
about -900 HU, about -800 HU. about -700 HU, about -600 HU, about -500 HU,
about -
400 HU, about -300 HU, about -200 HU, about -190 HU, about -180 HU, about -170
HU,
about -160 HU, about -150 HU, about -140 HU, about -130 HU, about -120 HU,
about -
110 HU, about -100 HU, about -90HU, about -80 HU, about -70 HU, about -60 HU,
about
-50 HU, about -40 HU, about -30 HU, about -20 HU, about -10 HU, about 0 HU,
about 10
HU, about 20 HU, about 30 HU, about 40 HU, about 50 HU, about 60 HU, about 70
HU,
about 80 HU, about 90 HU, about 100 HU, about 110 HU, about 120 HU, about 130
HU,
about 140 HU, about 150 HU, about 160 HU, about 170 HU, about 180 HU, about
190 HU,
about 200 HU, about 210 HU, about 220 HU, about 230 HU, about 240 HU, about
250 HU,
about 260 HU, about 270 HU, about 280 HU, about 290 HU. about 300 HU, about
310 HU,
about 320 HU, about 330 HU, about 340 HU, about 350 HU, about 360 HU, about
370 HU,
about 380 HU, about 390 HU, about 400 HU, about 410 HU, about 420 HU, about
430 HU,
about 440 HU, about 450 HU, about 460 HU, about 470 HU, about 480 HU, about
490 HU,
about 500 HU, about 510 HU, about 520 HU, about 530 HU, about 540 HU, about
550 HU,
about 560 HU, about 570 HU, about 580 HU, about 590 HU. about 600 HU, about
700 HU,
about 800 HU, about 900 HU, about 1000 HU, about 1100 HU, about 1200 HU, about
1300
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HU, about 1400 HU, about 1500 HU, about 1600 HU, about 1700 HU, about 1800 HU,
about 1900 HU, about 2000 HU, about 2100 HU, about 2200 HU, about 2300 HU,
about
2400 HU, about 2500 HU, about 2600 HU, about 2700 HU, about 2800 HU, about
2900
HU, about 3000 HU, about 3100 HU, about 3200 HU, about 3300 HU, about 3400 HU,
about 3500 HU, and/or about 4000 HU.
115221
In some embodiments, the system can be configured to determine and/or
characterize the burden of atherosclerosis based at least part on volume of
plaque. In some
embodiments, the system can be configured to analyze and/or determine total
volume of
plaque and/or volume of low-density non-calcified plaque, non-calcified
plaque, and/or
calcified plaque. In some embodiments, the system can be configured to perform
phenotyping of plaque by determining a ratio of one or more of the foregoing
volumes of
plaque, for example within an artery, lesion, vessel, and/or the like.
115231
In some embodiments, the system can be configured to analyze the
progression of plaque. For example, in some embodiments, the system can be
configured
to analyze the progression of one or more particular regions of plaque and/or
overall
progression and/or lesion and/or artery-specific progression of plaque. In
some
embodiments, in order to analyze the progression of plaque, the system can be
configured
to analyze one or more serial images of the subject for phenotyping
atherosclerosis. In
some embodiments, tracking the progression of plaque can comprise analyzing
changes
and/or lack thereof in total plaque volume and/or volume of low-density non-
calcified
plaque, non-calcified plaque, and/or calcified plaque. In some embodiments,
tracking the
progression of plaque can comprise analyzing changes and/or lack thereof in
density of a
particular region of plaque and/or globally.
115241
In some embodiments, at block 2710, the system can be configured to
determine a correlation of the baseline risk factor level of the subject with
the quantitative
phenotyping of atherosclerosis. In some embodiments, the system can be
configured to
utilize one or more multivariable regression analyses, artificial intelligence
(Al), and/or
machine learning (ML) algorithms to automatically and/or dynamically determine
a
correlation between the risk factor level of the subject with results of
quantitative
phenotyping of atherosclerosis. For example, there can be a correlation
between one or
more quantitative plaque phenotyping variables and one or more CAD risk level
factors.
Such correlation can be subject-dependent, meaning that such correlation can
be different
and/or the same among different subjects. In some embodiments, the system can
utilize an
Al and/or ML algorithm trained on a plurality of subject data sets with known
one or more
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quantitative plaque phenotyping variables and one or more CAD risk level
factors to
determine one or more distinct patterns which can be applied to a new subject.
115251
Generally speaking, even if two people have the exact same quantified
plaque phenotyping, whether based on volume, composition, rate of progression,
and/or the
like, they can still show different CAD risk factor levels, such as for
example different LDL
cholesterol levels. As such, subjecting everyone to the same CAD risk factor
level goal,
such as for example a particular LDL cholesterol level, may not have the same
desired
effect on atherosclerosis which can be thought of as the actual disease. As
such, some
systems, devices, and methods described herein provide for individualized,
subject-specific
CAD risk factor goals that will actually have a meaningful impact on
atherosclerosis and
risk of CAD. In particular, it can be advantageous to maintain the total
amount or volume
of plaque while hardening existing plaque, for example by changing more low-
density non-
calcified plaque and/or non-calcified plaque into calcified plaque. By being
able to
estimate how a change in a particular CAD risk factor level will actually
affect a quantified
plaque measure or variable for a subject, in some embodiments, the system can
be used to
generate and/or facilitate generation of effective patient-specific or subject-
specific
treatment(s).
115261
As discussed herein, in some embodiments, one or more quantified
atherosclerosis phenotyping and/or measures and/or variables can be correlated
to one or
more CAD risk factor levels of a particular subject. In some embodiments, the
system can
be configured to access a reference values database 2716 to facilitate
determination of such
correlation. In some embodiments, the reference values database 2716 can be
locally
accessible by the system and/or can be located remotely and accessible through
a network
connection. In some embodiments, the reference values database 2716 can
comprise a
plurality of CAD risk factor levels and/or quantified atherosclerosis
phenotyping derived
from a plurality of subjects, from which the system can be configured to
determine the
correlation between one or more quantified atherosclerosis phenotyping and one
or more
CAD risk factors for the subject. In some embodiments, the system can be
configured to
utilize such correlation to estimate the effect of how much a particular
change in a particular
CAD risk level factor will affect a particular quantified atherosclerosis
phenotyping for that
subject.
115271
In some embodiments, at block 2712, the system can be configured to
determine a threshold and/or thresholds of one or more quantitative
atherosclerosis
phenotyping measures or variables that will cause the subj ect to be
considered to have
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elevated and/or normal risk of CAD. For example, in some embodiments, one or
more
threshold values of one or more quantitative phenotyping measures or variables
can be tied
to normal, low, medium, or high risk of CAD. In some embodiments, one or more
threshold
values of one or more quantitative phenotyping measures or variables can be
tied to a
percentage and/or normal distribution of risk of CAD among a wider population,
such as
for example the average, 75th percentile, 90th percentile, and/or the like. In
some
embodiments, the percentage and/or normal distribution of CAD risk can be for
asymptomatic and/or symptomatic population at large and/or for an age and/or
gender
group of the subject and/or other group determined by another clinical factor.
115281
In some embodiments, the system can be configured to determine a
threshold that is specific for that particular individual or patient, rather
than one that applies
to the population at large. In some embodiments, the determined threshold can
be
applicable to a number or a group of individuals, for example of those that
share one or
more common characteristics. For example, for a particular subject, the system
can
determine that a particular volume of total plaque, non-calcified plaque, low-
density non-
calcified plaque, calcified plaque, and/or a ratio or thereof corresponds to a
particular
elevated or normal risk of CAD for the subject. In doing so, in some
embodiments, the
system can be configured to access the reference values database 2716. In some
embodiments, the system can be configured to utilize one or more Al and/or ML
algorithms
to determine one or more subject-specific thresholds of one or more
quantitative
phenotyping of atherosclerosis to lower the risk of CAD for the subject.
115291
In some embodiments, at block 2714, the system can be configured to
set or determine a CAD risk factor level goal for the individual or patient,
for example
based on the determined one or more thresholds of quantitative phenotyping of
atherosclerosis. As discussed herein, in some embodiments, the determined CAD
risk
factor goal can be individualized and/or patient-specific. For example, in
some
embodiments, the system can be configured to set a patient-specific or subject-
specific LDL
cholesterol goal for that individual that is expected to lower one or more
quantified
atherosclerosis phenotyping to a desired level. In some embodiments, the
system can be
configured to access the reference values database 2716 in determining a
subject-specific
CAD risk factor level goal. in some embodiments, the system can be configured
to utilize
one or more Al and/or ML algorithms to determine a subject-specific CAD risk
factor level
goal.
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115301
In some embodiments, at block 2718, the system can be configured
determine a proposed treatment for the individual based on the set risk factor
goal, which
can be used to treat the patient. For example, in some embodiments, the system
can
generate a proposed treatment for treating the patient to an LDL cholesterol
level that is
associated with normal or low atherosclerosis burden, type, and/or rate of
progression
and/or any other type of quantified phenotyping of atherosclerosis. In some
embodiments,
the system can be configured to access a risk / treatment database 2720 in
determining a
proposed treatment for the subject. In some embodiments, the risk / treatment
database
2720 can be locally accessible by the system and/or can be located remotely
and accessible
through a network connection. In some embodiments, the risk / treatment
database 2720
can comprise a plurality of treatments that were given to patients for
lowering risk of CAD,
with or without longitudinal treatment results, and/or one or more quantified
atherosclerosis
phenotyping variables and/or one or more CAD risk factor level data. In some
embodiments, the system can be configured to utilize one or more Al and/or ML
algorithms
to determine a subject-specific proposed treatment for lowering risk of CAD.
In some
embodiments, the proposed treatment can include one or more of medical
intervention,
such as a stent implantation or other procedure, medical treatment, such as
prescription of
statins or some other pharmaceutical, and/or lifestyle change, such as
exercise or dietary
changes.
115311
In some embodiments, as atherosclerosis burden, volume, composition,
type, and/or rate of progression may be dynamic, the system can be configured
to perform
serial quantified phenotyping of atherosclerosis and re-calibrate and/or
update the threshold
of a risk factor for the patient, such as for example LDL. As such, in some
embodiments,
in some embodiments, the system can be configured to repeat one or more
processes
described in relation to blocks 2702-2720.
115321
As such, in some embodiments, the systems, devices, and methods
described herein can be configured to leverage quantitative disease
phenotyping to
determine individual thresholds of risk factor control vs. lack of control.
Further, in some
embodiments, armed with this information, treatment targets for risk factors
can be custom-
made for individuals rather than relying on population-based estimates that
average across
a group of individuals.
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Computer System
115331
In some embodiments, the systems, processes, and methods described
herein are implemented using a computing system, such as the one illustrated
in Figure
27B. The example computer system 2730 is in communication with one or more
computing
systems 2750 and/or one or more data sources 2752 via one or more networks
2748. While
Figure 27B illustrates an embodiment of a computing system 2730, it is
recognized that the
functionality provided for in the components and modules of computer system
2730 can be
combined into fewer components and modules, or further separated into
additional
components and modules.
115341
The computer system 2730 can comprise Patient-Specific Risk Factor
Goal Determination and/or Tracking Module 2744 that carries out the functions,
methods,
acts, and/or processes described herein. The Patient-Specific Risk Factor Goal
Determination and/or Tracking Module 2744 is executed on the computer system
2730 by
a central processing unit 2736 discussed further below.
115351
In general the word "module,- as used herein, refers to logic embodied
in hardware or firmware or to a collection of software instructions, having
entry and exit
points. Modules are written in a program language, such as JAVA, C, or C++, or
the like.
Software modules can be compiled or linked into an executable program,
installed in a
dynamic link library, or can be written in an interpreted language such as
BASIC, PERL,
LAU, PHP or Python and any such languages. Software modules can be called from
other
modules or from themselves, and/or can be invoked in response to detected
events or
interruptions. Modules implemented in hardware include connected logic units
such as
gates and flip-flops, and/or can include programmable units, such as
programmable gate
arrays or processors.
115361
Generally, the modules described herein refer to logical modules that
can be combined with other modules or divided into sub-modules despite their
physical
organization or storage. The modules are executed by one or more computing
systems, and
can be stored on or within any suitable computer readable medium, or
implemented in-
whole or in-part within special designed hardware or firmware. Not all
calculations,
analysis, and/or optimization require the use of computer systems, though any
of the above-
described methods, calculations, processes, or analyses can be facilitated
through the use
of computers. Further, in some embodiments, process blocks described herein
can be
altered, rearranged, combined, and/or omitted.
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115371
The computer system 2730 includes one or more processing units (CPU)
2736, which can comprise a microprocessor. The computer system 2730 further
includes
a physical memory 2740, such as random access memory (RAM) for temporary
storage of
information, a read only memory (ROM) for permanent storage of information,
and a mass
storage device 2734, such as a backing store, hard drive, rotating magnetic
disks, solid state
disks (S SD), flash memory, phase-change memory (PCM), 3D XPoint memory,
diskette,
or optical media storage device. Alternatively, the mass storage device can be
implemented
in an array of servers. Typically, the components of the computer system 2730
are
connected to the computer using a standards based bus system. The bus system
can be
implemented using various protocols, such as Peripheral Component Interconnect
(PCI),
Micro Channel, SCSI, Industrial Standard Architecture (ISA) and Extended ISA
(EISA)
architectures.
115381
The computer system 2730 includes one or more input/output (I/O)
devices and interfaces 2742, such as a keyboard, mouse, touch pad, and
printer. The I/O
devices and interfaces 2742 can include one or more display devices, such as a
monitor,
that allows the visual presentation of data to a user. More particularly, a
display device
provides for the presentation of GUIs as application software data, and multi-
media
presentations, for example. The I/O devices and interfaces 2742 can also
provide a
communications interface to various external devices. The computer system 2730
can
comprise one or more multi-media devices 208, such as speakers, video cards,
graphics
accelerators, and microphones, for example.
Computing System Device / Operating System
115391
The computer system 2730 can run on a variety of computing devices,
such as a server, a Windows server, a Structure Query Language server, a Unix
Server, a
personal computer, a laptop computer, and so forth. In other embodiments, the
computer
system 2730 can run on a cluster computer system, a mainframe computer system
and/or
other computing system suitable for controlling and/or communicating with
large
databases, performing high volume transaction processing, and generating
reports from
large databases. The computing system 2730 is generally controlled and
coordinated by an
operating system software, such as z/OS, Windows, Linux, UNIX, BSD, PHP,
SunOS,
Solaris, MacOS, ICloud services or other compatible operating systems,
including
proprietary operating systems. Operating systems control and schedule computer
processes
for execution, perform memory management, provide file system, networking, and
I/O
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services, and provide a user interface, such as a graphical user interface
(GUI), among other
things.
Network
115401
The computer system 2730 illustrated in FIG. 27B is coupled to a
network 2748, such as a LAN, WAN, or the Internet via a communication link
2746 (wired,
wireless, or a combination thereof). Network 2748 communicates with various
computing
devices and/or other electronic devices. Network 2748 is communicating with
the one or
more computing systems 2750 and the one or more data sources 2752. The Patient-
Specific
Risk Factor Goal Determination and/or Tracking Module 2744 can access or can
be
accessed by computing systems 2750 and/or data sources 2752 through a web-
enabled user
access point. Connections can be a direct physical connection, a virtual
connection, and
other connection type. The web-enabled user access point can comprise a
browser module
that uses text, graphics, audio, video, and other media to present data and to
allow
interaction with data via the network 218.
115411
The output module can be implemented as a combination of an all-points
addressable display such as a cathode ray tube (CRT), a liquid crystal display
(LCD), a
plasma display, or other types and/or combinations of displays. The output
module can be
implemented to communicate with input devices 2742 and they also include
software with
the appropriate interfaces which allow a user to access data through the use
of stylized
screen elements, such as menus, windows, dialogue boxes, tool bars, and
controls (for
example, radio buttons, check boxes, sliding scales, and so forth).
Furthermore, the output
module can communicate with a set of input and output devices to receive
signals from the
user.
Other Systems
115421
The computing system 2730 can include one or more internal and/or
external data sources (for example, data sources 2752). In some embodiments,
one or more
of the data repositories and the data sources described above can be
implemented using a
relational database, such as DB2, Sybase, Oracle, CodeBase, and Microsoft(it)
SQL Server
as well as other types of databases such as a flat-file database, an entity
relationship
database, and object-oriented database, and/or a record-based database.
115431
The computer system 2730 can also access one or more data sources (or
databases) 2752. The databases 2752 can be stored in a database or data
repository. The
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computer system 2730 can access the one or more databases 2752 through a
network 2748
or can directly access the database or data repository through I/O devices and
interfaces
2742. The data repository storing the one or more databases 2752 can reside
within the
computer system 2730.
URLs and Cookies
115441
In some embodiments, one or more features of the systems, methods,
and devices described herein can utilize a URL and/or cookies; for example for
storing
and/or transmitting data or user information. A Uniform Resource Locator (URL)
can
include a web address and/or a reference to a web resource that is stored on a
database
and/or a server. The URL can specify the location of the resource on a
computer and/or a
computer network. The URL can include a mechanism to retrieve the network
resource. The source of the network resource can receive a URL, identify the
location of
the web resource, and transmit the web resource back to the requestor. A URL
can be
converted to an IP address, and a Doman Name System (DNS) can look up the URL
and
its corresponding IP address. URLs can be references to web pages, file
transfers, emails,
database accesses, and other applications. The URLs can include a sequence of
characters
that identify a path, domain name, a file extension, a host name, a query, a
fragment,
scheme, a protocol identifier, a port number, a usemame, a password, a flag,
an object, a
resource name and/or the like. The systems disclosed herein can generate,
receive,
transmit, apply, parse, serialize, render, and/or perform an action on a URL.
Examples of embodiments relating to determining patient specific risk factor
goals from
image-based quantification:
[1545]
The following are non-limiting examples of certain embodiments of
systems and methods for determining patient specific risk factor goals and/or
other related
features. Other embodiments may include one or more other features, or
different features,
that are discussed herein.
[1546]
Embodiment 1: A computer-implemented method for determining
patient-specific coronary artery disease (CAD) risk factor goals based on
quantification of
coronary atherosclerosis and vascular morphology features using non-invasive
medical
image analysis, the method comprising: accessing, by a computer system, a CAD
risk factor
level for a subject; accessing, by the computer system, a medical image of the
subject, the
medical image comprising one or more coronary arteries; analyzing, by the
computer
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system, the medical image of the subject to perform quantitative phenotyping
of
atherosclerosis and vascular morphology, the quantitative phenotyping of
atherosclerosis
comprising analysis of one or more of plaque volume, plaque composition, or
plaque
progression; determining, by the computer system, correlation of the CAD risk
factor level
with the quantitative phenotyping of atherosclerosis and vascular morphology;
determining, by the computer system, an individualized CAD risk factor level
threshold of
elevated risk of CAD for the subject based at least in part on the CAD risk
factor level and
the determined correlation of the CAD risk factor level with the quantitative
phenotyping
of atherosclerosis and vascular morphology; and determining, by the computer
system, a
subject-specific goal for the CAD risk factor level based at least in part on
the determined
individualized CAD risk factor level threshold of elevated risk of CAD for the
subject,
wherein the determined subject-specific goal for the CAD risk factor level is
configured to
be used to determine an individualized treatment for the subject, wherein the
computer
system comprises a computer processor and an electronic storage medium.
115471
Embodiment 2: The computer-implemented method of Embodiment 1,
wherein the CAD risk factor level comprises one or more of cholesterol level,
low-density
lipoprotein (LDL) cholesterol level, high-density lipoprotein (HDL)
cholesterol level,
cholesterol particle size and fluffiness, inflammation level, glycosylated
hemoglobin, or
blood pressure.
115481
Embodiment 3: The computer-implemented method of Embodiments 1
or 2, wherein the quantitative phenotyping of atherosclerosis is performed
based at least in
part on analysis of density values of one or more pixels of the medical image
corresponding
to plaque.
115491
Embodiment 4: The computer-implemented method of any one of
Embodiments 1-3, wherein the plaque volume comprises one or more of total
plaque
volume, calcified plaque volume, non-calcified plaque volume, or low-density
non-
calcified plaque volume.
115501
Embodiment 5: The computer-implemented method of Embodiment 3,
wherein the density values comprise radiodensity values.
115511
Embodiment 6: The computer-implemented method of any one of
Embodiments 3-5, wherein the plaque composition comprises composition of one
or more
of calcified plaque, non-calcified plaque, or low-density non-calcified
plaque.
115521
Embodiment 7: The computer-implemented method of Embodiment 6,
wherein one or more of the calcified plaque, non-calcified plaque, of low-
density non-
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calcified plaque is identified based at least in part on radiodensity values
of one or more
pixels of the medical image corresponding to plaque.
115531
Embodiment 8: The computer-implemented method of Embodiment 7,
wherein calcified plaque comprises one or more pixels of the medical image
with
radiodensity values of between about 351 and about 2500 Hounsfield units, non-
calcified
plaque comprises one or more pixels of the medical image with radiodensity
values of
between about 31 and about 250 Hounsfield units, and low-density non-calcified
plaque
comprises one or more pixels of the medical image with radiodensity values of
between
about -189 and about 30 Hounsfield units.
115541
Embodiment 9: The computer-implemented method of any of
Embodiments 1 to 8, wherein the plaque progression is determined by:
accessing, by the
computer system, one or more serial medical images of the patient, the one or
more serial
medical images comprising one or more coronary arteries; and analyzing, by the
computer
system, the one or more serial medical images of the patient to determine
plaque
progression based at least in part on a serial change in plaque volume.
115551
Embodiment 10: The computer-implemented method of Embodiment 9,
wherein the serial change in plaque volume is based on one or more of total
plaque volume,
calcified plaque volume, non-calcified plaque volume, or low-density non-
calcified plaque
volume.
115561
Embodiment 11: The computer-implemented method of any one of
Embodiments 1-10, wherein the vascular morphology comprises one or more of
absolute
minimum lumen diameter or area, lumen diameter, cross-sectional lumen area,
vessel
volume, lumen volume, arterial remodeling, vessel or lumen geometry, or vessel
or lumen
curvature.
115571
Embodiment 12: The computer-implemented method of any one of
Embodiments 1-11, wherein the correlation of the CAD risk factor level with
the
quantitative phenotyping of atherosclerosis is determined based at least in
part by
multivariable regression analysis.
115581
Embodiment 13: The computer-implemented method of any one of
Embodiments 1-12, wherein the correlation of the CAD risk factor level with
the
quantitative phenotvping of atherosclerosis is determined based at least in
part by a machine
learning algorithm.
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115591
Embodiment 14: The computer-implemented method of any one of
Embodiments 1-13, wherein the medical image comprises a Computed Tomography
(CT)
image.
115601
Embodiment 15: The computer-implemented method of any on of
Embodiments 1-14, wherein the medical image is obtained using an imaging
technique
comprising one or more of CT, x-ray, ultrasound, echocardiography,
intravascular
ultrasound (IVUS), MR imaging, optical coherence tomography (OCT), nuclear
medicine
imaging, positron-emission tomography (PET), single photon emission computed
tomography (SPECT), or near-field infrared spectroscopy (NIRS).
115611
Embodiment 16: The computer-implemented method of any one of
Embodiments 1-15, wherein the treatment for cardiovascular disease comprises
medical
intervention, medical treatment, or lifestyle interventions, including but not
limited to
changes in diet, physical activity, anxiety and stress level, sleep and
others.
115621
Embodiment 17: The computer-implemented method of any one of
Embodiments 1-16, further comprising: accessing, by the computer system, a
second
medical image of the subject, the second medical image obtained at a later
point in time
than the medical image; analyzing, by the computer system, the second medical
image of
the subject to perform quantitative phenotyping of atherosclerosis;
recalibrating, by the
computer system, the individualized CAD risk factor level threshold of
elevated risk of
CAD for the subject based at least in part on the quantitative phenotyping of
atherosclerosis
of the second medical image; and updating, by the computer system, the subject-
specific
goal for the CAD risk factor level based at least in part on the recalibrated
individualized
CAD risk factor level threshold of elevated risk of CAD for the subject,
wherein the updated
subject-specific goal for the CAD risk factor level is configured to used to
change or
maintain the individualized treatment for the subject.
115631
Embodiment 18: A system for determining patient-specific coronary
artery disease (CAD) risk factor goals based on quantification of coronary
atherosclerosis
using non-invasive medical image analysis, the system comprising: one or more
computer
readable storage devices configured to store a plurality of computer
executable instructions;
and one or more hardware computer processors in communication with the one or
more
computer readable storage devices and configured to execute the plurality of
computer
executable instructions in order to cause the system to: access a CAD risk
factor level for
a subject; access a medical image of the subject, the medical image comprising
one or more
coronary arteries; analyze the medical image of the subject to perform
quantitative
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phenotyping of atherosclerosis, the quantitative phenotyping of
atherosclerosis comprising
analysis of one or more of plaque volume, plaque composition, or plaque
progression;
determine correlation of the CAD risk factor level with the quantitative
phenotyping of
atherosclerosis; determine an individualized CAD risk factor level threshold
of elevated
risk of CAD for the subject based at least in part on the CAD risk factor
level and the
determined correlation of the CAD risk factor level with the quantitative
phenotyping of
atherosclerosis; and determine a subject-specific goal for the CAD risk factor
level based
at least in part on the determined individualized CAD risk factor level
threshold of elevated
risk of CAD for the subject, wherein the determined subject-specific goal for
the CAD risk
factor level is configured to be used to determine an individualized treatment
for the
subj ect.
[1564]
Embodiment 19: The system of Embodiment 18, wherein the CAD risk
factor level comprises one or more of cholesterol level, low-density
lipoprotein (LDL)
cholesterol level, high-density lipoprotein (HDL) cholesterol level,
cholesterol particle size
and fluffiness, inflammation level, glycosylated hemoglobin, or blood
pressure.
[1565]
Embodiment 20: The system of Embodiments 18 or 19, wherein the
quantitative phenotyping of atherosclerosis is performed based at least in
part on analysis
of density values of one or more pixels of the medical image corresponding to
plaque.
[1566]
Embodiment 21: The system of any one of Embodiments 18-21, wherein
the density values comprises radiodensity values.
[1567]
Embodiment 22: The system of any one of Embodiments 18-22, wherein
the plaque volume comprises one or more of total plaque volume, calcified
plaque volume,
non-calcified plaque volume, or low-density non-calcified plaque volume.
115681
Embodiment 23: The system of Embodiment 21, wherein the plaque
composition comprises composition of one or more of calcified plaque, non-
calcified
plaque, or low-density non-calcified plaque.
115691
Embodiment 24: The system of any one of Embodiments 18-23, wherein
the plaque progression is determined by: accessing, by the computer system,
one or more
serial medical images of the patient, the one or more serial medical images
comprising one
or more coronary arteries; and analyzing, by the computer system, the one or
more serial
medical images of the patient to determine plaque progression based at least
in part on a
serial change in plaque volume.
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[1570]
Embodiment 25: The system of any one of Embodiments 22-24, wherein
the serial change in plaque volume is based on one or more of total plaque
volume, calcified
plaque volume, non-calcified plaque volume, or low-density non-calcified
plaque volume.
[1571]
Embodiment 26: The system of any one of Embodiments 18-25, wherein
the correlation of the CAD risk factor level with the quantitative phenotyping
of
atherosclerosis is determined based at least in part by multivariable
regression analysis.
[1572]
Embodiment 27: The system of any one of Embodiments 18-26, wherein
the correlation of the CAD risk factor level with the quantitative phenotyping
of
atherosclerosis is determined based at least in part by a machine learning
algorithm.
115731
Embodiment 28: The system of any one of Embodiments 18-27, wherein
the medical image comprises a Computed Tomography (CT) image.
[1574]
Embodiment 29: The system of any one of Embodiments 18-28, wherein
the medical image is obtained using an imaging technique comprising one or
more of CT,
x-ray, ultrasound, echocardiography, intravascular ultrasound (IV US), MR
imaging,
optical coherence tomography (OCT), nuclear medicine imaging, positron-
emission
tomography (PET), single photon emission computed tomography (SPECT), or near-
field
infrared spectroscopy (NIRS).
[1575]
Embodiment 30: The system of any one of Embodiments 18-30, wherein
the treatment for cardiovascular disease comprises medical intervention,
medical treatment,
or lifestyle change.
[1576]
Embodiment 31: The system of any one of Embodiments 18-30, wherein
the system is further caused to: access a second medical image of the subject,
the second
medical image obtained at a later point in time than the medical image;
analyze the second
medical image of the subject to perform quantitative phenotyping of
atherosclerosis;
recalibrate the individualized CAD risk factor level threshold of elevated
risk of CAD for
the subject based at least in part on the quantitative phenotyping of
atherosclerosis of the
second medical image; and update the subject-specific goal for the CAD risk
factor level
based at least in part on the recalibrated individualized CAD risk factor
level threshold of
elevated risk of CAD for the subject, wherein the updated subject-specific
goal for the CAD
risk factor level is configured to used to change or maintain the
individualized treatment
for the subject.
115771
Embodiment 32: The system of any one of Embodiments 18-25, wherein
the system is further caused to analyze the medical image of the subject to
perform
phenotyping of vascular morphology, wherein the subject-specific goal for the
CAD risk
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factor level is further determined based at least in part on the phenotyping
of vascular
morphology, the vascular morphology comprising one or more of absolute minimum
lumen
diameter or area, lumen diameter, cross-sectional lumen area, vessel volume,
lumen
volume, arterial remodeling, vessel or lumen geometry, or wherein the subject-
specific goal
for the CAD risk factor level is further determined based at least in part on
the phenotyping
of vascular morphology, the vascular morphology comprising one or more of
absolute
minimum lumen diameter or area, lumen diameter, cross-sectional lumen area,
vessel
volume, lumen volume, arterial remodeling, vessel or lumen geometry, or vessel
or lumen
curvature.
Automated diagnosis, risk assessment, and characterization of heart disease
115781
Generally speaking, heart disease or a major adverse cardiovascular
event (MACE) or arterial disease, such as coronary artery disease (CAD) or
periphery
artery disease (PAD) can be extremely difficult to diagnose until a patient
becomes very
symptomatic. This can be due to the fact that existing methods focus on
detecting severe
and/or physical symptoms which typically arise only in later stages of heart
disease, such
as for example active chest pain, active heart attack, cardiogenic shock,
and/or the like. In
addition, risk of heart disease, MACE can be dependent on a number of
different factors
and/or variables, making it difficult to diagnose, characterize, and/or
predict. As used
herein, MACE can refer to one or more of a stroke, myocardial infarction,
cardiovascular
death, admission for heart failure, ischemic cardiovascular events, cardiac
death,
hospitalization for heart failure, angina pain, cardiovascular-related
illness, cardiac arrest,
heart attack, and/or the like.
115791
In some embodiments, the systems, devices, and methods described
herein address such technical shortcomings by providing an image-based and/or
non-
invasive approach to diagnose, characterize, predict, and/or otherwise assess
risk of MACE
or arterial disease of a subject by taking into account one or more analyses,
for example of
coronary atherosclerosis, aorti c atherosclerosis, and/or emphysema.
Coronary
atherosclerosis, aortic atherosclerosis, and/or emphysema can all be
considered a cause,
factor, and/or variable in risk of MACE or arterial disease. However, existing
technologies
fail to provide a comprehensive solution that can take such multiple factors
into
consideration in assessing risk of MACE or arterial disease. In addition, the
interrelation
between coronary atherosclerosis, aortic atherosclerosis, and/or emphysema
when
assessing risk of MACE or arterial disease can be difficult to ascertain. As
such, in some
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embodiments, the systems, methods, and devices are configured to determine a
likelihood
and/or risk of MACE or arterial disease based on inputs of one or more of
coronary
atherosclerosis, aortic atherosclerosis, and/or emphysema, for example
utilizing a machine
learning (ML) and/or artificial intelligence (Al) algorithm(s). In some
embodiments, by
the combination of analyzing coronary atherosclerosis, aortic atherosclerosis,
and
emphysema can provide synergistic effects in more accurately determining the
risk of
MACE and/or arterial disease. Moreover, it can be advantageous to non-
invasively
determine risk of MACE or arterial disease instead of using invasive measures,
such as for
example a stress test and/or the like. As such, in some embodiments, the
systems, methods,
and devices can be configured to analyze one or more images obtained non-
invasively to
derive, phenotype, characterize, quantify, and/or otherwise analyze coronary
atherosclerosis, aortic atherosclerosis, and/or emphysema, the results of
which can then be
used to diagnose, assess, and/or characterize risk of MACE or arterial disease
for a subject,
thereby providing a multi-factor and/or non-invasive approach to MACE or
arterial disease
risk assessment. Such risk assessment can further be used to generate a
proposed treatment
for a subject for lowering and/or maintaining risk of MACE or arterial
disease.
115801
In some embodiments, the system can be configured to analyze just
coronary and aortic atherosclerosis, and not emphysema, in assessing risk of
MACE or
arterial disease. In some embodiments, the system can be configured to analyze
just
coronary atherosclerosis and emphysema in assessing risk of MACE or arterial
disease. In
some embodiments, the system can be configured to analyze coronary
atherosclerosis,
aortic atherosclerosis, and emphysema in assessing risk of MACE or arterial
disease.
115811
In some embodiments, the system can be configured to utilize a
reference database with risk assessments of MACE or arterial disease based on
one or more
of coronary atherosclerosis, aortic atherosclerosis, and/or emphysema to
generate a
population-based percentage of risk of MACE or arterial disease for a subject.
In some
embodiments, the population-based percentage can be based on one or more other
factors,
such as for example age, gender, ethnicity, and/or risk factors.
115821
In particular, in some embodiments, the systems, devices, and methods
described herein are configured to diagnose, characterize, assess the risk of,
and/or augment
or enhance the diagnosis of MACE, heart disease, coronary heart disease,
coronary
atherosclerotic disease, arterial disease and/or the like on a sub-clinical
level. Further, in
some embodiments, the systems, devices, and methods described herein are
configured to
diagnose, characterize, assess the risk of, and/or augment or enhance the
diagnosis of
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MACE, arterial disease, heart disease, coronary heart disease, coronary
atherosclerotic
disease, and/or the like utilizing one or more image analysis techniques
and/or processes.
In some embodiments, the systems, devices, and methods described herein are
configured
to diagnose, characterize, assess the risk of, and/or augment or enhance the
diagnosis of
MACE, arterial disease, heart disease, coronary heart disease, coronary
atherosclerotic
disease, and/or the like even when the subject has not experienced any
physical symptoms,
such as active chest pain, active heart attack, cardiogenic shock, and/or the
like. In some
embodiments, the systems, devices, and methods described herein are configured
to
diagnose, characterize, assess the risk of, and/or augment or enhance the
diagnosis of
MACE, arterial disease, heart disease, coronary heart disease, coronary
atherosclerotic
disease, and/or the like without the need to analyze any such physical
symptoms. In some
embodiments, the systems, devices, and methods described herein are configured
to
diagnose, characterize, assess the risk of, and/or augment or enhance the
diagnosis of
MACE, arterial disease, heart disease, coronary heart disease, coronary
atherosclerotic
disease, and/or the like utilizing one or more image analysis techniques
and/or processes
and optionally supplementing the same based on a history of physical symptoms
experienced by the subject, such as for example active chest pain, active
heart attack,
cardiogenic shock, and/or the like. As such, in some embodiments, the systems,
methods,
and devices described herein can be configured to diagnose, characterize,
assess the risk
of, and/or augment or enhance the diagnosis of asymptomatic atherosclerosis,
such as
asymptomatic aortic atherosclerosis and/or asymptomatic coronary
atherosclerosis, and/or
emphysema.
115831
As discussed herein, in some embodiments, the systems, devices, and
methods described herein can be configured to utilize one or more image
analysis and/or
processing techniques to diagnose, characterize, assess the risk of, and/or
augment or
enhance the diagnosis of MACE, arterial disease, and/or heart disease, whether
symptomatic or asymptomatic, such as for example based on aortic
atherosclerosis,
coronary atherosclerosis, emphysema and/or the like. For example, in some
embodiments,
the systems, methods, and devices can be configured to analyze one or more
medical
images of a subject, such as a coronary CT angiography (CCTA), using one or
more image
processing, artificial intelligence, and/or machine learning techniques.
In some
embodiments, the systems, methods, and devices described herein can be
configured to
diagnose, characterize, assess the risk of, and/or augment or enhance the
diagnosis of heart
disease by analyzing one or more medical images, such as for example a
contrast-enhanced
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CCTA, non-contrast CT, non-contrast coronary calcium scoring, non-gated
contrast or
contrast chest CT scans, abdominal CT scan, MRI angiography, x-ray
fluoroscopy, and/or
the like.
115841
In some embodiments, the systems, methods, and devices described
herein can be configured to utilize analyses of coronary atherosclerosis,
aortic
atherosclerosis, and/or emphysema to identify high-risk subjects of MACE or
arterial
disease. For example, in some embodiments, the system can be configured to
utilize
analyses of coronary atherosclerosis, aortic atherosclerosis, and/or emphysema
to identify
new formers of plaque or non-calcified plaque, rapid progressors of plaque or
non-calcified
plaque, and/or non-responders to medicine or treatment. More specifically, in
some
embodiments, the system can be configured to utilize one or more plaque
parameters,
quantified plaque phenotyping, and/or the like described herein, as applied to
a coronary
and/or aortic artery, and/or image-based analysis of emphysema for such
analyses. In some
embodiments, the system can be configured to analyze just coronary and aortic
atherosclerosis, and not emphysema, to identify new formers of plaque or non-
calcified
plaque, rapid progressors of plaque or non-calcified plaque, and/or non-
responders to
medicine or treatment. In some embodiments, the system can be configured to
analyze just
coronary atherosclerosis and emphysema, and not aortic atherosclerosis, to
identify new
formers of plaque or non-calcified plaque, rapid progressors of plaque or non-
calcified
plaque, and/or non-responders to medicine or treatment. In some embodiments,
the system
can be configured to analyze coronary atherosclerosis, aortic atherosclerosis,
and
emphysema to identify new formers of plaque or non-calcified plaque, rapid
progressors of
plaque or non-calcified plaque, and/or non-responders to medicine or
treatment.
115851
In some embodiments, the systems, methods, and devices described
herein can be configured to utilize analyses of coronary atherosclerosis,
aortic
atherosclerosis, and/or emphysema to determine the likelihood of peripheral
artery disease
(PAD). PAD has a worldwide prevalence of more than 200 million, with an
estimated 8-
12 million Americans affected. The prevalence of PAD is expected to increase
as the
population ages, smoking status persists, and the prevalence of diabetes,
hypertension, and
obesity grow. Although awareness has improved, PAD is still associated with
significant
morbidity, mortality, and quality of life impairment. Given the substantial
prevalence of
PAD, it can be imperative that a screening program be undertaken to identify
those with
high risk of PAD, which can be done utilizing one or more systems, devices,
and methods
described herein.
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115861
As a non-limiting example, FIGS. 28A-28B illustrate an example
embodiment of identification of coronary and aortic disease / atherosclerosis
identified on
a coronary CT angiogram (CCTA) utilizing embodiments of the systems, devices,
and
methods described herein. As illustrated in Figure 28A, one or more coronary
arteries can
be imaged as part of a CCTA and, as illustrated in Figure 28B, the aorta can
also be imaged
as part of a CCTA. As such, analysis of coronary and aortic atherosclerosis
can be
performed together based on a single CCTA in some embodiments that are
configured to
analyze CCTAs using one or more image analysis techniques as described herein,
including
for example analysis of one or more plaque, fat, and/or vessel parameters. For
example,
the one or more plaque parameters can include plaque volume, composition,
attenuation,
location, geometry, and/or any other plaque parameters described herein.
115871
In some embodiments, the systems, devices, and methods described
herein can be configured to utilize the diagnosis, characterization, and/or
risk assessment
of heart disease of a subject, such as for example coronary and/or aortic
atherosclerosis, to
further generate a report, treatment, and/or prognosis and/or identify or
track resource
utilization for the subject. By utilizing such techniques, in some
embodiments, the systems,
devices, and methods described herein can allow for early diagnosis and/or
treatment of
heart disease prior to the subject experiencing physical symptoms. For
example, in some
embodiments, the systems, devices, and methods described herein can be
configured to
automatically and/or dynamically place a subject in a particular vascular or
heart disease
category based at least in part on the diagnosis, characterization, and/or
risk assessment of
heart disease of the subject based on image analysis. In some embodiments, the
systems,
devices, and methods described herein can be configured to further assign a
risk-adjusted
weight for the subject to anticipate prognosis and/or resource utilization for
the subject, for
example based at least in part on the diagnosis, characterization, and/or risk
assessment of
heart disease of the subject based on image analysis and/or the particular
vascular or heart
disease category determined for the subject.
115881
FIG. 28C is a flowchart illustrating an example embodiment(s) of
systems, devices, and methods for image-based diagnosis, risk assessment,
and/or
characterization of a major adverse cardiovascular event. As illustrated in
FIG. 28C, in
some embodiments, the system can be configured to analyze one or more of
coronary
atherosclerosis, aortic atherosclerosis, and/or emphysema, for example from a
medical
image, to determine risk of MACE or arterial disease (AD), such as PAD, for a
subject.
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115891
In some embodiments, at block 2802, the system can be configured to
access and/or modify one or more medical images. In some embodiments, the
medical
image can include one or more arteries, such as coronary, aorta, carotid,
and/or other
arteries and/or one or more portions of the lungs of a subject. In some
embodiments, the
medical image can comprise a CCTA. In some embodiments, the medical image can
comprise an image field that is typically acquired during a CCTA. In some
embodiments,
the medical image can comprise a larger image field than that is typically
acquired during
a CCTA, for example to capture one or more portions of the aorta and/or lungs.
In some
embodiments, the system can be configured to access multiple images, one or
more of
which captures one or more portions of the coronary arteries, aorta, and/or
lungs. For
example, in some embodiments, the system can be configured to access one
medical image
that comprises one or more portions of the coronary arteries and/or aorta of
the subject and
a separate image that comprises one or more portions of the lungs. In some
embodiments,
the system can be configured to access one medical image that comprises one or
more
portions of the coronary arteries, one medical image that comprises one or
more portions
of the aorta, and one medical image that comprises one or more portions of the
lungs. In
some embodiments, the system can be configured to access a single medical
image that
comprises one or more portions of the coronary arteries, aorta, and the lungs.
For example,
in some embodiments, the system can be configured to access a single image
acquired from
a single image acquisition to analyze one or more portions of the coronary
arteries, aorta,
and the lungs to determine risk of MACE or arterial disease, such as PAD, for
a subject
115901
In some embodiments, the medical image can be stored in a medical
image database 2804. In some embodiments, the medical image database 2804 can
be
locally accessible by the system and/or can be located remotely and accessible
through a
network connection. The medical image can comprise an image obtain using one
or more
modalities such as for example, CT, Dual-Energy Computed Tomography (DECT),
Spectral CT, photon-counting CT, x-ray, ultrasound, echocardiography,
intravascular
ultrasound (IVUS). Magnetic Resonance (MR) imaging, optical coherence
tomography
(OCT), nuclear medicine imaging, positron-emission tomography (PET), single
photon
emission computed tomography (SPECT), or near-field infrared spectroscopy
(NIRS). In
some embodiments, the medical image comprises one or more of a contrast-
enhanced CT
image, non-contrast CT image, MR image, and/or an image obtained using any of
the
modalities described above.
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115911
In some embodiments, the system can be configured to automatically
and/or dynamically perform one or more analyses of the medical image as
discussed herein.
For example, in some embodiments, at block 2806, the system can be configured
to
identify, analyze, and/or quantify coronary atherosclerosis. In some
embodiments, the
system can be configured to perform quantified phenotyping of coronary
atherosclerosis.
For example, in some embodiments, the quantitative phenotyping can be of
atherosclerosis
burden, volume, type, composition, and/or rate of progression for the
individual or patient.
In some embodiments, the system can be configured to utilize one or more image
processing, artificial intelligence (Al), and/or machine learning (ML)
algorithms to
automatically and/or dynamically perform quantitative phenotyping of
atherosclerosis. For
example, in some embodiments, the system can be configured to automatically
and/or
dynamically identify one or more arteries, vessels, and/or a portion thereof
on the medical
image, identify one or more regions of plaque, and/or perform quantitative
phenotyping of
plaque.
115921
In some embodiments, the system can be configured to identify and/or
characterize different types and/or regions of coronary atherosclerosis or
plaque, for
example based on density, absolute density, material density, relative
density, and/or
radiodensity. In some embodiments, the system can be configured to
characterize a region
of plaque into one or more sub-types of plaque. For example, in some
embodiments, the
system can be configured to characterize a region of plaque as one or more of
low density
non-calcified plaque, non-calcified plaque, or calcified plaque_ in some
embodiments,
calcified plaque can correspond to plaque having a highest density range, low
density non-
calcified plaque can correspond to plaque having a lowest density range, and
non-calcified
plaque can correspond to plaque having a density range between calcified
plaque and low
density non-calcified plaque. For example, in some embodiments, the system can
be
configured to characterize a particular region of plaque as low density non-
calcified plaque
when the radiodensity of an image pixel or voxel corresponding to that region
of plaque is
between about -189 and about 30 Hounsfield units (HU). In some embodiments,
the system
can be configured to characterize a particular region of plaque as non-
calcified plaque when
the radiodensity of an image pixel or voxel corresponding to that region of
plaque is
between about 31 and about 350 HU. In some embodiments, the system can be
configured
to characterize a particular region of plaque as calcified plaque when the
radiodensity of an
image pixel or voxel corresponding to that region of plaque is between about
351 and about
2500 HU.
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115931
In some embodiments, the lower and/or upper Hounsfield unit boundary
threshold for determining whether a plaque corresponds to one or more of low
density non-
calcified plaque, non-calcified plaque, and/or calcified plaque can be about -
1000 HU,
about -900 HU, about -800 HU, about -700 HU, about -600 HU, about -500 HU,
about -
400 HU, about -300 HU, about -200 HU, about -190 HU, about -180 HU, about -170
HU,
about -160 HU, about -150 HU, about -140 HU, about -130 HU, about -120 HU,
about -
110 HU, about -100 HU, about -90HU, about -80 HU, about -70 HU, about -60 HU,
about
-50 HU, about -40 HU, about -30 HU, about -20 HU, about -10 HU, about 0 HU,
about 10
HU, about 20 HU, about 30 HU, about 40 HU, about 50 HU, about 60 HU, about 70
HU,
about 80 HU, about 90 HU, about 100 HU, about 110 HU, about 120 HU, about 130
HU,
about 140 HU, about 150 HU, about 160 HU, about 170 HU, about 180 HU, about
190 HU,
about 200 HU, about 210 HU, about 220 HU, about 230 HU, about 240 HU, about
250 HU,
about 260 HU, about 270 HU, about 280 HU, about 290 HU, about 300 HU, about
310 HU,
about 320 HU, about 330 HU, about 340 HU, about 350 HU. about 360 HU, about
370 HU,
about 380 HU, about 390 HU, about 400 HU, about 410 HU, about 420 HU, about
430 HU,
about 440 HU, about 450 HU, about 460 HU, about 470 HU, about 480 HU, about
490 HU,
about 500 HU, about 510 HU, about 520 HU, about 530 HU, about 540 HU, about
550 HU,
about 560 HU, about 570 HU, about 580 HU, about 590 HU, about 600 HU, about
700 HU,
about 800 HU, about 900 HU, about 1000 HU, about 1100 HU, about 1200 HU, about
1300
HU, about 1400 HU, about 1500 HU, about 1600 HU, about 1700 HU, about 1800 HU,
about 1900 HU, about 2000 HU, about 2100 HU, about 2200 HU, about 2300 HU,
about
2400 HU, about 2500 HU, about 2600 HU, about 2700 HU, about 2800 HU, about
2900
HU, about 3000 HU, about 3100 HU, about 3200 HU, about 3300 HU, about 3400 HU,
about 3500 HU, and/or about 4000 HU.
115941
In some embodiments, the system can be configured to determine and/or
characterize the burden of coronary atherosclerosis based at least part on
volume of plaque.
In some embodiments, the system can be configured to analyze and/or determine
total
volume of coronary plaque and/or volume of low-density non-calcified plaque,
non-
calcified plaque, and/or calcified plaque in the analyzed coronaries. In some
embodiments,
the system can be configured to perform phenotyping of coronary
atherosclerosis by
determining a ratio of one or more of the foregoing volumes of plaque, for
example within
an artery, lesion, vessel, and/or the like.
115951
In some embodiments, the system can be configured to analyze the
progression of coronary atherosclerosis. For example, in some embodiments, the
system
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can be configured to analyze the progression of one or more particular regions
of plaque
and/or overall progression and/or lesion and/or artery-specific progression of
plaque. In
some embodiments, in order to analyze the progression of plaque, the system
can be
configured to analyze one or more serial images of the subject for phenotyping
atherosclerosis. In some embodiments, tracking the progression of plaque can
comprise
analyzing changes and/or lack thereof in total plaque volume and/or volume of
low-density
non-calcified plaque, non-calcified plaque, and/or calcified plaque. In some
embodiments,
tracking the progression of plaque can comprise analyzing changes and/or lack
thereof in
density of a particular region of plaque and/or globally.
115961
In some embodiments, at block 2812, the system can be configured to
determine a risk of MACE and/or arterial disease, such as PAD, based at least
in part on
the results of coronary atherosclerosis analysis and/or quantified
phenotyping. In
determining risk of MACE and/or arterial disease, in some embodiments, the
system can
be configured to access one or more reference values of quantified phenotyping
and/or
other analyses of coronary atherosclerosis as compared to risk of MACE or
arterial disease,
which can be stored on a coronary atherosclerosis risk database 2814. In some
embodiments, the one or more reference values of quantified phenotyping and/or
other
analyses of coronary atherosclerosis as compared to risk of MACE or arterial
disease can
be derived from a population with varying states of coronary atherosclerosis
as compared
to risk of MACE and/or arterial disease. In some embodiments, the coronary
risk database
2814 can be locally accessible by the system and/or can be located remotely
and accessible
through a network connection. In some embodiments, the system can be
configured to
utilize one or more artificial intelligence (Al) and/or machine learning (ML)
algorithms to
automatically and/or dynamically determine risk of MACE or arterial disease
based on
coronary plaque analysis.
115971
In some embodiments, at block 2808, the system can be configured to
identify, analyze, and/or quantify aortic atherosclerosis. In some
embodiments, the system
can be configured to perform quantified phenotyping of aortic atherosclerosis.
For
example, in some embodiments, the quantitative phenotyping can be of
atherosclerosis
burden, volume, type, composition, and/or rate of progression for the
individual or patient.
In some embodiments, the system can be configured to utilize one or more image
processing, artificial intelligence (Al), and/or machine learning (ML)
algorithms to
automatically and/or dynamically perform quantitative phenotyping of aortic
atherosclerosis. For example, in some embodiments, the system can be
configured to
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automatically and/or dynamically identify one or more arteries, vessels,
and/or a portion
thereof on the medical image, identify one or more regions of plaque, and/or
perform
quantitative phenotyping of plaque.
115981
In some embodiments, the system can be configured to identify and/or
characterize different types and/or regions of aortic atherosclerosis or
plaque, for example
based on density, absolute density, material density, relative density, and/or
radiodensity.
In some embodiments, the system can be configured to characterize a region of
aortic
plaque into one or more sub-types of plaque. For example, in some embodiments,
the
system can be configured to characterize a region of plaque as one or more of
low density
non-calcified plaque, non-calcified plaque, or calcified plaque. In some
embodiments,
calcified plaque can correspond to plaque having a highest density range, low
density non-
calcified plaque can correspond to plaque having a lowest density range, and
non-calcified
plaque can correspond to plaque having a density range between calcified
plaque and low
density non-calcified plaque. For example, in some embodiments, the system can
be
configured to characterize a particular region of plaque as low density non-
calcified plaque
when the radiodensity of an image pixel or voxel corresponding to that region
of plaque is
between about -189 and about 30 Hounsfield units (HU). In some embodiments,
the system
can be configured to characterize a particular region of plaque as non-
calcified plaque when
the radiodensity of an image pixel or voxel corresponding to that region of
plaque is
between about 31 and about 350 HU. In some embodiments, the system can be
configured
to characterize a particular region of plaque as calcified plaque when the
radiodensity of an
image pixel or voxel corresponding to that region of plaque is between about
351 and about
2500 HU.
115991
In some embodiments, the lower and/or upper Hounsfield unit boundary
threshold for determining whether an aortic plaque corresponds to one or more
of low
density non-calcified plaque, non-calcified plaque, and/or calcified plaque
can be about -
1000 HU, about -900 HU, about -800 HU, about -700 HU, about -600 HU, about -
500 HU,
about -400 HU, about -300 HU. about -200 HU, about -190 HU, about -180 HU,
about -
170 HU, about -160 HU, about -150 HU, about -140 HU, about -130 HU, about -120
HU,
about -110 HU, about -100 HU, about -90HU, about -80 HU, about -70 HU, about -
60 HU,
about -50 HU, about -40 HU, about -3() HU, about -20 HU, about -10 HU, about 0
HU,
about 10 HU, about 20 HU, about 30 HU, about 40 HU, about 50 HU, about 60 HU,
about
70 HU, about 80 HU, about 90 HU, about 100 HU, about 110 HU, about 120 HU,
about
130 HU, about 140 HU, about 150 HU, about 160 HU, about 170 HU, about 180 HU,
about
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190 HU, about 200 HU, about 210 HU, about 220 HU, about 230 HU, about 240 HU,
about
250 HU, about 260 HU, about 270 HU, about 280 HU, about 290 HU, about 300 HU,
about
310 HU, about 320 HU, about 330 HU, about 340 HU, about 350 HU, about 360 HU,
about
370 HU, about 380 HU, about 390 HU, about 400 HU, about 410 HU, about 420 HU,
about
430 HU, about 440 HU, about 450 HU, about 460 HU, about 470 HU, about 480 HU,
about
490 HU, about 500 HU, about 510 HU, about 520 HU, about 530 HU, about 540 HU,
about
550 HU, about 560 HU, about 570 HU, about 580 HU, about 590 HU, about 600 HU,
about
700 HU, about 800 HU, about 900 HU, about 1000 HU, about 1100 HU, about 1200
HU,
about 1300 HU, about 1400 HU, about 1500 HU, about 1600 HU, about 1700 HU,
about
1800 HU, about 1900 HU, about 2000 HU, about 2100 HU, about 2200 HU, about
2300
HU, about 2400 HU, about 2500 HU, about 2600 HU, about 2700 HU, about 2800 HU,
about 2900 HU, about 3000 HU, about 3100 HU, about 3200 HU, about 3300 HU,
about
3400 HU, about 3500 HU, and/or about 4000 HU.
116001
In some embodiments, the system can be configured to determine and/or
characterize the burden of aortic atherosclerosis based at least part on
volume of plaque. In
some embodiments, the system can be configured to analyze and/or determine
total volume
of aortic plaque and/or volume of low-density non-calcified plaque, non-
calcified plaque,
and/or calcified plaque in the analyzed portion of the aorta. In some
embodiments, the
system can be configured to perform phenotyping of aortic atherosclerosis by
determining
a ratio of one or more of the foregoing volumes of plaque, for example within
a portion of
the aorta, lesion, vessel, and/or the like.
116011
In some embodiments, the system can be configured to analyze the
progression of aortic atherosclerosis. For example, in some embodiments, the
system can
be configured to analyze the progression of one or more particular regions of
plaque and/or
overall progression and/or lesion and/or artery-specific progression of
plaque. In some
embodiments, in order to analyze the progression of plaque, the system can be
configured
to analyze one or more serial images of the subject for phenotyping
atherosclerosis. In
some embodiments, tracking the progression of plaque can comprise analyzing
changes
and/or lack thereof in total plaque volume and/or volume of low-density non-
calcified
plaque, non-calcified plaque, and/or calcified plaque. In some embodiments,
tracking the
progression of plaque can comprise analyzing changes and/or lack thereof in
density of a
particular region of plaque and/or globally.
116021
In some embodiments, at block 2816, the system can be configured to
determine a risk of MACE and/or arterial disease, such as PAD, based at least
in part on
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the results of aortic atherosclerosis analysis and/or quantified phenotyping.
In determining
risk of MACE and/or arterial disease, in some embodiments, the system can be
configured
to access one or more reference values of quantified phenotyping and/or other
analyses of
aortic atherosclerosis as compared to risk of MACE or arterial disease, which
can be stored
on an aortic atherosclerosis risk database 2818. In some embodiments, the one
or more
reference values of quantified phenotyping and/or other analyses of aortic
atherosclerosis
as compared to risk of MACE or arterial disease can be derived from a
population with
varying states of aortic atherosclerosis as compared to risk of MACE and/or
arterial disease.
In some embodiments, the aortic plaque risk database 2818 can be locally
accessible by the
system and/or can be located remotely and accessible through a network
connection. In
some embodiments, the system can be configured to utilize one or more
artificial
intelligence (Al) and/or machine learning (ML) algorithms to automatically
and/or
dynamically determine risk of MACE or arterial disease based on aortic plaque
analysis.
[1603]
In some embodiments, at block 2810, the system can be configured to
identify, analyze, and/or quantify emphysema. In some embodiments, the system
can be
configured to perform quantified phenotyping of emphysema. For example, in
some
embodiments, the quantitative phenotyping can be of emphysema burden, volume,
type,
composition, and/or rate of progression for the individual or patient. In some
embodiments,
the system can be configured to utilize one or more image processing,
artificial intelligence
(Al), and/or machine learning (ML) algorithms to automatically and/or
dynamically
perform quantitative phenotyping of emphysema For example, in some
embodiments, the
system can be configured to automatically and/or dynamically identify one or
more pixels
corresponding to emphysema and/or different levels of emphysema and/or risk
thereof for
quantitative phenotyping.
[1604]
In some embodiments, the system can be configured to identify and/or
characterize different types and/or regions and/or risk levels of emphysema,
for example
based on density, absolute density, material density, relative density, and/or
radiodensity.
For example, the system can be configured to ascertain different risk levels
of emphysema
based at least in part on the darkness and/or brightness of pixels
corresponding to areas of
the lungs, wherein a darker pixel can represent a higher risk of emphysema.
[1605]
In some embodiments, the system can be configured to utilize one or
more Hounsfield unit thresholds for characterizing different risk levels of
emphysema. For
example, in some embodiments, the system can be configured to identify one or
more pixels
of the lungs of a subject as corresponding to emphysema and/or a particular
type or risk of
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emphysema when the Hounsfield unit is above, below, and/or between one or more
of the
following Hounsfield units: about -1500 HU, about -1400 HU, about -1300 HU,
about -
1200 HU, about -1100 HU, about -1000 HU, about -990 HU, about -980 HU, about -
970
HU, about -960 HU, about -950 HU, about -940 HU, about -930 HU, about -920 HU,
about
-910 HU, about -900 HU, about -800 HU, about -700 HU, about -600 HU, and/or
about -
500 HU.
116061
In some embodiments, the system can be configured to determine and/or
characterize the burden of emphysema based at least part on volume of
emphysema. In
some embodiments, the system can be configured to analyze and/or determine
total volume
of emphysema and/or volume of particular risk level of emphysema.
116071
In some embodiments, the system can be configured to analyze the
progression of emphysema. For example, in some embodiments, the system can be
configured to analyze the progression of one or more particular regions of
emphysema
and/or overall progression of emphysema. In some embodiments, in order to
analyze the
progression of emphysema, the system can be configured to analyze one or more
serial
images of the subject for phenotyping emphysema. In some embodiments, tracking
the
progression of emphysema can comprise analyzing changes and/or lack thereof in
total
emphysema volume and/or volume of a particular risk-level of emphysema. In
some
embodiments, tracking the progression of emphysema can comprise analyzing
changes
and/or lack thereof in density of a particular region of emphysema and/or
globally.
116081
In some embodiments, at block 2820, the system can be configured to
determine a risk of MACE and/or arterial disease, such as PAD, based at least
in part on
the results of emphysema analysis and/or quantified phenotyping. In
determining risk of
MACE and/or arterial disease, in some embodiments, the system can be
configured to
access one or more reference values of quantified phenotyping and/or other
analyses of
emphysema as compared to risk of MACE or arterial disease, which can be stored
on an
emphysema risk database 2822. In some embodiments, the one or more reference
values
of quantified phenotyping and/or other analyses of emphysema as compared to
risk of
MACE or arterial disease can be derived from a population with varying states
of
emphysema as compared to risk of MACE and/or arterial disease. In some
embodiments,
the emphysema risk database 2822 can be locally accessible by the system
and/or can be
located remotely and accessible through a network connection. In some
embodiments, the
system can be configured to utilize one or more artificial intelligence (Al)
and/or machine
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learning (ML) algorithms to automatically and/or dynamically determine risk of
MACE or
arterial disease based on emphysema analysis.
[1609]
In some embodiments, at block 2824, the system can be configured to
generate a weighted measure of one or more determined risk levels of MACE
and/or arterial
disease, such as PAD. For example, in some embodiments, the system can be
configured
to generate a weighted measure of risk levels of MACE and/or arterial disease
derived from
analysis of one or more of coronary atherosclerosis, aortic atherosclerosis,
and/or
emphysema. In some embodiments, the system can be configured to weight one or
more
individually derived risk levels of MACE and/or arterial disease the same or
differently,
for example between 0 and 100%. For example, in some embodiments, the system
can be
configured to weight a particular MACE and/or arterial disease risk level
derived from one
of coronary atherosclerosis, aortic atherosclerosis, and emphysema 100% while
discounting the other two.
[1610]
In some embodiments, at block 2826, the system can be configured to
determine a subject-level multifactor risk of MACE and/or arterial disease,
such as PAD.
For example, in some embodiments, in determining the subject-level multifactor
risk of
MACE and/or arterial disease, the system can be configured to access one or
more reference
values of weighted measures of one or more MACE and/or arterial disease and/or
PAD
risks, which can be stored on a subject-level MACE or arterial disease risk
database 2828.
In some embodiments, the one or more reference values of weighted measures of
one or
more MACE and/or arterial disease risks can be derived from a population with
varying
levels of risk of MACE and/or arterial disease, such as PAD. In some
embodiments, the
subject-level MACE or arterial disease risk database 2828 can be locally
accessible by the
system and/or can be located remotely and accessible through a network
connection. In
some embodiments, the system can be configured to utilize one or more
artificial
intelligence (Al) and/or machine learning (ML) algorithms to automatically
and/or
dynamically determine a subject-level multifactor risk of MACE or arterial
disease, such
as PAD.
[1611]
In some embodiments, at block 2830, the system can be configured to
determine a proposed treatment for the subject based on the determined subject-
level
multifactor risk of MACE or arterial disease, such as PAD. For example, in
some
embodiments, the proposed treatment can include one or more of lifestyle
change, exercise,
diet, medication, and/or invasive procedure. In some embodiments, in
determining a
proposed treatment for the subject, the system can be configured to access one
or more
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reference treatments previously utilized for subjects with varying levels of
subject-level
multifactor risks of MACE or arterial disease, which can be stored on a
treatment database
2832. In some embodiments, the one or more reference treatments can be derived
from a
population with varying levels of subject-level multifactor risks of MACE or
arterial
disease, such as PAD. In some embodiments, the treatment database 2832 can be
locally
accessible by the system and/or can be located remotely and accessible through
a network
connection. In some embodiments, the system can be configured to utilize one
or more
artificial intelligence (Al) and/or machine learning (ML) algorithms to
automatically and/or
dynamically determine a proposed treatment for a subject based on a determined
subject-
level multifactor risk of MACE or arterial disease, such as PAD.
[1612]
In some embodiments, at block 2834, the system can be configured to
generate a graphical representation and/or report presenting one or more
findings and/or
analyses described herein in connection with FIG. 28C. For example, in some
embodiments, the system can be configured to generate or cause generation of a
display
presenting results of quantified phenotyping and/or other analysis of coronary
atherosclerosis and/or determined risk of MACE and/or arterial disease, such
as PAD,
based on the same. In some embodiments, the system can be configured to
generate or
cause generation of a display presenting results of quantified phenotyping
and/or other
analysis of aortic atherosclerosis and/or determined risk of MACE and/or
arterial disease,
such as PAD, based on the same. Further, in some embodiments, the system can
be
configured to generate or cause generation of a display presenting results of
quantified
phenotyping and/or other analysis of emphysema and/or determined risk of MACE
and/or
arterial disease, such as PAD, based on the same. In some embodiments, the
system can
be configured to generate or cause generation of a display presenting results
of a generated
weighted measure of risk of MACE or arterial disease based on one or more
individual
analyses, subject-level multifactor risk of MACE or arterial disease, and/or a
proposed
treatment for a subject.
[1613]
In some embodiments, the system can be configured to repeat one or
more processes described in relation to blocks 2802-2834, for example for one
or more
other vessels, segment, regions of plaque, different subjects, and/or for the
same subject at
a different time. As such, in some embodiments, the system can provide for
longitudinal
tracking of risk of MACE or arterial disease and/or personalized treatment for
a subject.
[1614]
FIG. 28D is a flowchart illustrating an example embodiment(s) of
systems, devices, and methods for image-based diagnosis, risk assessment,
and/or
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characterization of a major adverse cardiovascular event. As illustrated in
FIG. 28D, in
some embodiments, the system can be configured to access one or more medical
images
and identify, analyze, and/or quantify one or more of coronary
atherosclerosis, aortic
atherosclerosis, and/or emphysema utilizing one or more processes and/or
features
described above in relation to FIG. 28C, such as in connection with blocks
2802, 2804,
2806, 2808, and/or 2810.
[1615]
In contrast to the embodiments described in FIG. 28C, however, in some
embodiments as illustrated in FIG. 28D, the system can be configured to
generate a
weighted measure of one or more analysis results of coronary atherosclerosis,
aortic
atherosclerosis, and/or emphysema at block 2836 which can be used to directly
determine
a subject-level multifactor risk of MACE or arterial disease, such as PAD, at
block 2838,
as opposed to determining individual risk levels of MACE or arterial disease
based on a
single factor of coronary atherosclerosis, aortic atherosclerosis, and/or
emphysema. In
particular, in some embodiments, the system can be configured to weight one or
more
analysis results of coronary atherosclerosis, aortic atherosclerosis, and/or
emphysema the
same or differently, for example between 0 and 100%. For example, in some
embodiments,
the system can be configured to weight the analysis results of one of coronary
atherosclerosis, aortic atherosclerosis, and/or emphysema more due to
predicted accuracy
levels of one over another, while discounting others.
[1616]
In some embodiments, at block 2838, the system can be configured to
determine a subject-level multifactor risk of MACE and/or arterial disease,
such as PAD,
based on the generated weighted measure of one or more analysis results of
coronary
atherosclerosis, aortic atherosclerosis, and/or emphysema. For example, in
some
embodiments, in determining the subject-level multifactor risk of MACE and/or
arterial
disease, the system can be configured to access one or more reference values
of weighted
measures of analysis results of coronary atherosclerosis, aortic
atherosclerosis, and/or
emphysema, which can be stored on a subject-level MACE or arterial disease
risk database
2840. In some embodiments, the one or more reference values of weighted
measures of
analysis results of coronary atherosclerosis, aortic atherosclerosis, and/or
emphysema can
be derived from a population with varying levels of risk of MACE and/or
arterial disease.
In some embodiments, the subject-level MACE or arterial disease risk database
2840 can
be locally accessible by the system and/or can be located remotely and
accessible through
a network connection. In some embodiments, the system can be configured to
utilize one
or more artificial intelligence (Al) and/or machine learning (ML) algorithms
to
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automatically and/or dynamically determine a subject-level multifactor risk of
MACE or
arterial disease risk of MACE or arterial disease.
[1617]
In some embodiments, the system can further be configured to
determine a proposed treatment and/or generate a graphical representation or
report as
discussed herein in connection with blocks 2830, 2832, and 2834. In some
embodiments,
the system can be configured to repeat one or more processes described in
relation to FIG.
28D, for example for one or more other vessels, segment, regions of plaque,
different
subjects, and/or for the same subject at a different time. As such, in some
embodiments,
the system can provide for longitudinal tracking of risk of MACE or arterial
disease and/or
personalized treatment for a subject.
[1618]
FIG. 28E is a flowchart illustrating an example embodiment(s) of
systems, devices, and methods for image-based diagnosis, risk assessment,
and/or
characterization of a major adverse cardiovascular event. As illustrated in
FIG. 28E, in
some embodiments, the system can be configured to utilize one or more
databases or
datasets comprising a plurality of predetermined diagnoses, medical
conditions, risk scores,
and/or candidate treatments to effectively transform results of quantified
phenotyping
based on a medical image to a risk score and/or candidate treatments. For
example, in some
embodiments, the system can be configured to automatically and/or dynamically
perform
quantified phenotyping of a medical image to analyze coronary atherosclerosis,
aortic
atherosclerosis, and/or emphysema and/or utilize the results of such
quantified phenotyping
to determine a health risk assessment of the subject and/or one or more
candidate
treatments. In order to facilitate effective transformation of such quantified
phenotyping
results to a health risk assessment and/or one or more candidate treatments,
the system can
be configured to utilize one or more such databases and/or datasets. By
utilizing such
databases and/or datasets, in some embodiments, the system can be configured
to
efficiently process the results of quantified phenotyping in a repeatable
and/or automated
manner, thereby saving computing resources and/or need for human intervention.
As such,
in some embodiments, the system can be configured to automatically and/or
dynamically
analyze a medical image, perform quantified phenotyping, and process the
results through
an automated triage process to automatically assess a health risk and/or
treatment for a
subj ect.
[1619]
More specifically, in some embodiments, at block 2802, the system can
be configured to access one or more medical images, for example from a medical
image
database 2804 as discussed above in relation to FIGS. 28C-28D.
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116201
In some embodiments, at block 2842, the system can be configured to
analyze the one or more medical images to perform phenotyping, such as
quantified
phenotyping. In particular, in some embodiments, the system can be configured
to identify
one or more regions of interest for phenotyping, such as for example one or
more portions
of the coronary arteries, aortic arteries, and/or lungs of the subj ect. In
some embodiments,
the system can be configured to perform quantified phenotyping of one or more
of coronary
atherosclerosis, aortic atherosclerosis, and/or emphysema, for example
utilizing one or
more processes described herein in relation to FIGS. 28C-28D.
116211
In some embodiments, based on results of the quantified phenotyping,
the system at block 2844 can be configured to determine if a corresponding
diagnosis exists
in a database or dataset of predetermined diagnoses 2846. In some embodiments,
in order
to efficiently and/or effectively disregard healthy subjects, the
predetermined diagnoses can
correspond only to a subset of quantified phenotyping results. In other words,
in some
embodiments, not all quantified phenotyping results may correspond to a
predetermined
diagnosis. In some embodiments, if the quantified phenotyping result does not
correspond
to a predetermined diagnosis, the process can then be completed, as no further
analysis is
warranted. In contrast, if a corresponding preset or predetermined diagnosis
is found to
exist for the quantified phenotyping results, then the system can be
configured to further
analyze the results.
116221
In some embodiments, if a corresponding predetermined diagnosis is
found to exist for the quantified phenotyping results, the system at block
2848 can be
configured to determine if a corresponding medical condition exists in a
database or dataset
of predetermined medical conditions 2850. In some embodiments, in order to
efficiently
and/or effectively disregard healthy subjects, the predetermined medical
conditions can
correspond only to a subset of predetermined diagnoses. In other words, in
some
embodiments, not all predetermined diagnoses may correspond to a predetermined
medical
condition. In some embodiments, if the diagnosis derived from quantified
phenotyping
does not correspond to a predetermined medical condition, the process can then
be
completed, as no further analysis is warranted. In contrast, if a
corresponding preset or
predetermined medical condition is found to exist for the diagnosis derived
from the
quantified phenotyping results, then the system can be configured to further
analyze the
results.
116231
In some embodiments, if a corresponding predetermined medical
condition is found to exist, the system at block 2852 can be configured to
determine a health
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risk score for the subject, for example by accessing a risk database 2854. The
risk database
2854 can comprise one or more different risk levels and/or scores
corresponding to different
predetermined medical conditions.
116241
In some embodiments, the system can be configured to determine one
or more proposed and/or candidate treatments for the subject at block 2830,
for example
utilizing one or more treatments stored on a treatment database 2832, as
described in more
detail in relation to FIGS. 28C-28D. In some embodiments, the system can be
configured
to generate a graphical representation and/or report at block 2834, for
example displaying
the results of one or more of the quantified phenotyping, corresponding
diagnosis,
corresponding medical condition, determined risk score, and/or proposed or
candidate
treatment(s), as described in more detail in relation to FIGS. 28C-28D.
116251
In some embodiments, the system can be configured to repeat one or
more processes described in relation to blocks 2802-2854, for example for one
or more
other vessels, segment, regions of plaque, different subjects, and/or for the
same subject at
a different time. As such, in some embodiments, the system can provide for
longitudinal
tracking of a subject's health risk derived automatically from quantified
phenotyping of
serial medical images and utilizing one or more predetermined datasets of
diagnoses,
medical conditions, and/or risk scores for efficient and/or effective
processing.
Computer System
116261
In some embodiments, the systems, processes, and methods described
herein are implemented using a computing system, such as the one illustrated
in Figure
28F. The example computer system 2872 is in communication with one or more
computing
systems 2890 and/or one or more data sources 2892 via one or more networks
2888. While
Figure 28F illustrates an embodiment of a computing system 2872, it is
recognized that the
functionality provided for in the components and modules of computer system
2872 can be
combined into fewer components and modules, or further separated into
additional
components and modules.
116271
The computer system 2872 can comprise a Risk Assessment Module
2884 that carries out the functions, methods, acts, and/or processes described
herein. The
Risk Assessment Module 2884 executed on the computer system 2872 by a central
processing unit 2876 discussed further below.
116281
In general the word -module," as used herein, refers to logic embodied
in hardware or firmware or to a collection of software instructions, having
entry and exit
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points. Modules are written in a program language, such as JAVA, C, or C++, or
the like.
Software modules can be compiled or linked into an executable program,
installed in a
dynamic link library, or can be written in an interpreted language such as
BASIC, PERL,
LAU, PHP or Python and any such languages. Software modules can be called from
other
modules or from themselves, and/or can be invoked in response to detected
events or
interruptions. Modules implemented in hardware include connected logic units
such as
gates and flip-flops, and/or can include programmable units, such as
programmable gate
arrays or processors.
116291
Generally, the modules described herein refer to logical modules that
can be combined with other modules or divided into sub-modules despite their
physical
organization or storage. The modules are executed by one or more computing
systems, and
can be stored on or within any suitable computer readable medium, or
implemented in-
whole or in-part within special designed hardware or firmware. Not all
calculations,
analysis, and/or optimization require the use of computer systems, though any
of the above-
described methods, calculations, processes, or analyses can be facilitated
through the use
of computers. Further, in some embodiments, process blocks described herein
can be
altered, rearranged, combined, and/or omitted.
116301
The computer system 2872 includes one or more processing units (CPU)
2876, which can comprise a microprocessor. The computer system 2872 further
includes
a physical memory 2880, such as random access memory (RAM) for temporary
storage of
information, a read only memory (ROM) for permanent storage of information,
and a mass
storage device 2874, such as a backing store, hard drive, rotating magnetic
disks, solid state
disks (S SD), flash memory, phase-change memory (PCM), 3D XPoint memory,
diskette,
or optical media storage device. Alternatively, the mass storage device can be
implemented
in an array of servers. Typically, the components of the computer system 2872
are
connected to the computer using a standards based bus system. The bus system
can be
implemented using various protocols, such as Peripheral Component Interconnect
(PCI),
Micro Channel, SCSI, Industrial Standard Architecture (ISA) and Extended ISA
(EISA)
architectures.
116311
The computer system 2872 includes one or more input/output (I/O)
devices and interfaces 2882, such as a keyboard, mouse, touch pad, and
printer. The I/O
devices and interfaces 2882 can include one or more display devices, such as a
monitor,
that allows the visual presentation of data to a user. More particularly, a
display device
provides for the presentation of GUIs as application software data, and multi-
media
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presentations, for example. The I/O devices and interfaces 2882 can also
provide a
communications interface to various external devices. The computer system 2872
can
comprise one or more multi-media devices 2878, such as speakers, video cards,
graphics
accelerators, and microphones, for example.
Computing System Device / Operating System
116321
The computer system 2872 can run on a variety of computing devices,
such as a server, a Windows server, a Structure Query Language server, a Unix
Server, a
personal computer, a laptop computer, and so forth. In other embodiments, the
computer
system 2872 can run on a cluster computer system, a mainframe computer system
and/or
other computing system suitable for controlling and/or communicating with
large
databases, performing high volume transaction processing, and generating
reports from
large databases. The computing system 2872 is generally controlled and
coordinated by an
operating system software, such as z/OS, Windows, Linux, UNIX, BSD, PHP,
SunOS,
Solaris, MacOS, 'Cloud services or other compatible operating systems,
including
proprietary operating systems. Operating systems control and schedule computer
processes
for execution, perform memory management, provide file system, networking, and
I/O
services, and provide a user interface, such as a graphical user interface
(GUI), among other
things.
Network
116331
The computer system 2872 illustrated in FIG. 28F is coupled to a
network 2888, such as a LAN, WAN, or the Internet via a communication link
2886 (wired,
wireless, or a combination thereof). Network 2888 communicates with various
computing
devices and/or other electronic devices. Network 2888 is communicating with
one or more
computing systems 2890 and one or more data sources 2892. The Risk Assessment
Module
2884 can access or can be accessed by computing systems 2890 and/or data
sources 2892
through a web-enabled user access point. Connections can be a direct physical
connection,
a virtual connection, and other connection type. The web-enabled user access
point can
comprise a browser module that uses text, graphics, audio, video, and other
media to
present data and to allow interaction with data via the network 2888.
116341
The output module can be implemented as a combination of an all-points
addressable display such as a cathode ray tube (CRT), a liquid crystal display
(LCD), a
plasma display, or other types and/or combinations of displays. The output
module can be
implemented to communicate with input devices 2882 and they also include
software with
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the appropriate interfaces which allow a user to access data through the use
of stylized
screen elements, such as menus, windows, dialogue boxes, tool bars, and
controls (for
example, radio buttons, check boxes, sliding scales, and so forth).
Furthermore, the output
module can communicate with a set of input and output devices to receive
signals from the
user.
Other Systems
[1635]
The computing system 2872 can include one or more internal and/or
external data sources (for example, data sources 2892). In some embodiments,
one or more
of the data repositories and the data sources described above can be
implemented using a
relational database, such as DB2, Sybase, Oracle, CodeBase, and Microsoft SQL
Server
as well as other types of databases such as a flat-file database, an entity
relationship
database, and object-oriented database, and/or a record-based database.
[1636]
The computer system 2872 can also access one or more databases 2892.
The databases 2892 can be stored in a database or data repository. The
computer system
2872 can access the one or more databases 2892 through a network 2888 or can
directly
access the database or data repository through I/O devices and interfaces
2882. The data
repository storing the one or more databases 2892 can reside within the
computer system
2872.
URLs and Cookies
116371
In some embodiments, one or more features of the systems, methods,
and devices described herein can utilize a URL and/or cookies, for example for
storing
and/or transmitting data or user information. A Uniform Resource Locator (URL)
can
include a web address and/or a reference to a web resource that is stored on a
database
and/or a server. The URL can specify the location of the resource on a
computer and/or a
computer network. The URL can include a mechanism to retrieve the network
resource. The source of the network resource can receive a URL, identify the
location of
the web resource, and transmit the web resource back to the requestor. A URL
can be
converted to an IP address, and a Doman Name System (DNS) can look up the URL
and
its corresponding IP address. URLs can be references to web pages, file
transfers, emails,
database accesses, and other applications. The URLs can include a sequence of
characters
that identify a path, domain name, a file extension, a host name, a query, a
fragment,
scheme, a protocol identifier, a port number, a username, a password, a flag,
an object, a
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resource name and/or the like. The systems disclosed herein can generate,
receive,
transmit, apply, parse, serialize, render, and/or perform an action on a URL.
116381
A cookie, also referred to as an HTTP cookie, a web cookie, an intern&
cookie, and a browser cookie, can include data sent from a website and/or
stored on a user's
computer. This data can be stored by a user's web browser while the user is
browsing. The
cookies can include useful information for websites to remember prior browsing
information, such as a shopping cart on an online store, clicking of buttons,
login
information, and/or records of web pages or network resources visited in the
past. Cookies
can also include information that the user enters, such as names, addresses,
passwords,
credit card information, etc. Cookies can also perform computer functions. For
example,
authentication cookies can be used by applications (for example, a web
browser) to identify
whether the user is already logged in (for example, to a web site). The cookie
data can be
encrypted to provide security for the consumer. Tracking cookies can be used
to compile
historical browsing histories of individuals. Systems disclosed herein can
generate and use
cookies to access data of an individual. Systems can also generate and use
JSON web
tokens to store authenticity information, HTTP authentication as
authentication protocols,
IP addresses to track session or identity information, URLs, and the like.
Examples of embodintents relating to automatically determining a diagnosis,
risk
assessment, and characterization of heart disease
116391
The following are non-limiting examples of certain embodiments of
systems and methods for determining a diagnosis, risk assessment, and
characterization of
heart disease and/or other related features. Other embodiments may include one
or more
other features, or different features, that are discussed herein.
116401
Embodiment 1: A computer-implemented method of facilitating
assessment of risk of heart disease for a subject based on multi-dimensional
information
derived from non-invasive medical image analysis, the method comprising:
accessing, by
a computer system, one or more medical images of a subject, wherein the
medical image
of the subject is obtained non-invasively; analyzing, by the computer system,
the one or
more medical images of the subject to identify one or more portions of
coronary arteries,
aorta, and lungs of the subject; identifying, by the computer system, one or
more regions
of plaque in the identified one or more portions of the coronary arteries;
analyzing, by the
computer system, the identified one or more regions of plaque in the coronary
arteries to
perform quantified ph en oty pi ng of coronary atherosclerosis comprising
total plaque
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volume, low-density non-calcified plaque volume, non-calcified plaque volume,
and
calcified plaque volume in the one or more portions of coronary arteries;
identifying, by
the computer system, one or more regions of plaque in the identified one or
more portions
of the aorta; analyzing, by the computer system, the identified one or more
regions of
plaque in the aorta to perform quantified phenotyping of aortic
atherosclerosis comprising
total plaque volume, low-density non-calcified plaque volume, non-calcified
plaque
volume, and calcified plaque volume in the one or more portions of the aorta;
analyzing,
by the computer system, the identified one or more portions of the lungs of
the subject to
determine presence or state of emphysema; and causing, by the computer system,
display
of a graphical representation comprising results of the quantified phenotyping
of coronary
atherosclerosis, results of the quantified phenotyping of aortic
atherosclerosis, and presence
or state of emphysema to facilitate assessment of risk of heart disease for
the subject based
on multidimensional analysis of coronary atherosclerosis, aortic
atherosclerosis, and
emphysema, wherein the computer system comprises a computer processor and an
electronic storage medium.
116411
Embodiment 2: The computer-implemented method of Embodiment 1,
wherein the one or more medical images comprises a single medical image
showing the
one or more portions of the coronary arteries, aorta, and lungs appear on a
single medical
image.
116421
Embodiment 3: The computer-implemented method of Embodiments 1
or 2, wherein the one or more medical images comprises a plurality of medical
images.
116431
Embodiment 4: The computer-implemented method of any one of
Embodiments 1 to 3, wherein one or more of the quantitative phenotyping of
coronary
atherosclerosis or the quantitative phenotyping of aortic atherosclerosis is
performed based
at least in part on analysis of density values of one or more pixels of the
one or more medical
images corresponding to plaque.
116441
Embodiment 5: The computer-implemented method of Embodiment 4,
wherein the density values comprise radiodensity values.
116451
Embodiment 6: The computer-implemented method of any one of
Embodiments 1 to 5, wherein the presence or state of emphysema is determined
based at
least in part on analysis of density values of one or more pixels of the one
or more medical
images corresponding to the one or more portions of the lungs.
116461
Embodiment 7: The computer-implemented method of Embodiment 6,
wherein the density values comprise radiodensity values.
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[1647]
Embodiment 8: The computer-implemented method of any one of
Embodiments 1 to 7, wherein the one or more regions of plaque are identified
as low density
non-calcified plaque when a radiodensity value is between about -189 and about
30
Hounsfield units.
[1648]
Embodiment 9: The computer-implemented method of any one of
Embodiments 1 to 8, wherein the one or more regions of plaque are identified
as non-
calcified plaque when a radiodensity value is between about 30 and about 350
Hounsfield
units.
[1649]
Embodiment 10: The computer-implemented method of any one of
Embodiments 1 to 9, wherein the one or more regions of plaque are identified
as calcified
plaque when a radiodensity value is between about 351 and 2500 Hounsfield
units.
[1650]
Embodiment 11: The computer-implemented method of any one of
Embodiments 1 to 10, wherein the one or more medical images comprise a
Computed
Tomography (CT) image.
[1651]
Embodiment 12: The computer-implemented method of any one of
Embodiments 1 to 11, wherein the one or more medical images are obtained using
an
imaging technique comprising one or more of CT, x-ray, ultrasound,
echocardiography,
MR imaging, optical coherence tomography (OCT), nuclear medicine imaging,
positron-
emission tomography (PET), single photon emission computed tomography (SPECT),
or
near-field infrared spectroscopy (NIRS).
[1652]
Embodiment 13: The computer-implemented method of any one of
Embodiments 1 to 12, further comprising generating, by the computer system, a
multifactor
assessment of risk of heart disease for the subject based at least in part on
analysis of
coronary atherosclerosis, aortic atherosclerosis, and emphysema.
[1653]
Embodiment 14: The computer-implemented method of any one of
Embodiments 1 to 13, wherein the assessment of risk of heart disease is
generated utilizing
a machine learning algorithm.
[1654]
Embodiment 15: The computer-implemented method of any one of
Embodiments 1 to 14, further comprising generating, by the computer system, a
recommended treatment for the subject based at least in part on the generated
assessment
of risk of heart disease for the subject.
[1655]
Embodiment 16: The computer-implemented method of Embodiment
13, wherein the assessment of risk of heart disease is generated at least in
part by:
comparing results of the quantified phenotyping of coronary atherosclerosis to
a set of
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reference values of quantified phenotyping of coronary atherosclerosis
corresponding to
different levels of risk of heart disease; comparing results of the quantified
phenotyping of
aortic atherosclerosis to a set of reference values of quantified phenotyping
of aortic
atherosclerosis corresponding to different levels of risk of heart disease;
and comparing the
presence or state of emphysema to a set of reference values of state of
emphysema
corresponding to different levels of risk of heart disease.
116561
Embodiment 17: The computer-implemented method of Embodiment
16, wherein one or more of the set of reference values of quantified
phenotyping of
coronary atherosclerosis, set of reference values of quantified phenotyping of
aortic
atherosclerosis, or set of reference values of state of emphysema is derived
from a reference
population with varying levels of risk of heart disease.
116571
Embodiment 18: The computer-implemented method of any one of
Embodiments 1 to 17, wherein the reference population is selected based on one
or more
of age, gender, or ethnicity of the subject.
116581
Embodiment 19: The computer-implemented method of any one of
Embodiments 1 to 13, wherein the assessment of risk of heart disease is
generated at least
in part by: assessing risk of heart disease based on the results of quantified
phenotyping of
coronary atherosclerosis; assessing risk of heart disease based on the results
of the
quantified phenotyping of aortic atherosclerosis; assessing risk of heart
disease based on
the presence or state of emphysema; generating a weighted measure of the risk
of heart
disease assessed based on the results of quantified phenotyping of coronary
atherosclerosis,
the results of the quantified phenotyping of aortic atherosclerosis, and the
presence or state
of emphysema; and generating the multifactor assessment of heart disease based
on the
weighted measure.
116591
Embodiment 20: A computer-implemented method of assessing risk of
heart disease for a subject based on multi-dimensional information derived
from non-
invasive medical image analysis, the method comprising: accessing, by a
computer system,
results of quantified phenotyping of coronary atherosclerosis of a subject at
a first point in
time, the quantified phenotyping of coronary atherosclerosis comprising total
plaque
volume, low-density non-calcified plaque volume, non-calcified plaque volume,
and
calcified plaque volume in one or more portions of coronary arteries of the
subject;
accessing, by a computer system, results of quantified phenotyping of aortic
atherosclerosis
of the subject at the first point in time, the quantified phenotyping of
aortic atherosclerosis
comprising total plaque volume, low-density non-calcified plaque volume, non-
calcified
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plaque volume, and calcified plaque volume in one or more portions of the
aorta of the
subject; accessing, by the computer system, a medical image of the subject,
wherein the
medical image of the subject is obtained at a second point in time, the
medical image
comprising the one or more portions of coronary arteries and the one or more
portions of
the aorta of the subj ect; performing, by the computer system, quantitative
phenotyping of
coronary atherosclerosis at the second point in time; performing, by the
computer system,
quantitative phenotyping of aortic atherosclerosis at the second point in
time; analyzing, by
the computer system, progression of coronary atherosclerosis based at least in
part on
comparing the results of quantitative phenotyping of coronary atherosclerosis
between the
first point in time and the second point in time; analyzing, by the computer
system,
progression of aortic atherosclerosis based at least in part on comparing the
results of
quantitative phenotyping of aortic atherosclerosis between the first point in
time and the
second point in time; and assessing, by the computer system, a risk of heart
disease for the
subject based at least in part on the analysis of the progression of coronary
atherosclerosis
and the progression of aortic atherosclerosis, wherein the computer system
comprises a
computer processor and an electronic storage medium.
116601
Embodiment 21: The computer-implemented method of Embodiment
20, wherein the risk of heart disease for the subject is assessed to be high
when the volume
of non-calcified plaque in one or more of the coronary arteries or aorta is
higher at the
second point in time than at the first point in time.
[1661]
Embodiment 22: The computer-implemented method of any one of
Embodiments 20 or 21, wherein the risk of heart disease for the subject is
assessed to be
high when the total plaque volume in one or more of the coronary arteries or
aorta is higher
at the second point in time than at the first point in time.
[1662]
Embodiment 23: The computer-implemented method of any one of
Embodiments 20 to 22, wherein the risk of heart disease for the subject is
assessed to be
high when the subject was non-responsive to a medication prescribed to the
subject at the
first point in time to stabilize atherosclerosis.
116631
Embodiment 24: A computer-implemented method of assessing risk of
heart disease for a subject based on multi-dimensional information derived
from non-
invasive medical image analysis, the method comprising: accessing, by a
computer system,
results of quantified phenotyping of coronary atherosclerosis of a subj ect at
a first point in
time, the quantified phenotyping of coronary atherosclerosis comprising total
plaque
volume, low-density non-calcified plaque volume, non-calcified plaque volume,
and
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calcified plaque volume in one or more portions of coronary arteries of the
subject;
accessing, by a computer system, a state of emphysema of the subject analyzed
at the first
point in time; accessing, by the computer system, a medical image of the
subject, wherein
the medical image of the subject is obtained at a second point in time, the
medical image
comprising the one or more portions of coronary arteries and lungs of the
subject;
analyzing, by the computer system, the medical image to perform quantitative
phenotyping
of coronary atherosclerosis at the second point in time; analyzing, by the
computer system,
the medical image to determine a state of emphysema at the second point in
time; analyzing,
by the computer system, progression of coronary atherosclerosis based at least
in part on
comparing the results of quantitative phenotyping of coronary atherosclerosis
between the
first point in time and the second point in time; analyzing, by the computer
system,
progression of emphysema based at least in part on comparing the state of
emphysema
between the first point in time and the second point in time; and assessing,
by the computer
system, a risk of heart disease for the subject based at least in part on the
analysis of the
progression of coronary atherosclerosis and the progression of emphysema,
wherein the
computer system comprises a computer processor and an electronic storage
medium.
116641
Embodiment 25: The computer-implemented method of Embodiment
24, wherein the risk of heart disease for the subject is assessed to be high
when the volume
of non-calcified plaque in the one or more portions of coronary arteries is
higher at the
second point in time than at the first point in time.
[1665]
Embodiment 26: The computer-implemented method of any one of
Embodiments 24 to 25, wherein the risk of heart disease for the subject is
assessed to be
high when the total plaque volume in the one or more portions of coronary
arteries is higher
at the second point in time than at the first point in time.
[1666]
Embodiment 27: The computer-implemented method of any one of
Embodiments 24 to 26, wherein the risk of heart disease for the subject is
assessed to be
high when the subject was non-responsive to a medication prescribed to the
subject at the
first point in time to stabilize atherosclerosis.
116671
Embodiment 28: A computer-implemented method of assessing risk of
peripheral artery disease (PAD) for a subject based on multi-dimensional
information
derived from non-invasive medical image analysis, the method comprising:
accessing, by
a computer system, one or more medical images of a subject, wherein the
medical image
of the subject is obtained non-invasively; analyzing, by the computer system,
the one or
more medical images of the subject to identify one or more coronary arteries
of the subject;
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identifying, by the computer system, one or more regions of plaque in the
identified one or
more coronary arteries; analyzing, by the computer system, the identified one
or more
regions of plaque in the coronary arteries to perform quantified phenotyping
of coronary
atherosclerosis comprising total plaque volume, low-density non-calcified
plaque volume,
non-calcified plaque volume, and calcified plaque volume in the one or more
coronary
arteries; comparing, by the computer system, results of the quantified
phenotyping of
coronary atherosclerosis to a set of reference values of quantified
phenotyping of coronary
atherosclerosis corresponding to different levels of risk of PAD; and
generating, by the
computer system, an assessment of risk of PAD for the subject based at least
in part on the
comparison of the results of the quantified phenotyping of coronary
atherosclerosis to the
set of reference values, wherein the computer system comprises a computer
processor and
an electronic storage medium.
116681
Embodiment 29: The computer-implemented method of Embodiment
28, further comprising: identifying, by the computer system, one or more
portions of the
aorta of the subject on the medical image; identifying, by the computer
system, one or more
regions of plaque in the identified one or more portions of the aorta;
analyzing, by the
computer system, the identified one or more regions of plaque in the aorta to
perform
quantified phenotyping of aortic atherosclerosis comprising total plaque
volume, low-
density non-calcified plaque volume, non-calcified plaque volume, and
calcified plaque
volume in the one or more portions of the aorta; and comparing, by the
computer system,
results of the quantified phenotyping of aortic atherosclerosis to a set of
reference values
of quantified phenotyping of aortic atherosclerosis corresponding to different
levels of risk
of PAD, wherein the assessment of risk of PAD for the subject is further
generated based
at least in part on the comparison of the results of the quantified
phenotyping of aortic
atherosclerosis to the set of reference values of quantified phenotyping of
aortic
atherosclerosis.
116691
Embodiment 30: The computer-implemented method of any one of
Embodiments 28 to 30, further comprising: identifying, by the computer system,
one or
more portions of the lungs of the subject on the medical image; analyzing, by
the computer
system, the identified one or more portions of the lungs of the subject to
determine a state
of emphysema for the subject; and comparing, by the computer system, the
determined
state of emphysema for the subject to a set of reference values of states of
emphysema
corresponding to different levels of risk of PAD, wherein the assessment of
risk of PAD
for the subject is further generated based at least in part on the comparison
of the results of
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the determined state of emphysema for the subject to the set of reference
values of states
of emphysema.
[1670]
Embodiment 31: A computer-implemented method of assessing a health
risk of a subject based on quantitative phenotyping derived from non-invasive
medical
image analysis, the method comprising: accessing, by a computer system, one or
more
medical images of a subject, wherein the medical image of the subject is
obtained non-
invasively; analyzing, by the computer system, the one or more medical images
of the
subject to identify one or more regions of interest, the one or more regions
of interest
comprising one or more portions of portions of coronary arteries, aorta, or
lungs of the
subject; automatically analyzing, by the computer system, the one or more
regions of
interest to perform quantified phenotyping, the quantified phenotyping
comprising one or
more of coronary atherosclerosis, aortic atherosclerosis, or emphysema;
accessing, by the
computer system, a first dataset comprising a plurality of predetermined
diagnoses to
determine presence of an applicable predetermined diagnosis corresponding to
results of
the quantified phenotyping; accessing, by the computer system, when an
applicable
predetermined diagnosis corresponding to results of the quantified phenotyping
is present,
a second dataset comprising a plurality of predetermined medical conditions to
determine
presence of an applicable predetermined medical condition corresponding to the
applicable
predetermined diagnosis; automatically determining, by the computer system,
when an
applicable predetermined medical condition corresponding to the applicable
predetermined
diagnosis is present, a third database comprising a plurality of health risk
scores to
determine an applicable health risk score for the subject corresponding to the
applicable
predetermined medical condition, wherein the applicable health risk score is
derived from
the quantified phenotyping of the one or more medical images; and determining,
by the
computer system, one or more candidate treatments for the subject based on the
applicable
health risk score, wherein the computer system comprises a computer processor
and an
electronic storage medium.
[1671]
Embodiment 32: The computer-implemented method of Embodiment
31, further comprising causing, by the computer system, generation of a
graphical
representation of the determined one or more candidate treatments for the
subject.
[1672]
Embodiment 33: The computer-implemented method of Embodiments
31 or 32, wherein the quantitative phenotyping is performed based at least in
part on
analysis of density values of one or more pixels of the one or more medical
images.
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[1673]
Embodiment 34: The computer-implemented method of any one of
Embodiments 31-33, wherein the density values comprise radi dens ity values.
116741
Embodiment 35: The computer-implemented method of any one of
Embodiments 31-34, wherein the one or more medical images are obtained using
an
imaging technique comprising one or more of CT, x-ray, ultrasound,
echocardiography,
MR imaging, optical coherence tomography (OCT), nuclear medicine imaging,
positron-
emission tomography (PET), single photon emission computed tomography (SPECT),
or
near-field infrared spectroscopy (NIRS).
Improving accuracy of CAD measurements
[1675]
Various embodiments described herein relate to systems, devices, and
methods for improving the accuracy of CAD measurements in non-invasive
imaging.
While the primary examples described in this section relate to approaches for
improving
the accuracy of CAD measurements by non-invasive CT angiography, these
techniques can
be applied to any imaging modality of any anatomical structure that exhibits
motion (or
other artifacts) across a series of acquired images. In this way, the features
described herein
are broadly applicable, and this disclosure should not be limited to the
particular examples
described herein.
116761
As an example, in some embodiments, a CT scan is performed of the
heart, with multiple -phases" or -series" acquired during the cardiac cycle
(e.g., as the heart
is contracting or expanding). Each phase or series can comprise an image or a
plurality of
images (e.g., a video) captured during a different portion of the cardiac
cycle. In some
embodiments, the systems, methods, and devices described herein are configured
to
identify where, in the different phases or series acquired during the cardiac
cycle, the
optimal image quality for each artery, branch, or segment is present.
[1677]
The phase or series that provides the highest image quality for any
particular artery, branch, or segment can then be used to perform vision-based
or other
forms of CAD measurement. For example, in some embodiments, the phase or
series that
provides the highest image quality for any particular artery can be analyzed
to provide, for
example, quantitative phenotyping of atherosclerosis. The quantitative
phenotvping of
atherosclerosis can include, for example, analysis of one or more of plaque
volume, plaque
composition, or plaque progression. In this way, the systems, methods, and
devices
described herein are further configured to provide the capability to -mix-and-
match" these
arteries across different points in the cardiac cycle to ensure that
measurements of coronary
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atherosclerosis and vascular morphology are being done on the images at a
"phase- or
"series" that represents the ideal image quality for that particular artery,
branch, or segment.
116781
Additionally or alternatively, in some embodiments, the different phases
or series that provide the highest image quality for each of the different
arteries, branches,
or segments can also be combined into a composite image that provides improved
visualization of the heart.
116791
These features can provide a significant improvement over conventional
imaging and analysis modalities and can provide a solution to one or more
drawbacks
associated with the same.
116801
Coronary Computed Tomography Angiography (CCTA) has developed
into a clinically useful, guideline-directed non-invasive imaging modality for
diagnosis of
coronary artery disease (CAD). Improvements in CT technology now enable near
motion
free images of the coronary arteries, which allows for accurate measurements
of
atherosclerosis burden and type, and vascular morphology.
116811
However, CCTA is still susceptible to significant imaging artifacts,
owing to such common contributors as coronary artery motion, poor contrast
opacification
and beam hardening artifacts. For the first issue, coronary artery motion,
common solutions
have been to lower patients' heart rates using oral or intravenous beta
blocker medications
so that the limited temporal resolution of the current generation CT scanners
can still
produce relatively motion free images. Yet, even with slowing a patient's
heart rate and
maximizing temporal resolution on latest-generation scanners, the different
coronary
arteries move unpredictably during the cardiac cycle (e.g., as the heart is
contracting and
expanding). Imaging across the cardiac cycle can demonstrate this motion (and
its
associated motion artifacts) for each artery and its branches. Often, one
artery is visualized
with high image quality at one point of the cardiac cycle, while a different
artery is
visualized with high image quality at another point of the cardiac cycle. The
same can be
observed for contrast enhancement or beam hardening, with image quality
differing across
the cardiac cycle.
116821
At present, common clinical practice in image interpretation is to select
the "phase- or "series- within the cardiac cycle that overall represents the
best image
quality with motion-free images of the heart arteries. However, this approach
may allow
for the analysis of the majority of vessels which exhibit ideal image quality,
but does not
necessarily allow for analysis of each and every vessel at the point in the
cardiac cycle
when it is of highest quality. That is, one artery may be of ideal image
quality in one phase
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or series, while another artery may be of ideal image quality in another phase
or series. This
observation, which is noted for arteries, can also be applied to artery
branches and artery
segments. Currently, an imaging physician cognitively reunites the information
of both
reconstructions, acquisitions, or series of images and qualitatively make an
interpretation.
This is not ideal because it is prone to error, it is qualitatively (and not
quantitatively) done,
and it is very dependent on the expertise of the doctor.
116831
To address this need, this application, describes systems, methods, and
devices that are configured to identify optimal image quality on an artery,
branch, or
segment-by-artery, branch, or segment basis, and that can provide the
capability to -mix-
and-match" these arteries across different points in the cardiac cycle to
ensure that
measurements of coronary atherosclerosis and vascular morphology are being
done on the
images at the phase or series that represents the ideal image quality for that
particular artery,
branch, or segment.
116841
In some embodiments, the inventions provided herein describe novel
approaches to improving the accuracy of CAD measurements by non-invasive CT
angiography, but this technique can be applied to any imaging modality of any
anatomic
structure that exhibits motion (or other artifacts) across a series of
acquired images.
116851
As discussed herein, in some embodiments, the systems, devices, and
methods described herein are configured for improving the accuracy of CAD
measurements
in non-invasive imaging. In particular, in some embodiments, a CT scan is
performed of
the heart, with multiple "phases" or "series" acquired during the cardiac
cycle (e.g., as the
heart is contracting or expanding). In some embodiments, the systems, methods,
and
devices described herein are configured to identify where, in the different
phases or series
acquired during the cardiac cycle, the optimal image quality for each artery,
branch, or
segment is determined. Then, in some embodiments, the systems, methods, and
devices
described herein are configured to provide the capability to "mix-and-match"
these arteries
across different points in the cardiac cycle to ensure that measurements of
coronary
atherosclerosis and vascular morphology are being done on the images at a -
phase" or
"series" that represents the ideal image quality for that particular artery,
branch, or segment.
116861
FIG. 29A is a block diagram illustrating an example embodiment of a
system, device, and method for improving the accuracy of CAD measurements in
non-
invasive imaging. As illustrated in FIG. 29A, in some embodiments, the system
can receive
(e.g., capture or otherwise acquire) image data of a heart of an individual or
patient at block
2902. The image can include multiple phases or series acquired during the
cardiac cycle.
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For example, the image can include phases or series representing the heart as
it contracts
or expands during the cardiac cycle. Each phase or series can include, for
example, a single
image or a plurality of images (such as a video). In some embodiments, each
phase or
series can correspond to a portion or sub portion of the cardiac cycle.
116871
In some embodiments, the system can be configured to receive the
image of the individual or patient from a medical imaging device. For example,
the image
can comprise an image obtained by one or more modalities, such as computed
tomography
(CT), contrast-enhanced CT, non-contrast CT, x-ray, ultrasound,
echocardiography,
intravascular ultrasound (IVUS), MR imaging, optical coherence tomography
(OCT),
nuclear medicine imaging, positron-emission tomography (PET), single photon
emission
computed tomography (SPECT), and/or near-field infrared spectroscopy (NIRS).
In some
embodiments, the image can be stored on and/or received from a medical image
database
2914.
116881
In some embodiments, at block 2904, the system can be configured to a
analyze the image data received at block 2902 to label one or more of the
coronary arteries,
branches, or segments of the heart. For example, in some embodiments, an
algorithm is
developed, validated, and applied that is configured to auto-extract and auto-
label the
coronary arteries, their branches and the coronary segments. In some
embodiments, the
system can be configured to utilize one or more image processing, artificial
intelligence
(Al), and/or machine learning (ML) algorithms to automatically and/or
dynamically
identify and/or label one or more arteries, vessels, and/or a portion thereof
within each
phase or series of the image data.
116891
At block 2906, the system can be configured to identify one or more
anatomical landmarks of the heart that are identifiable across the multiple
phases or series
of the image. For example, identification of these landmarks can be used to
allow
comparison of the same structure or part of the structure in different
acquisitions or
reconstructions. In some embodiments, an algorithm can be developed,
validated, and
applied in order to identify the one or more anatomical landmarks. In some
embodiments,
the system can be configured to utilize one or more image processing,
artificial intelligence
(Al), and/or machine learning (ML) algorithms to automatically and/or
dynamically
identify the one or more anatomical landmarks associated with the one or more
arteries,
vessels, and/or portions thereof within each phase or series of the image
data. In some
embodiments, the anatomical landmarks can comprise beginning points and/or
endpoints
associated with the one or more arteries, vessels, and/or portions thereof In
some
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embodiments, the anatomical landmarks can comprise branches associated with
the one or
more arteries, vessels, and/or portions thereof. In some embodiments, other
anatomical
landmarks can be used.
116901
At block 2908, the system can be configured to, for one or more (or all)
of the coronary arteries, branches, or segments, rank image quality for each
of the phases
or series. For example, in some embodiments, an algorithm is developed,
validated, and
applied that is configured to, for one or more of the coronary arteries,
branches, or
segments, rank image quality for across the phases or series. In some
embodiments, the
system can be configured to utilize one or more image processing, artificial
intelligence
(Al), and/or machine learning (ML) algorithms to automatically and/or
dynamically to
determine an image quality rank. Determining an image quality rank can be
based on one
or more of a number of factors including, for example, clarity and/or
sharpness of a
representation of the coronary arteries, branches, or segments within a phase
or series of
the image data.
116911
At block 2940, the phase or series which shows a coronary artery,
branch, or segment with the highest image quality can be identified. Notably,
different
coronary arteries, branches, or segments may be shown with the highest image
quality in
different phases or series. Blocks 2908 and 2940 can be repeated for each of
the coronary
arteries, branches, or segments or for as many of them are desired.
Adventitiously, this
allows for the identification of which phase or series of the image data
provides the best
(e.g., clearest or sharpest) image of each of the identified coronary
arteries.
116921
After the phase or series representing the best image quality for each
coronary artery is identified, at block 2911, that phase or series can be
analyzed to
determine CAD measurements and/or vascular morphology for the associated
coronary
artery. For example at block 2708, the system can be configured to perform
quantitative
phenotyping of atherosclerosis for the articular coronary artery using the
phase or series
that has been identified to correspond to the highest image quality. For
example, in some
embodiments, the quantitative phenotyping can be of atherosclerosis burden,
volume, type,
composition, and/or rate of progression for the individual or patient. In some
embodiments,
the system can be configured to utilize one or more image processing,
artificial intelligence
(Al), and/or machine learning (ML) algorithms to automatically and/or
dynamically
perform quantitative phenotyping of atherosclerosis.
116931
In some embodiments, as part of quantitative phenotyping, the system
can be configured to identify and/or characterize different types and/or
regions of plaque,
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for example based on density, absolute density, material density, relative
density, and/or
radiodensity. For example, in some embodiments, the system can be configured
to
characterize a region of plaque into one or more sub-types of plaque. For
example, in some
embodiments, the system can be configured to characterize a region of plaque
as one or
more of low density non-calcified plaque, non-calcified plaque, or calcified
plaque. In
some embodiments, calcified plaque can correspond to plaque having a highest
density
range, low density non-calcified plaque can correspond to plaque having a
lowest density
range, and non-calcified plaque can correspond to plaque having a density
range between
calcified plaque and low density non-calcified plaque. For example, in some
embodiments,
the system can be configured to characterize a particular region of plaque as
low density
non-calcified plaque when the radiodensity of an image pixel or voxel
corresponding to
that region of plaque is between about -189 and about 30 Hounsfield units
(HU). In some
embodiments, the system can be configured to characterize a particular region
of plaque as
non-calcified plaque when the radiodensity of an image pixel or voxel
corresponding to
that region of plaque is between about 31 and about 350 HU. In some
embodiments, the
system can be configured to characterize a particular region of plaque as
calcified plaque
when the radiodensity of an image pixel or voxel corresponding to that region
of plaque is
between about 351 and about 2500 HU.
116941
In some embodiments, the lower and/or upper Hounsfield unit boundary
threshold for determining whether a plaque corresponds to one or more of low
density non-
calcified plaque, non-calcified plaque, and/or calcified plaque can be about -
1000 HU,
about -900 HU, about -800 HU, about -700 HU, about -600 HU, about -500 HU,
about -
400 HU, about -300 HU, about -200 HU, about -190 HU, about -180 HU, about -170
HU,
about -160 HU, about -150 HU, about -140 HU, about -130 HU, about -120 HU,
about -
110 HU, about -100 HU, about -90HU, about -80 HU, about -70 HU, about -60 HU,
about
-50 HU, about -40 HU, about -30 HU, about -20 HU, about -10 HU, about 0 HU,
about 10
HU, about 20 HU, about 30 HU, about 40 HU, about 50 HU, about 60 HU, about 70
HU,
about 80 HU, about 90 HU, about 100 HU, about 110 HU, about 120 HU, about 130
HU,
about 140 HU, about 150 HU, about 160 HU, about 170 HU, about 180 HU, about
190 HU,
about 200 HU, about 210 HU, about 2950 HU, about 230 HU, about 240 HU, about
250
HU, about 260 HU, about 270 HU, about 280 HU, about 290 HU, about 300 HU,
about
310 HU, about 320 HU, about 330 HU, about 340 HU, about 350 HU, about 360 HU,
about
370 HU, about 380 HU, about 390 HU, about 400 HU, about 410 HU, about 420 HU,
about
430 HU, about 440 HU, about 450 HU, about 460 HU, about 470 HU, about 480 HU,
about
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490 HU, about 500 HU, about 510 HU, about 520 HU, about 530 HU, about 540 HU,
about
550 HU, about 560 HU, about 570 HU, about 580 HU, about 590 HU, about 600 HU,
about
700 HU, about 800 HU, about 900 HU, about 1000 HU, about 1100 HU, about 1200
HU,
about 1300 HU, about 1400 HU, about 1500 HU, about 1600 HU, about 1700 HU,
about
1800 HU, about 1900 HU, about 2000 HU, about 2100 HU, about 29500 HU, about
2300
HU, about 2400 HU, about 2500 HU, about 2600 HU, about 2700 HU, about 2800 HU,
about 2900 HU, about 3000 HU, about 3100 HU, about 3200 HU, about 3300 HU,
about
3400 HU, about 3500 HU, and/or about 4000 HU.
116951
In some embodiments, the system can be configured to determine and/or
characterize the burden of atherosclerosis based at least part on volume of
plaque. In some
embodiments, the system can be configured to analyze and/or determine total
volume of
plaque and/or volume of low-density non-calcified plaque, non-calcified
plaque, and/or
calcified plaque. In some embodiments, the system can be configured to perform
phenotyping of plaque by determining a ratio of one or more of the foregoing
volumes of
plaque, for example within an artery, lesion, vessel, and/or the like.
116961
In some embodiments, the system can be configured to analyze the
progression of plaque. For example, in some embodiments, the system can be
configured
to analyze the progression of one or more particular regions of plaque and/or
overall
progression and/or lesion and/or artery-specific progression of plaque. In
some
embodiments, in order to analyze the progression of plaque, the system can be
configured
to analyze one or more serial images of the subject for phenotyping
atherosclerosis. In
some embodiments, tracking the progression of plaque can comprise analyzing
changes
and/or lack thereof in total plaque volume and/or volume of low-density non-
calcified
plaque, non-calcified plaque, and/or calcified plaque. In some embodiments,
tracking the
progression of plaque can comprise analyzing changes and/or lack thereof in
density of a
particular region of plaque and/or globally.
116971
Additionally or alternatively, in some embodiments, at block 2912, the
coronary arteries, branches, or segments can be visualized according to the
images
identified at block 2940¨e.g., those that show the coronary arteries,
branches, or segments
with the highest image quality. In some embodiments, the coronary arteries,
branches, or
segments can be visualized together in a "mix-and-match" approach (e.g.,
combining
images from different phases or series). Visualization can be performed
according to
various methods, including volume-rendered techniques, multiplanar reformation
or
reconstructions (MPRs), tabular forms, or others. In some embodiments, the
visualization
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can use the landmarks identified at block 2906 to align and generate a
composite image. In
some embodiments, the visualization can be stored in the medical image
database 2914.
[1698]
In the example provided above, this approach has been described within
the context of CT imaging of the coronary arteries. However, the methods,
systems, and
devices described herein can also be used with other imaging modalities and
other
anatomical structures as well. For example, the methods, systems, and devices
described
herein can also be used with ultrasound imaging (for example, of other
arterial beds (e.g.,
carotid, aorta, lower extremity, etc.)), MRI, or nuclear testing, among
others. Thus, the
methods, systems, and devices described herein can also be applied to image
reconstructions in other forms (e.g., reconstruction of an acquired CT volume
with different
thickness, different kernel, or in acquisitions with EKG synchronization, such
as, different
timing after the R wave of the EKG). The methods, systems, and devices
described herein
can also be applied to merge imaging information from different types of image
acquisitions (single energy CT vs. spectral CT) so as to be able to
reconstruct a specific
structure with a mix and or aggregation of different information (fusion)
obtain in all those
different components (including change through time).
[1699]
In some embodiments, the methods, systems, and devices described
herein can also be applied to depict -multi-phase" or -multi-series"
information in a virtual
4D way.
[1700]
The methods, systems, and devices described herein also be applied to
enhance the phenotypic richness of the artery/branch/segment (or other, such
as
structure/organ/patient) by combining methods for image visualization from
multiple
imaging modalities (e.g., CT for atherosclerosis, PET for inflammation, or
other).
117011
The methods, systems, and devices described herein can be used to fuse
information from previous images to illustrate the change over time after such
interventions
as medications, exercise or other.
[1702]
The methods, systems, and devices described herein can be used to
predict the future response, such as from pharmacologic treatment or aging.
[1703]
In some embodiments, the systems, processes, and methods described
herein are implemented using a computing system, such as the one illustrated
in Figure
29B. The example computer system 2932 is in communication with one or more
computing
systems 2950 and/or one or more data sources 2952 via one or more networks
2948. While
Figure 2 illustrates an embodiment of a computing system 2932, it is
recognized that the
functionality provided for in the components and modules of computer system
2932 can be
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combined into fewer components and modules, or further separated into
additional
components and modules.
117041
The computer system 2932 can comprise an improved CAD
measurement module 2944 that carries out the functions, methods, acts, and/or
processes
described herein. The improved CAD measurement and/or visualization module
2944 is
executed on the computer system 2932 by a central processing unit 2936
discussed further
below.
117051
In general the word "module," as used herein, refers to logic embodied
in hardware or firmware or to a collection of software instructions, having
entry and exit
points. Modules are written in a program language, such as JAVA, C, or C++, or
the like.
Software modules can be compiled or linked into an executable program,
installed in a
dynamic link library, or can be written in an interpreted language such as
BASIC, PERL,
LAU, PHP, or Python and any such languages. Software modules can be called
from other
modules or from themselves, and/or can be invoked in response to detected
events or
interruptions. Modules implemented in hardware include connected logic units
such as
gates and flip-flops, and/or can include programmable units, such as
programmable gate
arrays or processors.
117061
Generally, the modules described herein refer to logical modules that
can be combined with other modules or divided into sub-modules despite their
physical
organization or storage. The modules are executed by one or more computing
systems, and
can be stored on or within any suitable computer readable medium, or
implemented in-
whole or in-part within special designed hardware or firmware. Not all
calculations,
analysis, and/or optimization require the use of computer systems, though any
of the above-
described methods, calculations, processes, or analyses can be facilitated
through the use
of computers. Further, in some embodiments, process blocks described herein
can be
altered, rearranged, combined, and/or omitted.
117071
The computer system 2932 includes one or more processing units (CPU)
206, which can comprise a microprocessor. The computer system 2932 further
includes a
physical memory 210, such as random access memory (RAM) for temporary storage
of
information, a read only memory (ROM) for permanent storage of information,
and a mass
storage device 2934, such as a backing store, hard drive, rotating magnetic
disks, solid state
disks (S SD), flash memory, phase-change memory (PCM), 3D XPoint memory,
diskette,
or optical media storage device. Alternatively, the mass storage device can be
implemented
in an array of servers. Typically, the components of the computer system 2932
are
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connected to the computer using a standards-based bus system. The bus system
can be
implemented using various protocols, such as Peripheral Component Interconnect
(PCI),
Micro Channel, SCSI, Industrial Standard Architecture (ISA) and Extended ISA
(EISA)
architectures.
[1708]
The computer system 2932 includes one or more input/output (I/O)
devices and interfaces 2942, such as a keyboard, mouse, touch pad, and
printer. The I/O
devices and interfaces 2942 can include one or more display devices, such as a
monitor,
that allows the visual presentation of data to a user. More particularly, a
display device
provides for the presentation of GUIs as application software data, and multi-
media
presentations, for example. The I/O devices and interfaces 2942 can also
provide a
communications interface to various external devices. The computer system 2932
can
comprise one or more multi-media devices 2938, such as speakers, video cards,
graphics
accelerators, and microphones, for example.
[1709]
The computer system 2932 can run on a variety of computing devices,
such as a server, a Windows server, a Structure Query Language server, a Unix
Server, a
personal computer, a laptop computer, and so forth. In other embodiments, the
computer
system 2932 can run on a cluster computer system, a mainframe computer system
and/or
other computing system suitable for controlling and/or communicating with
large
databases, performing high volume transaction processing, and generating
reports from
large databases. The computing system 2932 is generally controlled and
coordinated by an
operating system software, such as z/OS, Windows, Linux, UNIX, BSD, PHP,
SunOS,
Solaris, MacOS, 'Cloud services or other compatible operating systems,
including
proprietary operating systems. Operating systems control and schedule computer
processes
for execution, perform memory management, provide file system, networking, and
I/O
services, and provide a user interface, such as a graphical user interface
(GUI), among other
things.
[1710]
The computer system 2932 illustrated in FIG. 29B is coupled to a
network 2948, such as a LAN, WAN, or the Internet via a communication link
2946 (wired,
wireless, or a combination thereof). Network 2948 communicates with various
computing
devices and/or other electronic devices. Network 2948 is communicating with
one or more
computing systems 2950 and one or more data sources 2952. The improved CAD
measurement and/or visualization module 2944 can access or can be accessed by
computing systems 2950 and/or data sources 2952 through a web-enabled user
access point.
Connections can be a direct physical connection, a virtual connection, and
other connection
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type. The web-enabled user access point can comprise a browser module that
uses text,
graphics, audio, video, and other media to present data and to allow
interaction with data
via the network 2948.
[1711]
The output module can be implemented as a combination of an all-points
addressable display such as a cathode ray tube (CRT), a liquid crystal display
(LCD), a
plasma display, or other types and/or combinations of displays. The output
module can be
implemented to communicate with input devices 2942 and they also include
software with
the appropriate interfaces which allow a user to access data through the use
of stylized
screen elements, such as menus, windows, dialogue boxes, tool bars, and
controls (for
example, radio buttons, check boxes, sliding scales, and so forth).
Furthermore, the output
module can communicate with a set of input and output devices to receive
signals from the
user.
[1712]
The computing system 2932 can include one or more internal and/or
external data sources (for example, data sources 2952). In some embodiments,
one or more
of the data repositories and the data sources described above can be
implemented using a
relational database, such as DB2, Sybase, Oracle, CodeBase, and Microsoft SQL
Server
as well as other types of databases such as a flat-file database, an entity
relationship
database, and object-oriented database, and/or a record-based database.
[1713]
The computer system 2932 can also access one or more databases 2952.
The databases 2952 can be stored in a database or data repository. The
computer system
2932 can access the one or more databases 2952 through a network 2948 or can
directly
access the database or data repository through I/O devices and interfaces
2942. The data
repository storing the one or more databases 2952 can reside within the
computer system
2932.
Examples of embodiments relating to improving accuracy of CAD measurements
[1714]
The following are non-limiting examples of certain embodiments of
systems and methods for improving accuracy of CAD measurements and/or other
related
features. Other embodiments may include one or more other features, or
different features,
that are discussed herein.
[1715]
Embodiment 1: A computer-implemented method for improving
accuracy of coronary artery disease measurements in non-invasive imaging
analysis, the
method comprising: accessing, by a computer system, image data of a heart of a
patient,
wherein the image data comprises multiple phases or series acquired during a
cardiac cycle;
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identifying, by the computer system and based on the image data, one or more
coronary
arteries, branches, or segments associated with the heart; determining, by the
computer
system, an image quality rank for each of the one or more coronary arteries,
branches, or
segments for each of the phases or series of the image data; determining, by
the computer
system, which phase or series of the image data provides the highest image
quality rank for
each of the one or more coronary arteries, branches, or segments; and
determining, by the
computer system, for each of the one more coronary arteries, branches, or
segments, one or
more CAD measurements or vascular morphology based on the phase or series of
the image
data that provides the highest image quality rank, wherein the computer system
comprises
a computer processor and an electronic storage medium.
[1716]
Embodiment 2: The computer-implemented method of Embodiment 1,
wherein determining the one or more CAD measurements or vascular morphology
based
on the phase or series of the image data that provides the highest image
quality rank
comprises analyzing, by the computer system, the phase or series to perform
quantitative
phenotyping of atherosclerosis.
[1717]
Embodiment 3: The computer-implemented method of Embodiment 2,
the quantitative phenotyping of atherosclerosis comprises analysis of one or
more of plaque
volume, plaque composition, or plaque progression.
[1718]
Embodiment 4: The computer-implemented method of Embodiment 3,
wherein the quantitative phenotyping of atherosclerosis is performed based at
least in part
on analysis of density values of one or more pixels of the medical image data
corresponding
to plaque.
[1719]
Embodiment 5: The computer-implemented method of Embodiment 4,
wherein the plaque volume comprises one or more of total plaque volume,
calcified plaque
volume, non-calcified plaque volume, or low-density non-calcified plaque
volume.
[1720]
Embodiment 6: The computer-implemented method of Embodiment 4,
wherein the density values comprise radiodensity values.
[1721]
Embodiment 7: The computer-implemented method of Embodiment 4,
wherein the plaque composition comprises composition of one or more of
calcified plaque,
non-calcified plaque, or low-density non-calcified plaque.
[1722]
Embodiment 8: The computer-implemented method of Embodiment 7,
wherein one or more of the calcified plaque, non-calcified plaque, of low-
density non-
calcified plaque is identified based at least in part on radiodensity values
of one or more
pixels of the medical image data corresponding to plaque.
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117231
Embodiment 9: The computer-implemented method of any of
Embodiments 1 to 8, further comprising visualizing, by the computer system,
the coronary
arteries, branches, and segments based on the identified phases or series.
117241
Embodiment 10: The computer-implemented method of Embodiment 9,
wherein visualizing the coronary arteries, branches, and segments comprises
generating,
by the computing system, a composite image from the phases or series having
the highest
image quality rank.
117251
Embodiment 11: The computer-implemented method of any of
Embodiment 1 to 10, further comprising identifying, by the computer system,
one or more
landmarks within each phase or series.
117261
Embodiment 12: The computer-implemented method of Embodiment
11, wherein the landmarks comprise anatomical landmarks associated with the
coronary
arteries, branches, and segments.
117271
Embodiment 13: The computer-implemented method of any of
Embodiments 1 to 12, wherein the medical image data is obtained using an
imaging
technique comprising one or more of computed tomography (CT), x-ray,
ultrasound,
echocardiography, intravascular ultrasound (IVUS), MR imaging, optical
coherence
tomography (OCT), nuclear medicine imaging, positron-emission tomography
(PET),
single photon emission computed tomography (SPECT), or near-field infrared
spectroscopy (NIRS).
117281
Embodiment 14: The computer-implemented method of any of
Embodiments 1 to 13, wherein visualizing the coronary arteries, branches, and
segments
based on the selected phases or series comprises presenting an image of each
coronary
arteries, branches, and segments based on the selected images corresponding to
the phase
or series associated with the highest image quality for that coronary artery,
branch, or
segment.
117291
Embodiment 15: A system for improving accuracy of coronary artery
disease measurements in non-invasive imaging analysis, the system comprising:
one or
more computer readable storage devices configured to store a plurality of
computer
executable instructions; and one or more hardware computer processors in
communication
with the one or more computer readable storage devices and configured to
execute the
plurality of computer executable instructions in order to cause the system to:
access image
data of a heart of a patient, wherein the image data comprises multiple phases
or series
acquired during a cardiac cycle; identify based on the image data, one or more
coronary
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arteries, branches, or segments associated with the heart; determine an image
quality rank
for each of the one or more coronary arteries, branches, or segments for each
of the phases
or series of the image data; determine which phase or series of the image data
provides the
highest image quality rank for each of the one or more coronary arteries,
branches, or
segments; and determine for each of the one more coronary arteries, branches,
or segments,
one or more CAD measurements or vascular morphology based on the phase or
series of
the image data that provides the highest image quality rank,
[1730]
Embodiment 16: The system of Embodiment 15, wherein determining
the one or more CAD measurements or vascular morphology based on the phase or
series
of the image data that provides the highest image quality rank comprises
analyzing, by the
computer system, the phase or series to perform quantitative phenotyping of
atherosclerosis.
[1731]
Embodiment 17: The system of Embodiment 16, wherein the
quantitative phenotyping of atherosclerosis comprises analysis of one or more
of plaque
volume, plaque composition, or plaque progression.
[1732]
Embodiment 18: The system of Embodiment 17, wherein the
quantitative phenotyping of atherosclerosis is performed based at least in
part on analysis
of density values of one or more pixels of the medical image data
corresponding to plaque.
[1733]
Embodiment 19: The system of Embodiment 18, wherein the plaque
volume comprises one or more of total plaque volume, calcified plaque volume,
non-
calcified plaque volume, or low-density non-calcified plaque volume.
[1734]
Embodiment 20: The system of Embodiment 18, wherein the density
values comprise radiodensity values.
117351
Embodiment 21: The system of Embodiment 18, wherein the plaque
composition comprises composition of one or more of calcified plaque, non-
calcified
plaque, or low-density non-calcified plaque.
[1736]
Embodiment 22: The system of Embodiment 21, wherein one or more
of the calcified plaque, non-calcified plaque, of low-density non-calcified
plaque is
identified based at least in part on radiodensity values of one or more pixels
of the medical
image corresponding to plaque.
[1737]
Embodiment 23: The system of any of Embodiments 15 t o22, further
comprising visualizing, by the computer system, the coronary arteries,
branches, and
segments based on the identified images.
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117381
Embodiment 24: The system of Embodiment 23, wherein visualizing the
coronary arteries, branches, and segments comprises generating, by the
computing system,
a composite image from the phases or series having the highest image quality
rank.
117391
Embodiment 25: The system of any of Embodiments 15 to 24, further
comprising identifying, by the computer system, one or more landmarks within
each phase
or series.
117401
Embodiment 26: The system of Embodiment 25, wherein the landmarks
comprise anatomical landmarks associated with the coronary arteries, branches,
and
segments.
117411
Embodiment 27: The system of any of Embodiments 15 to 26, wherein
the medical image is obtained using an imaging technique comprising one or
more of
computed tomography (CT), x-ray, ultrasound, echocardiography, intravascular
ultrasound
(IVUS), MR imaging, optical coherence tomography (OCT), nuclear medicine
imaging,
positron-emission tomography (PET), single photon emission computed tomography
(SPECT), or near-field infrared spectroscopy (NIRS).
117421
Embodiment 28: The system of any of Embodiments 15 to 27, wherein
visualizing the coronary arteries, branches, and segments based on the
selected images
comprises presenting an image of each coronary arteries, branches, and
segments based on
the selected images corresponding to the phase or series associated with the
highest image
quality for that coronary artery, branch, or segment.
Longitudinal diagnosis, risk assessment, characterization of heart disease
117431
Various embodiments described herein relate to systems, devices, and
methods for longitudinal image-based phenotyping to enhance drug discovery or
development. For example, some embodiments relate to image-based phenotyping
of high-
risk atherosclerosis features to accelerate drug discovery or development for
coronary
artery disease (CAD) or the like.
[1744]
Historically, the process for developing new drugs has been a lengthy
process involving much trial and error. In order to develop a new drug, one
must first
identify a target for the drug, the target being, for example, a cellular or
molecular target
for the drug to act upon in order to achieve a desired outcome in preventing
and/or treating
a disease. Example, targets for drugs for treating CAD include, LDL receptors,
PCSK9,
Factor VII, among others. Each of these cellular or biological targets plays,
for example, a
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role in the process of clotting blood. By affecting one or more of these
targets, the
associated step in the clotting process may be affected as a way of treating
CAD.
117451
Identifying a drug target using current methods is often imprecise and
requires considerable time (e.g., years or decades) for several reasons.
Currently, several
methodologies exist for identifying a target for a drug. Historically, in the
drug
development process, researches have gone after risk factors associated with
the disease
they are attempting to treat. In the case of CAD, researchers have considered
the
mechanisms associated with high cholesterol, high blood pressure, and/or high
glucose.
Each of these has been statistically correlated with an increased risk of CAD,
and
accordingly, by endeavoring to affect the mechanisms associated with these
risk factors,
one can hope to identify a target for treating and/or preventing surrogate by
considering the
mechanisms of CAD risk factors as a surrogate. A problem with using surrogates
in
identifying drug delivery is that the specific mechanisms associated with the
disease are
not identified. That is, there is no guarantee that the surrogate factor is
associated with a
cause of the disease, and not merely a correlated effect.
117461
Another way that targets have been sought, is by considering patient
outcomes over considerable lengths of time (e.g., 3 years, 5 years, 10 years,
20 years, or
longer). For example, studies can be performed that follow large groups of
patients (e.g.,
10,000 people) over long time periods (e.g., 10 years). Members of the patient
population
that experience CAD events can be identified. and biological markers (e.g.,
collected
through blood samples or other assays) of these patients can be compared with
similar
biological markers in members of the patient population that have not
experienced CAD
events. Differences between the biological markers of the patients who
experience adverse
events and those who do not can be useful in identifying targets for drug
development.
However, this process is lengthy as patient populations must be studied over
significant
lengths of time. Additionally, even with specifically identifying those
patients that
experience the disease, it can be difficult to identify targets associated
with the cause of the
disease.
117471
An improved method for identifying a target for drug development can
include examining the blood or other biological specimens of those who
currently
experience the disease. This can be done in a variety of ways. For example,
biological
samples of those with the disease (cases) can be compared with those that do
not have the
disease (controls). Another example, can be examining those patients on the
extremes. For
example, one can examine biological samples from patients who, for various
reasons, one
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would expect to suffer from the disease, but who do not. Similarly, it may be
extremely
valuable to examine biological samples from people who have the disease, but
do not have
any risk factors commonly associated with the disease.
117481
In order to gain valuable insight by studying biological samples from
those who do or do not have the disease, it is important to be able to
accurately understand
and characterize the level of disease in those patients. Accordingly, this
application
contemplates leveraging the image-based CAD measurement and analysis tools
described
herein to establish baseline and/or follow-up imaging that can be used to
characterize and
quantify a patient's disease. This imaging can be coupled with bioassay
analysis to
determine relationships between the bioassay analysis and the disease as a way
to identify
targets (e.g., molecular or cellular targets) for drug discovery and
development. This can
be done in several ways.
117491
In one example, image-based CAD phenotyping can be used to identify
and quantify the CAD of various patients. The same patients can undergo
bioassay
analysis. The results of the image-based CAD phenotyping and bioassay analysis
can be
related for each patient. The results can be compared between patients with
high levels of
CAD (cases) and patients with low levels of CAD (controls). Examining the
differences in
the bioassays between the case and control groups can be useful in identifying
targets for
drug discovery and development.
117501
In another example, patients can undergo image-based CAD
phenotyping and associated bioassay analysis at different points in time. For
example, first
image-based CAD phenotyping and associated bioassay analysis at a first time
may
establish a baseline for a patient. At a later time, for example, 1 year, 5
years, or 10 years
later, or at a time when the patient's CAD has developed or progressed,
additional image-
based CAD phenotyping and associated bioassay analysis can be performed.
Comparison
of the changes in CAD and the changes in the bioassay analysis between the two
time
periods can be used to identify targets for drug discovery and development.
This type of
dynamic evaluation can be accomplished in several ways.
117511
In one example, upon determining that a patient's CAD has progressed
(or improved) between the two time points, the bioassay from the initial,
first time point
can be analyzed to determine targets for drug discovery or development.
117521
In another example, upon determining that a patient's CAD has
progressed (or improved) between the two time points, the bioassay from the
initial, first
time point and the bioassay from the later time point can be examined to
determine targets
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for drug discovery or development. One can examine the association between
bioassay at
the initial timepoint to baseline burden or changes in disease over time.
Alternatively or
additionally, one can look at the bioassay from the later time point and, upon
identification
of an individual who rapidly progresses, regresses, transforms, one can look
at the bioassay
after the change has occurred. Or, one can examine the changes between the
initial
timepoint and the later timepoint (as a parallel marker of change), for
example, to examine
the changes in disease in relationship to the changes in bioassay.
[1753]
As described herein and shown, for example, in FIG. 30A, in some
embodiments, a potential drug target for treatment of coronary atherosclerosis
is identified,
and administered to an individual (block 3052). At the same time, a control
individual who
is not administered the potential drug candidate is also identified (block
3052). At block
3054, both the test case and the control individual can undergo contrast-
enhanced CT
imaging of the heart and heart arteries. At block 106, a computer system can
be configured
to extract atherosclerosis features and vascular morphology characteristics
for each
individual (the test case and the control).
[1754]
Additionally, at block 3058, biological specimens are obtained from the
test case and control individuals. Such biological specimens can include, for
example,
saliva, blood, stool and others. Assays can be performed to determine the
relationship of
coronary atherosclerosis and vascular morphology parameters to biological
specimens,
including for genetics, proteomics, transcriptomics, metabolomics,
microbiomics, and
others.
[1755]
At block 3060, a computer system can be configured to associate the
atherosclerosis features and vascular morphology characteristics by CT to the
output of the
biological specimen assays (e.g., specific proteomic signatures). These
atherosclerosis
features and vascular morphology features can be specific and associated with
clinically-
manifest adverse events (e.g., MACE, MI, or death), and disease features
include volume,
composition, remodeling, location, diffuseness, and direction.
[1756]
In some embodiments, at block 3062, based upon the output of the
biological specimen assays associated with the second algorithm (e.g.,
coronary
atherosclerosis burden, high-risk plaque), biological specimen assay outputs
are identified
as -targets" for drug discovery or development.
[1757]
In some embodiments, the principles described above can further be
extended to image-based phenotyping of high-risk atherosclerosis progression
to accelerate
drug discovery or development. For example, the principles can be extended by
performing
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serial CT imaging for changes in atherosclerosis and vascular morphology. An
example
method can include, for example, repeat CT scans performed in the future
(e.g., 1 month,
1 year, 2 years). The atherosclerosis features and vascular morphology
characteristics are
quantified by the aforementioned algorithms. Afterwards, a computer system can
be
configured to relate the change in atherosclerosis features and vascular
morphology
characteristics to the biological specimen assays.
[1758]
In some embodiments, the computer system can further be developed to
quantify the quantified changes to the biological specimen assay output (e.g.,
specific
proteomic signatures). Based upon the output of the biological specimen assays
(e.g.,
proteomic signatures) that are common to both the second and the fourth
algorithms (e.g.,
coronary plaque progression, non-reduction in high-risk plaque), biological
specimen assay
outputs are identified as "targets" for drug discovery or development.
[1759]
In some embodiments, these principles can still be extended even further
to image-based phenotyping of atherosclerosis stabilization or progression to
identify
optimal drug responders or non-responders. For example, the principles can be
extended
by performing serial CT imaging for changes in atherosclerosis and vascular
morphology.
An example method can include, for individuals treated with a specific drug,
repeating CT
scans in the future (e.g., 1 month, 1 year, 2 years). The atherosclerosis
features and vascular
morphology characteristics are quantified as described above. The computer
system can
further be configured to relate the change in atherosclerosis features and
vascular
morphology characteristics to the biological specimen assays. Individuals
treated with this
specific drug are classified as responders (e.g., reduced plaque progression)
versus non-
responders (e.g., continued plaque progression, continued high-risk plaque
features, new
high-risk plaques, etc.). The computer system can further relate responders
versus non-
responders to the biological specimen assay outputs.
[1760]
The approach described herein can be used with: multivariable
adjustment of CAD risk factors and treatment and patient
demographics/biometrics;
protein, serum or urine markers, cytologic or histologic information, diet,
exercise, digital
wearables; combination targets of genomics and proteomics and/or microbiomics
and
metabolomics, etc.; combining different image features, and/or different
information from
different image modalities (e.g., liver steatosis from an ultrasound, delayed
enhancement
from an MRI).
[1761]
In some embodiments, the systems, processes, and methods described
herein are implemented using a computing system, such as the one illustrated
in Figure 2.
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The example computer system 3002 is in communication with one or more
computing
systems 3020 and/or one or more data sources 3022 via one or more networks
3018. While
Figure 2 illustrates an embodiment of a computing system 3002, it is
recognized that the
functionality provided for in the components and modules of computer system
3002 can be
combined into fewer components and modules, or further separated into
additional
components and modules.
[1762]
The computer system 3002 can comprise an image-based phenotyping
module 3014 that carries out the functions, methods, acts, and/or processes
described
herein. The image-based phenotyping module 3014 is executed on the computer
system
3002 by a central processing unit 3006 discussed further below.
[1763]
In general the word "module," as used herein, refers to logic embodied
in hardware or firmware or to a collection of software instructions, having
entry and exit
points. Modules are written in a program language, such as JAVA, C, or C++, or
the like.
Software modules can be compiled or linked into an executable program,
installed in a
dynamic link library, or can be written in an interpreted language such as
BASIC, PERL,
LAU, PHP, or Python and any such languages. Software modules can be called
from other
modules or from themselves, and/or can be invoked in response to detected
events or
interruptions. Modules implemented in hardware include connected logic units
such as
gates and flip-flops, and/or can include programmable units, such as
programmable gate
arrays or processors.
[1764]
Generally, the modules described herein refer to logical modules that
can be combined with other modules or divided into sub-modules despite their
physical
organization or storage. The modules are executed by one or more computing
systems, and
can be stored on or within any suitable computer readable medium, or
implemented in-
whole or in-part within special designed hardware or firmware. Not all
calculations,
analysis, and/or optimization require the use of computer systems, though any
of the above-
described methods, calculations, processes, or analyses can be facilitated
through the use
of computers. Further, in some embodiments, process blocks described herein
can be
altered, rearranged, combined, and/or omitted.
[1765]
The computer system 3002 includes one or more processing units (CPU)
3006, which can comprise a microprocessor. The computer system 3002 further
includes a
physical memory 3010, such as random access memory (RAM) for temporary storage
of
information, a read only memory (ROM) for permanent storage of information,
and a mass
storage device 3004, such as a backing store, hard drive, rotating magnetic
disks, solid state
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disks (S SD), flash memory, phase-change memory (PCM), 3D XPoint memory,
diskette,
or optical media storage device. Alternatively, the mass storage device can be
implemented
in an array of servers. Typically, the components of the computer system 3002
are
connected to the computer using a standards based bus system. The bus system
can be
implemented using various protocols, such as Peripheral Component Interconnect
(PCI),
Micro Channel, SCSI, Industrial Standard Architecture (ISA) and Extended ISA
(EISA)
architectures.
117661
The computer system 3002 includes one or more input/output (I/O)
devices and interfaces 3012, such as a keyboard, mouse, touch pad, and
printer. The I/O
devices and interfaces 3012 can include one or more display devices, such as a
monitor,
that allows the visual presentation of data to a user. More particularly, a
display device
provides for the presentation of GUIs as application software data, and multi-
media
presentations, for example. The I/O devices and interfaces 3012 can also
provide a
communications interface to various external devices. The computer system 3002
can
comprise one or more multi-media devices 3008, such as speakers, video cards,
graphics
accelerators, and microphones, for example.
117671
The computer system 3002 can run on a variety of computing devices,
such as a server, a Windows server, a Structure Query Language server, a Unix
Server, a
personal computer, a laptop computer, and so forth. In other embodiments, the
computer
system 3002 can run on a cluster computer system, a mainframe computer system
and/or
other computing system suitable for controlling and/or communicating with
large
databases, performing high volume transaction processing, and generating
reports from
large databases. The computing system 3002 is generally controlled and
coordinated by an
operating system software, such as z/OS, Windows, Linux, UNIX, BSD, PHP,
SunOS,
Solaris, MacOS, ICloud services or other compatible operating systems,
including
proprietary operating systems. Operating systems control and schedule computer
processes
for execution, perform memory management, provide file system, networking, and
I/O
services, and provide a user interface, such as a graphical user interface
(GUI), among other
things.
117681
The computer system 3002 illustrated in FIG. 30B is coupled to a
network 3018, such as a LAN, WAN, or the Internet via a communication link
3016 (wired,
wireless, or a combination thereof). Network 3018 communicates with various
computing
devices and/or other electronic devices. Network 3018 is communicating with
one or more
computing systems 3020 and one or more data sources 3022. The image-based
phenoty ping
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module 3014 can access or can be accessed by computing systems 3020 and/or
data sources
3022 through a web-enabled user access point. Connections can be a direct
physical
connection, a virtual connection, and other connection type. The web-enabled
user access
point can comprise a browser module that uses text, graphics, audio, video,
and other media
to present data and to allow interaction with data via the network 3018.
[1769]
The output module can be implemented as a combination of an all-points
addressable display such as a cathode ray tube (CRT), a liquid crystal display
(LCD), a
plasma display, or other types and/or combinations of displays. The output
module can be
implemented to communicate with input devices 3012 and they also include
software with
the appropriate interfaces which allow a user to access data through the use
of stylized
screen elements, such as menus, windows, dialogue boxes, tool bars, and
controls (for
example, radio buttons, check boxes, sliding scales, and so forth).
Furthermore, the output
module can communicate with a set of input and output devices to receive
signals from the
user.
[1770]
The computing system 3002 can include one or more internal and/or
external data sources (for example, data sources 3022). In some embodiments,
one or more
of the data repositories and the data sources described above can be
implemented using a
relational database, such as DB2, Sybase, Oracle, CodeBase, and Microsoft SQL
Server
as well as other types of databases such as a flat-file database, an entity
relationship
database, and object-oriented database, and/or a record-based database.
[1771]
The computer system 3002 can also access one or more databases 3022.
The databases 3022 can be stored in a database or data repository. The
computer system
3002 can access the one or more databases 3022 through a network 3018 or can
directly
access the database or data repository through I/O devices and interfaces
3012. The data
repository storing the one or more databases 3022 can reside within the
computer system
3002.
Examples of embodiments relating to longitudinal diagnosis, risk assessment,
characterization of heart disease
[1772]
The following are non-limiting examples of certain embodiments of
systems and methods for determining longitudinal diagnosis, risk assessment,
characterization of heart disease and/or other related features. Other
embodiments may
include one or more other features, or different features, that are discussed
herein.
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[1773]
Embodiment 1: A computer-implemented method for image-based
phenotyping to enhance drug discovery or development, the method comprising:
accessing,
by a computer system, a first medical image of a test case patient; analyzing,
by the
computer system, the first medical image of the test case patient to perform
quantitative
phenotyping of atherosclerosis associated with the test case patient, the
quantitative
phenotyping of atherosclerosis comprising analysis of one or more of plaque
volume,
plaque composition, or plaque progression; accessing, by the computer system,
a second
medical image of a control patient; analyzing, by the computer system, the
second medical
image of the test case patient to perform quantitative phenotyping of
atherosclerosis
associated with the control patient, the quantitative phenotyping of
atherosclerosis
comprising analysis of one or more of plaque volume, plaque composition, or
plaque
progression; relating, by the computer system, outputs of assays performed on
biological
specimens obtained from the test case patient and the control patient to the
atherosclerosis
features and vascular morphology characteristics associated with the test case
patient and
the control patient, respectively; and based on the related outputs of the
assays and
atherosclerosis features and vascular morphology characteristics, identifying,
by the
computer system, biological specimen assay outputs as targets for drug
discovery or
development, wherein the computer system comprises a computer processor and an
electronic storage medium.
[1774]
Embodiment 2: The computer-implemented method of Embodiment 1,
wherein the targets for drug discovery and development are identified based on
comparison
of the test case patient to the control patient.
[1775]
Embodiment 3: The computer-implemented method of Embodiment 2,
wherein the comparison of the test case patient to the control patient is
based on comparing
the quantitative phenotyping of atherosclerosis of the test case patient and
the control
patient.
[1776]
Embodiment 4: The computer-implemented method of Embodiment 3,
wherein the comparison of the test case patient to the control patient is
based on comparing
changes of the quantitative phenotyping of atherosclerosis of the test case
patient and the
control patient over time.
[1777]
Embodiment 5: The computer-implemented method of Embodiment 4,
wherein the changes are evaluated based on quantitative phenotyping performed
at greater
than two points of time.
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117781
Embodiment 6: The computer-implemented method of any of
Embodiments 1 to 5, wherein the comparison of the test case patient to the
control patient
is based on comparing the outputs of assays performed on biological specimens
obtained
from the test case patient and the control patient.
117791
Embodiment 7: The computer-implemented method of Embodiment 6,
wherein the comparison of the test case patient to the control patient is
based on comparing
changes of the outputs of assays performed on biological specimens obtained
from the test
case patient and the control patient over time.
117801
Embodiment 8: The computer-implemented method of Embodiment 4,
wherein the changes are evaluated based on quantitative phenotyping performed
at greater
than two points of time.
117811
Embodiment 9: The computer-implemented method of any of
Embodiments 1 to 8, wherein the biological specimen assay outputs as targets
for drug
discovery or development comprise one or more of genomics, proteomics,
transcriptomics,
metabolomics, microbiomics, and epigenetics.
117821
Embodiment 10: The computer-implemented method of any of
Embodiments 1 to 9, wherein the quantitative phenotyping is further comprises
an analysis
of one or more of plaque remodeling, plaque location, plaque diffuseness, and
plaque
direction.
117831
Embodiment 11: The computer-implemented method of any of
Embodiments 1 to 10, wherein the quantitative phenotyping of atherosclerosis
is performed
based at least in part on analysis of density values of one or more pixels of
the medical
image corresponding to plaque.
117841
Embodiment 12: The computer-implemented method of Embodiment
11, wherein the plaque volume comprises one or more of total plaque volume,
calcified
plaque volume, non-calcified plaque volume, or low-density non-calcified
plaque volume.
117851
Embodiment 13: The computer-implemented method of Embodiment
11, wherein the density values comprise radiodensity values.
117861
Embodiment 14: The computer-implemented method of Embodiment
11, wherein the plaque composition comprises composition of one or more of
calcified
plaque, non-calcified plaque, or low-density non-calcified plaque.
117871
Embodiment 15: The computer-implemented method of Embodiment
14, wherein one or more of the calcified plaque, non-calcified plaque, of low-
density non-
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calcified plaque is identified based at least in part on radiodensity values
of one or more
pixels of the medical image corresponding to plaque.
117881
Embodiment 16: The computer-implemented method of any of
Embodiments 1 to 15, wherein the biologic specimens are obtained from one or
more of
the following: saliva, blood, or stool.
117891
Embodiment 17: The computer-implemented method of any of
Embodiments 1 to 16, wherein the biologic specimens are analyzed to determine
one or
more of genetics, proteomics, transcriptomics, metabolomics, microbiomics.
117901
Embodiment 18: The computer-implemented method of any of
Embodiments 1 to 17, wherein the medical image comprises a Computed Tomography
(CT) image.
117911
Embodiment 19: The computer-implemented method of any of
Embodiments 1 to 10, wherein the medical image is obtained using an imaging
technique
comprising one or more of CT, x-ray, ultrasound, echocardiography,
intravascular
ultrasound (IVUS), MR imaging, optical coherence tomography (OCT), nuclear
medicine
imaging, positron-emission tomography (PET), single photon emission computed
tomography (SPECT), or near-field infrared spectroscopy (NIRS).
117921
Embodiment 20: A system for improving accuracy of coronary artery
disease measurements in non-invasive imaging analysis, the system comprising:
one or
more computer readable storage devices configured to store a plurality of
computer
executable instructions; and one or more hardware computer processors in
communication
with the one or more computer readable storage devices and configured to
execute the
plurality of computer executable instructions in order to cause the system to:
access a first
medical image of a test case patient; analyze first medical image of the test
case patient to
perform quantitative phenotyping of atherosclerosis associated with the test
case patient,
the quantitative phenotyping of atherosclerosis comprising analysis of one or
more of
plaque volume, plaque composition, or plaque progression; access a second
medical image
of a control patient; analyze the second medical image of the test case
patient to perform
quantitative phenotyping of atherosclerosis associated with the control
patient, the
quantitative phenotyping of atherosclerosis comprising analysis of one or more
of plaque
volume, plaque composition, or plaque progression; relate outputs of assays
performed on
biological specimens obtained from the test case patient and the control
patient to the
atherosclerosis features and vascular morphology characteristics associated
with the test
case patient and the control patient, respectively; and based on the related
outputs of the
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assays and atherosclerosis features and vascular morphology characteristics,
identify
biological specimen assay outputs as targets for drug discovery or development
[1793]
Embodiment 21: The system of Embodiment 20, wherein the targets for
drug discovery and development are identified based on comparison of the test
case patient
to the control patient.
[1794]
Embodiment 22: The system of Embodiment 21, wherein the
comparison of the test case patient to the control patient is based on
comparing the
quantitative phenotyping of atherosclerosis of the test case patient and the
control patient.
[1795]
Embodiment 23 The system of Embodiment 22, wherein the comparison
of the test case patient to the control patient is based on comparing changes
of the
quantitative phenotyping of atherosclerosis of the test case patient and the
control patient
over time.
[1796]
Embodiment 24: The system of Embodiment 23, wherein the changes
are evaluated based on quantitative phenotyping performed at greater than two
points of
time.
[1797]
Embodiment 25: The system of any of Embodiments 20 to 24, wherein
the comparison of the test case patient to the control patient is based on
comparing the
outputs of assays performed on biological specimens obtained from the test
case patient
and the control patient.
[1798]
Embodiment 26: The system of Embodiment 25, wherein the
comparison of the test case patient to the control patient is based on
comparing changes of
the outputs of assays performed on biological specimens obtained from the test
case patient
and the control patient over time.
117991
Embodiment 27: The system of Embodiment 26, wherein the changes
are evaluated based on quantitative phenotyping performed at greater than two
points of
time.
[1800]
Embodiment 28: The system of any of Embodiments 20 to 27, wherein
the biological specimen assay outputs as targets for drug discovery or
development
comprise one or more of genomics, proteomics, transcriptomics, metabolomics,
microbiomics, and epigenetics.
[1801]
Embodiment 29: The system of any of Embodiments 20 to 28, wherein
the quantitative phenotyping is further comprises an analysis of one or more
of plaque
remodeling, plaque location, plaque diffuseness, and plaque direction.
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[1802]
Embodiment 30: The system of any of Embodiments 20 to 29, wherein
the quantitative phenotyping of atherosclerosis is performed based at least in
part on
analysis of density values of one or more pixels of the medical image
corresponding to
plaque.
[1803]
Embodiment 31: The system of Embodiment 29, wherein the plaque
volume comprises one or more of total plaque volume, calcified plaque volume,
non-
calcified plaque volume, or low-density non-calcified plaque volume.
[1804]
Embodiment 32: The system of Embodiment 29, wherein the density
values comprise radiodensity values.
118051
Embodiment 33: The system of Embodiment 29, wherein the plaque
composition comprises composition of one or more of calcified plaque, non-
calcified
plaque, or low-density non-calcified plaque.
[1806]
Embodiment 34: The system of Embodiment 33, wherein one or more
of the calcified plaque, non-calcified plaque, of low-density non-calcified
plaque is
identified based at least in part on radiodensity values of one or more pixels
of the medical
image corresponding to plaque.
[1807]
Embodiment 35: The system of any of Embodiments 20 to 34, wherein
the biologic specimens are obtained from one or more of the following: saliva,
blood, or
stool.
[1808]
Embodiment 36: The system of any of Embodiments 20 to 35, wherein
the biologic specimens are analyzed to determine one or more of genetics,
proteomics,
transcriptomics, metabolomics, microbiomics.
[1809]
Embodiment 37: The system of any of Embodiments 20 to 36, wherein
the medical image comprises a Computed Tomography (CT) image.
[1810]
Embodiment 38: The system of any of Embodiments 20 to 37, wherein
the medical image is obtained using an imaging technique comprising one or
more of CT,
x-ray, ultrasound, echocardiography, intravascular ultrasound (IVUS), MR
imaging,
optical coherence tomography (OCT), nuclear medicine imaging, positron-
emission
tomography (PET), single photon emission computed tomography (SPECT), or near-
field
infrared spectroscopy (NIRS).
Other Embodiment(s)
[1811]
Although this invention has been disclosed in the context of certain
embodiments and examples, it will be understood by those skilled in the art
that the
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invention extends beyond the specifically disclosed embodiments to other
alternative
embodiments and/or uses of the invention and obvious modifications and
equivalents
thereof In addition, while several variations of the embodiments of the
invention have been
shown and described in detail, other modifications, which are within the scope
of this
invention, will be readily apparent to those of skill in the art based upon
this disclosure. It
is also contemplated that various combinations or sub-combinations of the
specific features
and aspects of the embodiments may be made and still fall within the scope of
the invention.
It should be understood that various features and aspects of the disclosed
embodiments can
be combined with, or substituted for, one another in order to form varying
modes of the
embodiments of the disclosed invention. Any methods disclosed herein need not
be
performed in the order recited. Thus, it is intended that the scope of the
invention herein
disclosed should not be limited by the particular embodiments described above.
[1812]
Conditional language, such as, among others, "can,- -could,- "might,"
or -may," unless specifically stated otherwise, or otherwise understood within
the context
as used, is generally intended to convey that certain embodiments include,
while other
embodiments do not include, certain features, elements and/or steps. Thus,
such conditional
language is not generally intended to imply that features, elements and/or
steps are in any
way required for one or more embodiments or that one or more embodiments
necessarily
include logic for deciding, with or without user input or prompting, whether
these features,
elements and/or steps are included or are to be performed in any particular
embodiment.
The headings used herein are for the convenience of the reader only and are
not meant to
limit the scope of the inventions or embodiments.
[1813]
Further, while the methods and devices described herein may be
susceptible to various modifications and alternative forms, specific examples
thereof have
been shown in the drawings and are herein described in detail. It should be
understood,
however, that the invention is not to be limited to the particular forms or
methods disclosed,
but, to the contrary, the invention is to cover all modifications,
equivalents, and alternatives
falling within the spirit and scope of the various implementations described
and the
appended embodiments. Further, the disclosure herein of any particular
feature, aspect,
method, property, characteristic, quality, attribute, element, or the like in
connection with
an implementation or embodiment can be used in all other implementations or
embodiments set forth herein. Any methods disclosed herein need not be
performed in the
order recited. The methods disclosed herein may include certain actions taken
by a
practitioner; however, the methods can also include any third-party
instruction of those
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actions, either expressly or by implication. The ranges disclosed herein also
encompass any
and all overlap, sub-ranges, and combinations thereof. Language such as "up
to," "at least,"
"greater than," "less than," "between," and the like includes the number
recited. Numbers
preceded by a term such as -about" or -approximately" include the recited
numbers and
should be interpreted based on the circumstances (e.g., as accurate as
reasonably possible
under the circumstances, for example +5%, +10%, +15%, etc.). For example,
"about 3.5
mm- includes "3.5 mm.- Phrases preceded by a term such as "substantially-
include the
recited phrase and should be interpreted based on the circumstances (e.g., as
much as
reasonably possible under the circumstances). For example, -substantially
constant"
includes "constant." Unless stated otherwise, all measurements are at standard
conditions
including temperature and pressure.
[1814]
As used herein, a phrase referring to "at least one of" a list of items
refers
to any combination of those items, including single members. As an example,
"at least one
of: A, B, or C" is intended to cover: A. B, C, A and B, A and C, B and C, and
A, B, and
C. Conjunctive language such as the phrase "at least one of X, Y and
unless specifically
stated otherwise, is otherwise understood with the context as used in general
to convey that
an item, term, etc. may be at least one of X, Y or Z. Thus, such conjunctive
language is
not generally intended to imply that certain embodiments require at least one
of X, at least
one of Y, and at least one of Z to each be present.
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