Note: Descriptions are shown in the official language in which they were submitted.
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SELECTIVE SAMPLING FOR ASSESSING STRUCTURAL SPATIAL FREQUENCIES
WITH SPECIFIC CONTRAST MECHANISMS
REFERENCE TO RELATED APPLICATIONS
[PARA 11 This application is a continuation in part of application serial
no. 15/288,974
filed on 10/07/2016 which is a continuation in part of 15/167,828 filed on
05/27/2016 which
is a continuation in part of application serial no. 14/840,327 filed on
08/31/2015 now US
Patent no. 9366738. Application 14/840,327 relies on the priority of US
provisional
applications serial no. 62/044,321 filed on 09/01/2014 entitled SELECTIVE
SAMPLING
MAGNETIC RESONANCE-BASED METHOD FOR ASSESSING STRUCTURAL
SPATIAL FREQUENCIES, serial no. 62/064,206 filed on 10/15/2014 having the same
title
and serial no. 62/107,465 filed on 01/25/2015 entitled MICRO-TEXTURE
CHARACTERIZATION BY MRI, the disclosures of which are incorporate herein by
reference. Application 15/167,828 additionally relies on the priority of
provisional
application serial no. 62/302,577 filed on 03/02/2016 entitled METHOD FOR
ASSESSING
STRUCTURAL SPATIAL FREQUENCIES USING HYBRID SAMPLING WITH LOW
OR INCREASED GRADIENT FOR ENHANCEMENT OF VERY LOW NOISE
SELECTIVE SAMPLING WITH NO GRADIENT. Application 15/288,974 relies on the
priority of US provisional application serial nos. 62/238,121 filed on
10/07/2015 entitled
SELECTIVE SAMPLING MAGNETIC RESONANCE-BASED METHOD FOR
ASSESSING STRUCTURAL SPATIAL FREQUENCIES and provisional application
serial no. 62/382,695 filed on 09/01/2016 entitled SELECTIVE SAMPLING FOR
ASSESSING STRUCTURAL SPATIAL FREQUENCIES WITH SPECIFIC CONTRAST
MECHANISMS. The referenced applications all have a common assignee with the
present
application and the disclosures thereof are incorporated herein by reference.
BACKGROUND
Field of The Invention
[PARA 21 The herein claimed method relates to the field of diagnostic
assessment of
fine textures in biological systems for pathology assessment and disease
diagnosis, and in
material and structural evaluation in industry and in engineering research.
More
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specifically, the embodiments disclosed herein provide methods for repeat
measurement of
signal at k-values associated with the spatial organization of biologic tissue
texture, with the
MRI machine gradients turned off and at k-values in an associated neighborhood
with a low
gradient applied during signal acquisition. Various contrast mechanisms can be
used in
conjunction with the embodiments disclosed herein; in cases of novel contrast
mechanisms,
such as DWI, DTI, or ASL, which, in addition to changing timing, may require
additional
tailored RF and gradient pulses, the novel contrast can be incorporated into
the
embodiment, forming an integrated sequence; another method would be one
wherein data
acquisition by the novel contrast sequence is run in parallel with data
acquisition by the
disclosed embodiments and the data compared, with the data obtained by the
embodiments
disclosed providing direct measure of the fine tissue texture for calibrating
and
understanding the data obtained by another contrast method. That is, the use
of varying
contrast methods allows use of the embodiments in conjunction with other MRI
imaging
and measurement methods either in an integrated form, wherein the timing and
any
additional RF and gradient pulses used to set contrast are combined into one
pulse sequence,
or in parallel operation wherein data acquired using the disclosed embodiments
is acquired
and compared as a calibration/complimentary assessment of the data acquired by
other
contrast mechanisms. The data obtained either by the integrated method or the
complementary method can further be mapped across a region of tissue to assess
the spatial
variation in pathology. The methods enable in vivo assessment, towards
diagnosis and
monitoring of disease and therapy-induced textural changes in tissue.
Representative targets
of the technique are: 1) for assessment of changes to trabecular architecture
caused by bone
disease, allowing assessment of bone health and fracture risk; 2) evaluation
of fibrotic
development in soft tissue diseases such as, for example, liver, lung, and
heart disease; 3)
changes to fine structures in neurologic diseases, such as, for example, the
various forms of
dementia, Multiple Sclerosis (MS), or in cases of brain injury and downstream
neuro-
pathology as in, for example, Traumatic Brain Injury (TBI) and Chronic
Traumatic
Encephalopathy (CTE), or for characterization and monitoring of abnormal
neurologic
conditions such as autism and schizophrenia; 4) assessment of vascular changes
such as in
the vessel network surrounding tumors or associated with development of CVD
(Cerebrovascular Disease), and of changes in mammary ducting in response to
tumor
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growth; 5) assessment of fibrotic diseases, from lung and liver fibrosis, to
cardiac and cystic
fibrosis, pancreatic fibrosis, muscular dystrophy, bladder and heart diseases,
and
myelofibrosis, in which fibrotic structures replace bone marrow, cancers, such
as breast
cancer and prostate cancer, muscle diseases, such as Central Core Disease, in
which the
lobular formations in muscle become infiltrated with fibrotic development; 6)
lung disease
diagnosis such as Idiopathic Pulmonary Fibrosis (IPFL The invention also has
applications
in assessment of fine structures for a range of industrial purposes such as
measurement of
material properties in manufacturing or in geology to characterize various
types of rock, as
well as other uses for which measurement of fine structures/textures is
needed.
Description of The Related Art
[PARA 31 Though fine textural changes in tissue have long been recognized
as the
earliest markers in a wide range of diseases, robust clinical assessment of
fine texture
remains elusive, the main difficulty arising from blurring caused by subject
motion over the
time required for data acquisition.
[PARA 41 Early and accurate diagnosis is key to successful disease
management.
Though clinical imaging provides much information on pathology, many of the
tissue
changes that occur as a result of disease onset and progression, or as a
result of therapy, are
on an extremely fine scale, often down to tens of microns. Changes in fine
tissue texture
have been recognized for many years by diagnosticians, including radiologists
and
pathologists as the earliest harbinger of a large range of diseases, but in
vivo assessment and
measurement of fine texture has remained outside the capabilities of current
imaging
technologies. For instance, differential diagnosis of obstructive lung disease
relies on a
textural presentation in the lung parenchyma, but the robustness of the
Computed
Tomography (CT) measure of early stage disease is limited. Trabecular bone
microarchitecture, the determinant of fracture risk in aging bone, has also
remained elusive
due to image blurring from patient motion during Magnetic Resonance (MR)
imaging scans.
Post processing analysis of MR-images is sometimes used to try to
differentiate image
textures in structures such as tumors and white matter. (DRABYCZ, S., et al.;
"Image
texture characterization using the discrete orthogonal S-transform"; Journal
of Digital
Imaging, Vol. 22, No 6, 2009. KHIDER, M., et al.; "Classification of
trabecular bone
texture from MRI and CT scan images by multi-resolution analysis"; 29th Annual
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International Conference of the IEEE Engineering in Medicine and Biology
Society, EMBS
2007.) But post processing analysis is limited in effect as it doesn't deal
with the underlying
problem that prevents high resolution acquisition of textural information,
i.e. subject
motion. (MACLAREN, J. et al.; "Measurement and correction of microscopic head
motion
during magnetic resonance imaging of the brain", PLOS/ONE, Nov. 7, 2012.
MACLARAN, J. et al.; "Prospective motion correction in brain imaging: a
review;
Magnetic Resonance in Medicine, Vol. 69, 2013.)
[PARA 51 The main sources of motion affecting MR imaging are cardiac
pulsatile
motion, respiratory-induced motion and twitching. The first two are quasi-
cyclic, the usual
approach to which is gating at the slowest phase of motion. However, even with
gating,
there is sufficient motion between acquisitions to cause loss of spatial phase
coherence at
the high k-values of interest for texture measurements. This problem is
exacerbated by the
fact that motion may not be perfectly cyclic, and often originates from
combined sources.
Twitching is rapid, inducing random displacements, and hence it is not
possible to maintain
coherence at the high k-values of interest when measuring texture.
[PARA 61 While Positron Emission Tomography (PET) provides valuable
diagnostic
information, it is not capable of resolution below about 5mm and relies on the
use of
radioactive tracers for imaging as well as x-ray beams for positioning,
raising dose
concerns, especially if repeat scanning is needed. (BERRINGTON DE GONZALEZ, A.
et
al.; "Projected cancer risks from Computed Tomographic scans performed in the
United
States in 2007"; JAMA Internal Medicine, Vol. 169, No. 22, Dec 2009.) Further,
PET
imaging is extremely costly, requiring a nearby cyclotron. CT resolution down
to 0.7 mm is
possible in theory, though this is obtained at high radiation dose and is
subject to reduction
by patient motion over the few minute scan time. The non-negligible risk from
the
associated radiation dose makes CT problematic for longitudinal imaging and
limits
available resolution. Along with serious dose concerns, digital x-ray
resolution is limited
because the 2-dimensional image obtained is a composite of the absorption
through the
entire thickness of tissue presented to the beam. Current clinical diagnostics
for the diseases
that are the target of the embodiments disclosed herein are fraught with
difficulties in
obtaining sufficient in vivo resolution, or accuracy. In some cases, no
definitive diagnostic
exists currently. In other pathologies, particularly in breast and liver,
diagnosis is dependent
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on biopsy, with its non-negligible risk of morbidity and even mortality, and
which is prone
to high read and sampling errors. (WELLER, C; "Cancer detection with MRI as
effective as
PET-CT scan but with zero radiation risks"; Medical Daily, Feb. 18, 2014.)
[PARA 71 Bone health is compromised by ageing, by bone cancer, as a side
effect of
cancer treatments, diabetes, rheumatoid arthritis, and as a result of
inadequate nutrition,
among other causes. Bone disease affects over ten million people annually in
the US alone,
adversely affecting their quality of life and reducing life expectancy. For
assessment of
bone health, the current diagnostic standard is Bone Mineral Density (BMD), as
measured
by the Dual Energy X-ray Absorptiometry (DEXA) projection technique. This
modality
yields an areal bone density integrating the attenuation from both cortical
and trabecular
bone, similar to the imaging mechanism of standard x-ray, but provides only
limited
information on trabecular architecture within the bone, which is the marker
linked most
closely to bone strength. (KANIS, J. AND GLUER, C.; "An update on the
diagnosis and
assessment of osteoporosis with densitometry"; Osteoporosis International,
Vol. 11, issue 3,
2000. LEGRAND, E. et al.; "Trabecular bone microarchitecture, bone mineral
density, and
vertebral fractures in male osteoporosis"; JBMR, Vol. 15, issue 1, 2000.) BMD
correlates
only loosely with fracture risk. A post-processing technique, TBS (Trabecular
Bone Score)
attempts to correlate the pixel gray-level variations in the DEXA image, to
yield
information on bone microarchitecture. A comparison study determined that BMD
at hip
remains a better predictor of fracture. But, though TBS does not yield a
detailed assessment
of trabecular architecture. (BOUSSON, V., et al.; "Trabecular Bone Score
(TBS): available
knowledge, clinical relevance, and future prospects"; Osteoporosis
International, Vol. 23,
2012. DEL RIO, et al.; "Is bone microarchitecture status of the spine assessed
by TBS
related to femoral neck fracture? A Spanish case-control study": Osteoporosis
International,
Vol. 24, 2013.) TBS is a relatively new technique and is still being
evaluated.
[PARA 81 Measurement of bone microarchitecture, specifically trabecular
spacing and
trabecular element thickness, requires resolution on the order of tenths of a
millimeter. MRI,
ultrasound imaging, CT, and microCT have all been applied to this problem. In
MRI,
though high contrast between bone and marrow is readily obtained, resolution
is limited by
patient motion over the long time needed to acquire an image with sufficient
resolution to
characterize the trabecular network. The finer the texture size of this
network, the greater
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the blurring from motion. An attempt to mitigate the effects of patient motion
by looking
only at the skeletal extremities, removed from the source of cardiac and
respiratory motion
sources, has been tried using both MRI and microCT. However, the correlation
between
bone microarchitecture in the extremities and that in central sites in not
known. Further, a
large data matrix, hence long acquisition time, is still required to obtain
sufficient image
information to determine trabecular spacing and element thickness. This long
acquisition
time results in varying levels of motion-induced blurring, depending on
patient
compliance¨twitching is still a serious problem even when measuring
extremities. A
proposed MR-based technique, fineSA (JAMES, T., CHASE, D.; "Magnetic field
gradient
structure characteristic assessment using one dimensional (1D) spatial-
frequency
distribution analysis" ; US patent No. 7,932,720 7; April 26, 2011.), attempts
to circumvent
the problem of patient motion by acquiring a much smaller data matrix of
successive,
finely-sampled, one-dimensional, frequency-encoded acquisitions which are
subsequently
combined to reduce noise. Imaging in this case is reduced to one dimension,
reducing the
size of the data matrix acquired and, hence, the acquisition time. However, as
the gradient
encoded echoes, are very low Signal to Noise (SNR), noise averaging is
required. Though
some resolution advantage is gained by this method relative to 2 and 3-d
imaging, the need
to acquire many repeat spatially-encoded echoes over several response times
(TRs) for
signal averaging results in an acquisition time on the order of minutes¨too
long to provide
motion immunity. Thus, resolution improvement obtainable by the technique is
limited.
[PARA 91 What is needed is an accurate, robust, non-invasive, in vivo
measure of
trabecular spacing and trabecular element thickness capable of assessing bones
in the
central skeleton, as these are the key markers for assessing bone health and
predicting
fracture risk. Until now, no clinical technique has been able to provide this
capability.
[PARA 101 Fibrotic diseases occur in response to a wide range of biological
insults and
injury in internal organs, the development of collagen fibers being the body's
healing
response. The more advanced a fibrotic disease, the higher the density of
fibers in the
diseased organ. Fibrotic pathology occurs in a large number of diseases, from
lung and
liver fibrosis, to cardiac and cystic fibrosis, pancreatic fibrosis, muscular
dystrophy, bladder
and heart diseases, and myelofibrosis, in which fibrotic structures replace
bone marrow.
Fibrotic development is attendant in several cancers, such as breast cancer. A
different
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pathology development is seen in prostate cancer, where the disease destroys
healthy
organized fibrous tissue. In all cases, textural spacings highlighted in the
tissue change in
response to disease progression, as collagen fibers form along underlying
tissue structures.
In liver disease, the textural wavelength changes as the healthy tissue
texture in the liver is
replaced by a longer wavelength texture originating from the collagen
"decoration" of the
lobular structure in the organ. In other organs/diseases, textural change
reflects the upset in
healthy tissue with development of texture indicative of fibrotic
intervention.
[PARA 111 To span the range of disease progression in most fibrotic
pathologies,
evaluation of textural changes from fibrotic development requires resolution
on the scale of
tenths of a mm. One of the most prevalent of such pathologies, liver disease,
is
representative of the difficulty of assessing fibrotic structure. Currently,
the gold standard
for pathology assessment is tissue biopsy¨a highly invasive and often painful
procedure
with a non-negligible morbidity¨and mortality¨risk (patients need to stay at
the hospital
for post-biopsy observation for hours to overnight), and one that is prone to
sampling errors
and large reading variation. (REGEV, A.; "Sampling error and intraobserver
variation in
liver biopsy in patients with chronic HCV infection" ; American Journal of
Gastroenterology; 97, 2002. BEDOSSA, P. et al.; "Sampling variability of liver
fibrosis in
chronic hepatitis C"; Hepatology, Vol. 38, issue 6, 2004. VAN THIEL, D. et
al.; "Liver
biopsy: Its safety and complications as seen at a liver transplant center";
Transplantation,
May 1993.) Ultrasound, another modality often used to assess tissue damage in
liver
disease, is only able to provide adequate assessment in the later stages of
the disease¨it is
used to diagnose cirrhosis. Magnetic Resonance-based Elastography (MRE), which
has
been under development for some time for use in assessment of liver disease,
is not capable
of early-stage assessment¨the read errors are too large prior to significant
fibrotic invasion
(advanced disease). Further, this technique requires expensive additional
hardware, the
presence of a skilled technician, and takes as much as 20 minutes total set up
and scanning
time, making it a very costly procedure. The ability to image fibrotic texture
directly by
MR imaging is compromised both by patient motion over the time necessary to
acquire data
and by lack of contrast between the fibers and the surrounding tissue. Even
acquisition
during a single breath hold is severely compromised by cardiac pulsatile
motion and
noncompliance to breath hold, which results in significant motion at many
organs, such as
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liver and lungs. And SNR is low enough that motion correction by combining
reregistered
MR-intensity profiles obtained from successive echoes is extremely
problematic. Similarly,
assessment of the amount of cardiac fibrosis in early stage disease using MRI
is seriously
hampered by cardiac pulsation over the time of the measurement. As motion is,
unlike
Gaussian noise, a non-linear effect, it can't be averaged out¨there must be
sufficient signal
level to allow reregistration before averaging for electronic noise-reduction.
A more
sensitive (higher SNR), non-invasive technique, capable of assessing textural
changes
throughout the range of fibrotic development, from onset to advanced
pathology, is needed
to enable diagnosis and monitoring of therapy response.
[PARA 121 Onset and progression of a large number of neurologic diseases are
associated with changes in repetitive fine neuronal and vascular
structures/textures.
However, ability to assess such changes in the brain is only available post
mortem.
Currently, definitive diagnosis of Alzheimer's Disease (AD) is by post mortem
histology of
brain tissue. AD and other forms of dementia such as Dementia with Lewy
Bodies, motor
diseases such as Amyotrophic Lateral Sclerosis (ALS), Parkinson's disease,
conditions
precipitated by Traumatic Brain Injury (TBI) such as Chronic Traumatic
Encephalopathy
(CTE), as well as those caused by other pathologies or trauma, or conditions
that involve
damage to brain structures such as Multiple Sclerosis (MS), Cerebrovascular
Disease
(CVD), and other neurologic diseases, are often only diagnosable in advanced
stages by
behavioral and memory changes, precluding the ability for early stage
intervention. Further,
conditions such as epilepsy and autism have been associated with abnormal
variations in
fine neuronal structures, which, if clinically diagnosable, would allow
targeted selection for
testing therapy response.
[PARA 131 Various in vivo diagnostic techniques are available for AD and other
dementias, but none of them are definitive. These techniques range from
written diagnostic
tests, which are prone to large assessment errors, to PET imaging to assess
amyloid plaque
density or glucose metabolism (FDG PET). As discussed previously, PET imaging
is
extremely expensive, cannot provide high resolution, and relies on use of
radioisotopes and
positioning x-ray beams, complicating approval for longitudinal use due to
dose concerns.
Further, neither amyloid imaging nor FDG PET has been shown to provide a
definitive
indication of AD. (MOGHBEL, M. et al. "Amyloid Beta imaging with PET in
Alzheimer's
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disease: is it feasible with current radiotracers and technologies?"; Eur. J.
Nucl. Med. Mol.
Imaging.)
[PARA 141 Use of CSF biomarkers for dementia diagnosis is painful and highly
invasive
and cannot differentiate signal levels by anatomic position in the brain, as
is possible with
imaging biomarkers. As various forms of dementia are found to have different
spatial/temporal progression through the brain, this is a serious drawback to
use of liquid
biopsy. Another disease associated with various forms of dementia is CVD
(Cerebrovascular Disease), which induces cognitive impairment as a result of
reduced blood
flow through blocked vessels leading to brain tissue. Something capable of
high-resolution
assessment of pathology-induced changes in micro-vessels is needed here.
[PARA 151 Tissue shrinkage due to atrophy in many forms of dementia including
AD is
measurable with careful registration of longitudinally-acquired data in MRI,
but the disease
is advanced by the time this shrinkage is measureable. Early stages of disease
are indicated
in post mortem histology by degradation in the columnar ordering of cortical
neurons, the
normal spacing for these columns being on the order of 100 microns in most
cortical
regions. (CHANCE, S. et al.; "Microanatomical correlates of cognitive ability
and decline:
normal ageing, MCI, and Alzheimer's disease"; Cerebral Cortex, August 2011. E.
DI ROSA
et al.; "Axon bundle spacing in the anterior cingulate cortex of the human
brain"; Journal of
Clinical Neuroscience, 15, 2008.) This textural size, and the fact that the
cortex is extremely
thin, makes speed of acquisition paramount, as even tiny patient motion will
make data
collection impossible. Assessment of textural changes on the order of tens of
microns is
extremely problematic in vivo, but would, if possible, enable targeting a
range of fine
textural changes in neuronal disease diagnosis and monitoring, and would play
an important
role in therapy development.
[PARA 161 Another possible neurologic application for the claimed method is
to, in
vivo, determine the boundaries of the various control regions of the cerebral
cortex or the
different Brodmann's areas of which these are comprised. Such ability would
greatly aid
data interpretation in brain function studies, such as those performed using,
for example,
FMRI (Functional Magnetic Resonance Imaging).
[PARA 171 The
three classes of diseases listed above, bone disease, fibrotic diseases, and
neurologic diseases are not an all-inclusive list. Other disease states in
which pathology-
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induced changes of fine structures occur, for instance angiogenic growth of
vasculature
surrounding a tumor, or fibrotic development and changes in vasculature and
mammary
gland ducting in response to breast tumor development, also are pathologies
wherein the
ability to resolve fine tissue textures would enable early detection of
disease, and
monitoring of response to therapy.
[PARA 181 The ability to measure changes in fine textures would be of great
value for
disease diagnosis. Non-invasive techniques that do not rely on use of ionizing
radiation or
radioactive tracers allow the most leeway for early diagnosis and repeat
measurement to
monitor disease progression and response to therapy. Magnetic Resonance
Imaging (MRI),
which provides tunable tissue contrast, is just such a non-invasive technique,
with no
radiation dose concerns. However, in order to circumvent the problem of signal
degradation
due to patient motion, data must be taken on a time scale not previously
possible.
SUMMARY OF THE INVENTION
[PARA 191 The embodiments disclosed herein provide a method for pathology
assessment employing tissue texture using magnetic resonance (MR) which may be
used
integrally with an MR imaging technique. A contrast mechanism is selected for
enhancing
the contrast between component tissue types in a multiphase biologic sample
for
measurement with a MR imaging process. The selected contrast mechanism is then
applied
and a volume of interest (VOI) is selectively excited employing a plurality of
time varying
radio frequency signals and applied gradients. An encoding gradient pulse is
applied to
induce phase wrap to create a spatial encode for a specific k-value and
orientation, the
specific k-value determined based on texture within the VOI. A series of
gradients is
initiated to produce k-value encodes, a resulting k-value set being a subset
of that required
to produce an image of the VOI. Multiple sequential samples of the NMR RF
signal
encoded with the k-value set are recorded. Post processing the recorded NMR
signal
samples is accomplished to produce a data set of signal vs k-values for k-
values in the k-
value set, to characterize textural features of tissue in the VOI. The MR
imaging process is
then performed as an integral or hybrid pulse sequence with the texture
measurement.
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BRIEF DESCRIPTION OF THE DRAWINGS
[PARA 201 The features and advantages of embodiments disclosed herein will be
better
understood by reference to the following detailed description when considered
in
connection with the accompanying drawings wherein:
[PARA 211 FIG. 1 is a simulation showing the number of data samples required
for
averaging to achieve an output SNR > 20dB as a function of input SNR;
[PARA 221 FIG. 2 is a simulation showing the number of data samples needed for
averaging to achieve a SNR > 20db as a function of location in k-space;
[PARA 231 FIG. 3 is an example timing diagram of a pulse sequence for the
claimed
method showing the timing of a single TR;
[PARA 241 FIG. 4 is a close-up of the example timing diagram of FIG. 3;
[PARA 251 FIG. 5 is an example of a timing diagram for the claimed method,
designed
to acquire multiple measures of a select set of k-values, with a different
number of samples
acquired at each k-value to counteract the decrease in energy density at
increasing k-value;
[PARA 261 FIG. 6 is a simulation showing that the ability provided by the
claimed
method to acquire many repeats of signal at each targeted k-value within a
single TR
enables robust signal averaging to boost SNR;
[PARA 271 FIG. 7 is a simulation showing the results of attempting to acquire
90
samples for averaging using the conventional frequency-encoded echo approach,
wherein
acquisition of signal at only a small number of repeats of a particular k-
value are possible in
each TR due to the long record time for each echo;
[PARA 281 FIG. 8 is an example timing diagram for the claimed method designed
to
provide data acquisition over multiple refocused echoes within a single TR;
and,
[PARA 291 FIGs. 9 and 10 are a depiction of two possible shapes for the
acquisition
volume of interest (VOI);
[PARA 301 FIG. 11 is an example timing diagram of a pulse sequence for the
claimed
hybrid method showing the timing of a single TR;
[PARA 311 FIG. 12 is a detailed view of the hybrid elements of the method at
an
expanded scale;
[PARA 321 FIG. 13 is a further detailed view of the very-low SNR acquisition
mode
portion of FIG. 12;
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[PARA 331 FIG. 14 is a further detailed view of the low SNR acquisition
portion of
FIG. 12;
[PARA 341 FIG. 15 is a further detailed view of the high SNR acquisition
portion of
FIG. 12;
[PARA 351 FIG. 16 is an example timing diagram of a pulse sequence for the
claimed
hybrid method showing data acquisition in a single echo;
[PARA 361 FIG. 17 is a further detailed view of the very-low SNR, low SNR and
high
SNR acquisition portions of FIG. 16;
[PARA 371 FIG. 18 is an example timing diagram of a pulse sequence for a low
SNR
acquisition;
[PARA 381 FIG. 19 is a further detailed view of the low SNR acquisition mode
of FIG.
18;
[PARA 391 FIG. 20 is an example timing diagram of a pulse sequence for high
SNR
acquisition;
[PARA 401 FIG. 21 is a further detailed view of the high SNR acquisition mode
of FIG.
20;
[PARA 411 FIGs. 22A and 22B are pictorial representations of healthy and
osteoporotic
bone structure;
[PARA 421 FIG. 23 is a pictorial representation of fibrotic tissue in a
liver;
[PARA 431 FIG. 24 is an example timing diagram of a pulse sequence
implementing a
first diffusion contrast;
[PARA 441 FIG. 25 timing diagram of a pulse sequence implementing a second
diffusion
contrast;
[PARA 451 FIG. 26 is a pictorial representation of VOIs dispersed in
fibrotic tissue;
[PARA 461 FIG. 27 is a pictorial representation of cortical minicolumns in
the brain;
[PARA 471 FIG. 28 is a pictorial representation of VOI placement in the
brain;
[PARA 481 FIGs. 29A -29C are representations of three histology images showing
progressive pathology with AD advancement;
[PARA 491 FIG. 30 is exemplary representation of placement of a VOI in the
brain
cortex and application of gradients for k-value;
[PARA 501 FIG. 31 is a flow chart demonstrating
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[PARA 511 FIG. 32 is a flow chart demonstrating procedural flow using scout
acquisitions; and,
[PARA 521 FIGs. 33A and 33B are a flow chart demonstrating work flow for
calibration
of texture characterization for application in diagnosis of targeted diseases.
DETAILED DESCRIPTION OF THE INVENTION
[PARA 531 The following definition of terms as used herein is provided:
1800 inversion pulse RF pulse that inverts the spins in a tissue region to
allow refocusing
of the MR signal.
180 pulse An RF pulse that tips the net magnetic field vector antiparallel
to Bo
90 pulse An RF pulse that tips the net magnetic field vector into the
transverse plane
relative to Bo
3T 3 Tesla
A/D Analog to digital converter
AD Alzheimer's Disease
ADC Average diffusion coefficient measured in Diffusion Weighted Imaging
Adiabatic pulse excitation Adiabatic pulses are a class of amplitude- and
frequency-
modulated RF-pulses that are relatively insensitive to 6 inhomogeneity and
frequency offset
effects.
ASL Arterial Spin Labelling
AWGN Additive White Gaussian Noise Additive white Gaussian noise (AWGN) is a
basic
noise model used in Information theory to mimic the effect of many random
processes that
occur in nature.
BPH Benign Prostatic Hyperplasia
Biopsy A biopsy is a sample of tissue extracted from the body in order to
examine it more
closely.
BOLD Blood Oxygenation Level Dependent
C/N Contrast to Noise, a measure of image quality based on signal
differences between
structural elements rather than on overall signal level
CAWGN Complex-valued, additive white Gaussian noise
CBF Cerebral Blood Flow
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Chemical shift Small variations in MR resonant frequency due to the
different
molecular environments of the nuclei contributing to an MR signal.
CJD Creutzfeld-Jakob Disease
Crusher gradients Gradients applied on either side of a 1800 RF refocussing
slice
selection pulse to reduce spurious signals generated by imperfections in the
pulse.
CSF Cerebrospinal fluid
CVD Cerebrovascular disease
DCE Dynamic Contrast Enhanced
DEXA Dual Energy X-ray Absorptiometry is a means of measuring bone mineral
density
using two different energy x-ray beams.
DSC Dynamic susceptibility contrast
DTI Diffusion Tensor Imaging
DWI Diffusion Weighted Imaging
Echo The RF pulse sequence where a 90 excitation pulse is followed by a 180
refocusing pulse to eliminate field inhomogeneity and chemical shift effects
at the echo.
Frequency encodes Frequency-encoding of spatial position in MRI is
accomplished
through the use of supplemental magnetic fields induced by the machine
gradient coils
Gaussian noise Gaussian noise is statistical noise having a probability
density
function (PDF) equal to that of the normal distribution, which is also known
as the Gaussian
distribution.
Gradient pulse a pulsing of the machine magnetic field gradients to alter
the k-value
encode
Gradient set the set of coils around the bore of an MR scanner used primarily
to spatially
encode signal or to set a particular phase wrap in a selected direction
GRE Gradient Recalled Echo
Interleaved acquisition Signal acquisition from a multiplicity of VOIs,
successively
excited within a single TR
Isochromat A microscopic group of spins that resonate at the same frequency.
k-space The 2D or 3D Fourier transform of the MR image.
k-value coefficient The coefficient in a Fourier series or transform
reflecting the relative
weight of each specific k-value in the series.
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k-space The 2D or 3D Fourier transform of the MR image.
k-value One of the points in k-space reflecting the spacing of structural
elements in a
texture field.
k-value selection pulse The gradient pulse used to select a specific k-
value encode
along the sampled direction
Library of k-space values the net collection of k-space coefficients acquired
in a
particular region of tissue for tissue characterization
Machine gradients the magnetic field gradients imposable through use of the
set of
gradient coils in an MR scanner
MRE Magnetic Resonance Elastography¨an imaging technique that measures the
stiffness of soft tissues using acoustic shear waves and imaging their
propagation using
MRI.
MRI Magnetic Resonance Imaging
MRS Magnetic Resonance Spectroscopy
MS Multiple Sclerosis
MTI Magnetization Transfer Imaging
Noise floor In signal theory, the noise floor is the measure of the signal
created from the
sum of all the noise sources and unwanted signals within a measurement system
NMR Nuclear Magnetic Resonance
PET Positron Emission Tomography is a functional imaging technique that
produces a
three-dimensional image of functional processes in the body using a positron-
emitting
radiotracer.
Phase coherence (spatial) When referring to multiple measurements within a
common
VOI of a or multiple k-values indicates that the sample has the same position
relative to the
measurement frame of reference
Phase encode A phase encode is used to impart a specific phase angle to a
transverse
magnetization vector. The specific phase angle depends on the location of the
transverse
magnetization vector within the phase encoding gradient, the magnitude of the
gradient, and
the duration of the gradient application.
Phase wrap The helical precession of the phase of the transverse magnetization
along a
phase encoded sample
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Pitch with reference to the pitch of a screw, the tightness of the phase wrap
along the
direction of k-value encode
Profile A one dimensional plot of signal intensity
RF Radio Frequency electromagnetic signal
Semi-crystalline texture a texture exhibiting regular spacing along one or
more
directions
slice (slab) Used interchangeably to indicate a non-zero thickness planar
section of the
Slice-selective refocusing Refocussing of spins through combination of a slice
selective
gradient and an RF pulse such that the bandwidth of the RF pulse selects a
thickness along
the direction of the gradient, and the RF pulse tips the net magnetization
vector away from
its equilibrium position Only those spins processing at the same frequency as
the RF pulse
will be affected.
SE Spin Echo
SNR Signal to Noise Ratio
Spoiler gradients see crusher gradients
T2 Defined as a time constant for the decay of transverse magnetization
arising from
natural interactions at the atomic or molecular levels.
T2* In any real NMR experiment, the transverse magnetization decays much
faster than
would be predicted by natural atomic and molecular mechanisms; this rate is
denoted T2*
("T2-star"). T2* can be considered an "observed" or "effective" T2, whereas
the first T2
can be considered the "natural" or "true" T2 of the tissue being imaged. T2*
is always less
than or equal to T2.
TBS Trabecular Bone Score is a technique that looks for texture patterns in
the DEXA
signal for correlation with bone microarchitecture for assessing bone health
TbTh trabecular thickness for bone measurement.
TbSp trabecular spacing for bone measurement.
TbN trabecular number for bone measurement.
TE Spin Echo sequences have two parameters: Echo Time (TE) is the time
between the
90 RF pulse and MR signal sampling, corresponding to maximum of echo. The 180
RF
pulse is applied at time TE/2. Repetition Time is the time between 2
excitations pulses (time
between two 90 RF pulses).
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Textural frequency the number of texture wavelength repeats per unit length in
a texture
Texture wavelength the characteristic spacing between structural elements in a
texture
TR Spin Echo
sequences have two parameters: Echo Time (TE) is the time
between the 90 RF pulse and MR signal sampling, corresponding to maximum of
echo.
The 180 RF pulse is applied at time TE/2. Repetition Time is the time between
2
excitations pulses (time between two 90 RF pulses).
Vector combination gradient A magnetic
gradient resulting from any vector
combination of the gradient coil set
VOI Volume of Interest
Windowing function In signal processing, a window function (also known as an
apodization function or tapering function) is a mathematical function that is
zero-valued
outside of some chosen interval
x-ray diffraction X-ray
diffraction is a tool used for identifying the atomic and
molecular structure of a crystal
[PARA 541 The embodiments disclosed herein provide an MR-based technique that
enables in vivo, non-invasive, high-resolution measurement and assessment of
fine biologic
textures, enabling monitoring of texture formation and/or change in response
to disease
onset and progression in a range of pathologies. This same method can be
applied to fine-
texture characterization in other biologic and physical systems. It enables MR-
based
resolution of fine textures to a size scale previously unattainable in in vivo
imaging.
[PARA 551 In standard MR "imaging" the morphology of a large region of anatomy
or
an organ is imaged by using a pulse sequence that induces contrast from one
tissue
type/organ to the next. To obtain an image, signal must be averaged over
individual voxels,
the size of the voxels then setting the image resolution.
[PARA 561 This imaging will be sensitive to tissue contrast near lesions,
such as tumors,
etc. that appear then at localized points of an organ in the image. Or, the
image might show
that an organ has changed, perhaps become enlarged, relative to a healthy
organ. As such,
the basic image is the anatomy and any localized pathology shows up on this
anatomical
image.
[PARA 571 To acquire a 3D image, data acquisition can be by acquisition of
multiple,
spatially adjoining, slices, or as a 3D dataset directly. In effect, a 2D
slice is a "map" of
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signal level, the individual pixels of the map being the individual voxels.
The signal level of
each voxel depends on the contrast mechanism applied. The relative signal
levels of each
voxel then form the image. Though, in effect, this is a map of voxel
brightness, when the
intent is to depict morphologic variation directly across a region of the
anatomy, it is called
an image.
[PARA 581
Alternatively, mapping is the creation of a display of an indirect quantity
that
affects the brightness of an MR signal. It is not a map of morphology, but of
an inferred
tissue characteristic, such as Apparent Diffusion Coefficient, or Fractional
Anisotropy, or
organ stiffness. As such, the brightness in the voxels that make up the mapped
quantity also
form an "image" but in common usage this would be called a map. Often such a
map is
compared to an image from the same anatomical region. This would allow, for
instance, a
tumor region that shows up bright on, say, the brain in an image, to be
compared to the
diffusion coefficient in that region.
[PARA 591 The terms imaging and mapping may be used interchangeably in the
description herein and refer to a resulting dataset for display or
manipulation not necessarily
an attempted reproduction of a "picture" image of the morphology.
[PARA 601 The method, while described herein with respect to biological
systems for
examination of tissue, is equally applicable for assessment of fine structures
in a range of
industrial purposes such as measurement of material properties in
manufacturing or in
geology to characterize various types of rock, as well as other uses for which
measurement
of fine structures/textures is needed.
[PARA 611 The embodiments disclosed herein achieve this significant
improvement in in
vivo resolution of fine texture by acquiring the requisite data fast enough
that the effect of
subject motion, the factor that limits MRI resolution, becomes negligible.
This fast
acquisition is achieved by acquiring data incrementally--at a single location,
orientation and
at one, or a select set or range, of k-values at a time¨within one TR, if
multiple pulses are
used, or within one excitation pulse. After applying an encoding gradient to
select the k-
value of interest, data is acquired with the gradient switched off, allowing
multiple
acquisition repeats of the signal at the encoded k-value for subsequent
averaging to reduce
electronic noise, thus enabling robust measure at individual k-values before
motion blurring
can occur. To build up measurements on a larger set of selected k-values
present within the
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tissue, or towards development of a continuous spectrum of textural spacings
within the
tissue, the acquisition TR can be repeated as many times as necessary,
changing the encode
as needed to span the desired extent of real and of k-space required. The set
of signals
measured at one or more k-values output from each TR are now high SNR due to
the ability
to average repeats without motion effects, and since the measure of interest
is textural
spacing, and not development of an image, the lack of phase coherence between
TRs is of
no concern.
[PARA 621 In its simplest form the embodiments disclosed herein consist of
acquiring
MR signal from within an inner volume to encompass a specific tissue region of
interest,
such as a lesion, an organ, a location in an organ, a specific region of bone,
or a number of
regions in a diseased organ for sampling. This inner volume may be excited by
one of a
number of methods, including but not limited to: intersecting slice-selective
refocusing,
selective excitation using phased-array transmit in combination with
appropriate gradients,
adiabatic pulse excitation to scramble signal from the tissue outside the
region of interest,
outer volume suppression sequences, and other methods of selectively exciting
spins in an
internal volume including physically isolating the tissue of interest, to name
a few,
[PARA 631 After definition of a volume of interest (VOI), in certain
embodiments, the
gradient is turned off, and multiple samples of signal centered at a specific
k-value, the
spread of which is defined by receiver BW and sampling length, are acquired.
This
measurement is repeated only at a set of k-values and in specified directions
within the VOI,
rather than trying to map all of k-space as is required to generate an image.
One or more
samples of signal at a particular k-value are acquired within an acquisition
block during a
single TR, or excitation pulse, and the k-value subsequently incremented or
decremented,
allowing further samples at other k-values as desired during the same TR, or
excitation
pulse. This method allows multiple sampling at each k-value of interest over a
time period
of milliseconds, providing immunity to subject motion. The process can then be
repeated in
further TRs, the requirement on motion between signal acquisitions at specific
k-values
being only that the VOI remain within the tissue region of interest. Buildup
of a magnitude
spectrum of textural frequencies may be accomplished without the need to
acquire it in a
spatially coherent manner. Because the quantities of interest are the relative
intensities of
the various k-values (textural spacings) present in the sample volume, as long
as the
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acquisition volume remains within a representative sample of tissue, any
motion between
the blocks does not compromise the measurement. In the case of motion of
sufficiently large
magnitude that an internally excited volume might be formed in other tissue
volumes over
the course of building up a spectrum of k-values contained in the tissue, use
of fairly robust,
real-time piloting and acquisition algorithms can be used for gross
repositioning of the
internal selectively excited volume and for rejecting data sets that have
failed to stay in the
proper tissue.
[PARA 641 Repositioning the VOI to allow sampling of texture at multiple
positions
within or across an organ or anatomy allows determination of the variation in
pathology
through the organ. The data acquired can, with reference to positioning
images, be mapped
spatially. Either the VOI can be moved in successive TRs or interleaved
acquisition done
within a single TR by exciting additional volumes during the time that the
signal is
recovering in advance of the next TR. The requirement is that successive VOIs
be excited in
new tissue, that does not overlap the previous slice selects. Spatial
variation of pathology
can be determined by this method. This can also be used to monitor temporal
progression of
a pathology through an organ if the measure is repeated longitudinally.
[PARA 651 Tailoring the pulse sequence to pre-wind phase in the sample volume
can
position the measure of signal at the highest k-values of interest at the echo
peak where the
signal is strongest, providing best SNR measurement.
[PARA 661 Sampling of signal at k-values, with the acquisition axis
oriented along
varying directions, aligned at varying angles and along varying paths, either
rectilinear or
curved, within the volume(s) under study can yield important information on
texture,
especially textures with semi-ordered structure in specific directions, such
as neuronal
minicolumns. Measurement of signal vs. k-values associated with columnar
spacing is
extremely sensitive to alignment of the sampling path, as slight variations in
sampling
direction on either side of perpendicular show a rapid drop off in signal for
that k-value.
Rocking the acquisition path on either side of the signal maximum can yield a
measure of
pathology-induced randomness which is indicated by the width of the signal vs.
k-value
peak.
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[PARA 671 With the gradient switched off for data acquisition, tuning the
bandwidth to
particular chemical species can enhance structural information when the
chemical
composition of the structure under study is known.
[PARA 681 The embodiments disclosed herein can be used in conjunction with
time-
dependent contrast schemes that target blood flow. Some of these contrast
techniques are
Blood Oxygenation Level Dependent (BOLD) imaging, Arterial Spin Labeling (ASL)
imaging, and Dynamic Susceptibility Contrast (DSC) imaging. As these methods
use
various techniques to highlight vasculature, changes in the texture of the
vasculature
associated with many pathologies, including CVD (cerebrovascular disease) and
tumor
growth can be measured.
[PARA 691 The embodiments disclosed herein can also be used in conjunction
with
various novel MR-contrast mechanisms, including DWI, DTI and MTI, to provide
front end
information toward parameter selection for the diffusion techniques as well as
correlation
with their measurements of tissue health.
[PARA 701 The rapid repeat measurement of signal at a single k-value, with the
total
time to acquire a block being on the order of a msec, reduces patient and
machine motion-
induced blurring to a negligible level, enabling robust assessment of fine
textures previously
not accessible in vivo. (For comparison, standard MR image acquisition times
are much
longer in duration over which patients are asked to remain completely
stationary.) Since the
excited tissue defining the VOI moves with any tissue motion, acquisition
within one TR, or
excitation pulse, is largely immune to subject motion. The SNR of signal
measured at each
k-value selected is significantly improved through combination of the
individual samples at
each k-value within a block; this averaging can be done without concern for
subject motion,
which is eliminated due to the rapid sequential acquisition of the individual
samples in the
block.
[PARA 711 This significant improvement in SNR is made possible because the
embodiments disclosed herein focus on acquiring signal at only the k-values of
interest for
determination of fine texture pathology signatures, rather than on acquisition
of the large
number of spatially-encoded echoes required for image formation. The
significantly reduced
data matrix enables the increased number of coherent repeats at the targeted k-
values, and
hence significant improvement in SNR.
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[PARA 721 Energy density within a range of textural spacings is proportional
to textural
wavelength, or inversely to k-value¨i.e. the higher the k-value, the lower the
associated
signal intensity. The fast acquisition enabled through use of the embodiments
disclosed
herein, enables tailoring the number of acquisition repetitions at a
particular k-value to
acquire k-values for which there is low signal first, before T2 and T2*
effects have degraded
signal amplitude. In this way, the SNR of each repeat to be averaged for noise
cancellation
(or spatial-phase-corrected before combining it with the measurements of k-
value from
subsequent TRs) will be above this threshold. It does not matter that there is
motion
between acquisition cycles at different k-values as long as each acquisition
lies within the
tissue volume of interest (VOI). As the claimed method targets only assessment
of
pathology-induced changes in tissue texture, there is no requirement for phase
coherence
over an entire cycle of data acquisition, as is required in imaging.
[PARA 731 Several benefits result from acquiring data after the gradient is
switched off
for single-k-value sampling in a reduced volume (the VOI). By proper pulse
sequencing, the
echo record window can be designed such that recording begins with the highest
k-values of
interest, as signal level is highest at the echo peak. This enables recording
of fine structures
currently unachievable with in vivo MR imaging.
[PARA 741 Additionally, T2* is longer with the gradient off, so SNR is
improved by the
longer acquisition times possible. This allows acquisition of an increased
number of
samples, N.
[PARA 751 Coil combination is also simplified by having higher SNR for each k-
value,
hence providing a significant improvement in overall SNR. This is especially
beneficial as
the trend in MRI is towards coil arrays composed of many small element coils.
As the
acquisition volumes targeted in the embodiments disclosed herein are small,
correction for
phase across the sample volume is not needed. Only one phase and gain value
for each coil
is needed for combining the multiple element channels. These can be combined
using the
Maximal Ratio Combining (MRC) method, which weights the coil with the highest
SNR
most heavily, or other multi-signal combination methods. (Phase and gain for
the elements
of a given coil array can be determined once from a phantom and applied to
patient data.)
[PARA 761 Signal acquisition and data sampling in a standard MRI scan is done
by
acquiring complex-valued samples of multiple echoes, while applying a gradient
sequence
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concurrently, as well as in sequence with the echoes. Imaging relies on
frequency encode
for one of the dimensions because this allows a line in k-space to be acquired
with each
phase encode rather than a single point. For 3-dimensional imaging, two
dimensions in k-
space normally rely on phase encode to generate the targeted filling of k-
space, with the
third dimension frequency-encoded. Phase encode acquisition in imaging usually
entails
acquisition of on the order of 256 k-values in each of the phase-encode
directions, hence is a
relatively slow process. Clinical MRI scans take on the order of 10-15 minutes
to generate
an image. The aim in image construction is to acquire sufficient k-space
coverage to fill out
all the coefficients in the 2 or 3-dimensional Fourier series, which is why in
standard MR
resolution is limited by subject motion.
[PARA 771 The embodiments disclosed herein is in direct contrast to standard
MR data
acquisition, with its focus on image generation. Image formation is plagued by
blurring
resulting from subject motion over the long time necessary to acquire the
large data matrix
required. Since the target of the embodiments disclosed herein is texture
rather than image,
the only requirement on subject motion is that the sampled volume remain
within a region
of similar tissue properties over the course of acquiring data. This is a much
less stringent
and easy to achieve target than the requirement of structural phase coherence,
as the scale of
the allowable motion is then large enough, and of a temporal order, to be
easily correctable
by real-time motion assessment and correction techniques. The speed of
acquisition for the
embodiments disclosed herein is such that, in most cases, real-time motion
correction may
not be necessary at all. While other methods have focused on post-processing
of images to
try to extract textural measures, the embodiments disclosed herein eliminates
the need for
image generation, focusing instead on directly measuring texture, hence
enabling a more
sensitive and robust measure.
[PARA 781 Frequently, k-space sampling is considered synonymous with sampling
of an
echo in the presence of a gradient set. In certain embodiments disclosed
herein, the
approach to k-space filling is to acquire only the set of k-values needed for
texture
evaluation in the targeted pathology, with data acquired after the gradient is
switched off.
This method enables such rapid acquisition of single-k-value repeats for
averaging for noise
reduction that subject motion does not degrade the data.
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[PARA 791 Along with the huge improvement in SNR that arises from sampling k-
values individually, with many repeats of a select set of k-values acquired in
a single TR, or
single excitation pulse, acquisition after the gradient is switched off allows
further
significant improvement in SNR and hence, increase in measurement robustness.
This is
explained in the following discussion.
[PARA 801 These same benefits of improved SNR can be achieved with the
gradient ON
during signal acquisition, allowing acquisition across an evolving range of k-
values, as long
as the gradient is low enough. Signal can be acquired in the presence of a low
gradient used
to provide specified trajectory across a small range in k-space during data
acquisition.
[PARA 811 MR echo sampling provides specific samples vs. time of a time-
dependent
echo. The echo is comprised by the gradients applied concurrently (for the
frequency-
encode axis) and prior to (for a phase encode axis), but also contains the
isochromats
associated with the different chemical species of the sample, as well as the
envelope (T2 &
T2*) associated with spin-spin interactions.
[PARA 821 Conventional frequency-encoded spin acquisitions impose a time-
varying
gradient upon the sample, which effectively travels in k-space along a pre-
defined path. For
rectilinear sampling, the path is along a straight line.
[PARA 831 Frequency encodes generate only one measurement at a given k-
value¨at a
given point in time, the acquired sample of the echo represents the one value
which
corresponds to the Fourier coefficient at a specific k-space location. The
next echo sample
represents the value at a different k-space location, the next k-value
dependent on the slope
of the gradient applied concurrently. As long as there is sufficient signal at
the
corresponding k-value, this approach works well. However, in cases where the
signal of
interest is near or even below the noise floor, usually additional samples and
subsequent
post-processing will be required.
[PARA 841 One way to reduce the noise floor in a frequency-encoded gradient
read-out
is to reduce the gradient strength and lower the receiver bandwidth.
Decreasing the receiver
bandwidth will indeed decrease the noise level, and improve lower signal level
detection
(proportional to the term kB TB, with kB corresponding to Boltzmann's
constant, T
corresponding to Temperature in Kelvin, and B is the receiver bandwidth in
Hz.) However,
this comes at the expense of larger chemical shift artifacts.
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[PARA 851 Chemical shift artifacts arise as a consequence of the different
isochromats
associated with different chemical species within the biological sample. In a
frequency-
encoded k-space read-out, those chemical species which resonate at a slightly
higher
frequency will appear to be displaced from their actual location in image
space towards the
direction of increasing frequency. If the spatial frequency encoding gradient
is shallow, the
apparent displacement can be quite large.
[PARA 861 As such, to minimize chemical shift artifacts, the gradient slope
is typically
made as steep as possible to minimize the apparent shift to within a narrow
range (i.e.
within 1 or two pixels in the image domain). However, this then requires a
larger receiver
bandwidth to accommodate the larger frequency range. This in turn increases
the overall
noise floor at a level proportional to the receive bandwidth.
[PARA 871 The conclusion is that frequency readouts generally force a trade-
off between
gradient strength, noise level, and chemical shift artifacts.
[PARA 881 A common technique for noise reduction in signal acquisition is
through
repeat sampling of a signal and subsequent combination of the multiple
measurements. For
linear noise sources, such as Gaussian noise, this technique improves SNR
through
cancellation of the random noise on the signal, the cancellation effect
increasing with the
number of samples, N.
[PARA 891 Noise reduction by this cancellation technique works for static
subjects.
However, motion-induced blurring is a non-linear effect, so signal combining
for which the
individual measurements have shifted through large spatial phase angles
(relative to the
textural/structural wavelengths under study) does not lead to an improved SNR.
A fairly
standard technique to correct for motion is to look at the MR intensity data
in real space and
reregister successive traces/images to each other to maximize overlap. It is
assumed that, as
with the reduction in white noise, linear combination of these reregistered
signals will result
in reduction of the blurring caused by the motion. However, this only works if
the SNR on
each individual acquisition is high enough. Reregistering low SNR samples
results in a high
variance in the estimated position. Threshold theory defines that combining
reregistered
signals with non-linear blurring, when the original signals are below a
certain noise
threshold, only increases signal error.
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[PARA 901 The nonlinearity introduced by subject motion increases at higher k-
values,
since the motion-induced textural phase shift increases with k¨i.e. as the
size of the
structures of interest decrease, the adverse consequence of motion become more
acute. This
implies that the multiple samples to be combined need to be derived from the
same
acquisition sequence, acquired in a sufficiently short time span, to ensure
there is negligible
motion between samples.
[PARA 911 The Cramer-Rao Lower Bound provides insight into the number of
samples
that are required for a lower bound on the residual variance of an estimate,
i.e. the SNR vs.
number of samples, in Additive White Gaussian Noise (AWGN). For low source
SNRs in
AWGN, one needs a large number of samples to average in order to obtain a
usable SNR.
The primary assumption is that multiple acquisitions can be taken, then
averaged to achieve
the higher SNR. (CRAMER, H.; "Mathematical Methods of Statistics"; Princeton
University Press, 1946. RAO, C.R., "Information and the accuracy attainable in
the
estimation of statistical parameters"; Bulletin of the Calcutta Mathematical
Society 37,
1945.)
[PARA 921 Referring to the drawings, the graph in FIG. 1 comparing output SNR
shown
in trace 102 with number of samples required shown in trace 104 demonstrates
that, for high
input SNRs, a single sample is sufficient to yield a low noise measure. For
lower SNRs,
multiple samples are required to "average out" the noise contribution. The
ability to
combine the samples explicitly assumes that the underlying signal of interest
is relatively
constant during the multiple sample acquisition process (i.e. the only
component which
changes is the noise).
[PARA 931 The graph in FIG. 2 is a simulation with signal model, trace 202,
providing
an input SNR, trace 204, showing number of samples of k-value, trace 206,
needed to yield
a SNR of 20dB as a function of location in k-space, given an input noise level
of 3mV rms.
Since spectral energy density is generally proportional to k-1, to maintain
adequate SNR a
larger number of input samples is required at higher spatial frequencies
(higher k-values).
The noise level for the simulation is adjusted for -10dB SNR at k = 2
cycles/mm
(2\,=500 m).
[PARA 941 As pointed out above, this type of averaging is possible for
purely static
samples with no displacement or deformation of the targeted tissue occurring
over the
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temporal span of data acquisition. However, for in vivo applications, natural
motion occurs
even if the patient is compliant. As the texture spacing of interest
decreases, the adverse
consequences of motion become more acute. More to the point, this type of
averaging is
based upon the assumption that the underlying signal is the same across
acquisitions, and
that only the zero-mean, complex-valued, additive, white Gaussian noise
(CAWGN)
changes. If the signal itself changes, the result will be an average, not only
of the noise, but
also of the N different versions of the underlying signal, which really
doesn't improve SNR.
[PARA 951 Using low SNR samples to estimate and correct for motion will result
in a
high variance of the estimated position. This in turn yields a large variance
in the
"corrected" acquisitions and does not yield the anticipated increase in SNR
when these
acquisitions are averaged. This implies that the multiple samples need to be
derived from
the same acquisition sequence, where motion between samples is extremely
small. This is
enabled by the embodiments disclosed herein.
[PARA 961 The issue becomes more acute with shorter structural wavelengths.
Consider
two acquisitions, noise-free for the moment, one of which has been displaced
by an amount
d. For a given k-value, an attempt to average them produces:
Y[27-t-k] := S [27-t-k][1+ j2zkd 11 2
(0.1)
S (27ck) Y (27ck)
Where is the complex-valued signal, and represents the average of
the
two acquisitions.
[PARA 971 This can be expressed as:
Y (27ck):= S(2R-k)e-vekd cos(irkcl)
(0.2)
Which shows both a magnitude attenuation and phase shift, due to the
displacement d.
Limiting the magnitude attenuation to a floor value a, where 0 < a < 1, limits
d to:
Idl cos' (a)
71-k (0.3)
[PARA 981 This shows that, for a given magnitude error, the allowable
displacement
decreases with increasing values of k. This is because, the smaller the
textural spacing of
interest, the less motion can be tolerated over the course of data
acquisition.
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[PARA 991 To deal with this problem, an alternate approach is taken in the
embodiments
disclosed herein, which is to dispense with the frequency-encoded readout and
to sample
specific k-space points, acquiring one or multiple measurements at each k-
value of interest
at a single spatial location and orientation at a time.
[PARA 1001 Within a given acquisition in standard MR practice, there are M
samples
which are acquired of the echo. Instead of acquiring a sample at each k-value,
N < M of
those samples could be used for estimation of the (complex-valued) underlying
signal value
at a specific k-value. Multiple samples within an acquisition can be combined
with much
less concern of movement than across acquisitions because they are much closer
in time.
[PARA 1011 If the entire echo is used to measure one k-value, the receive
bandwidth can
be adjusted so as to pass the most abundant resonant peaks in the underlying
NMR
spectrum, and attenuate frequencies above them.
[PARA 1021 Taking a straight MRS spectrum (no structural phase encodes), would
yield a
spectrum consisting primarily of peaks corresponding to H20 (with a chemical
shift of
6=4.7ppm), as well as Carbon-Hydrogen bonds which occur in fat (e.g. CH3, CH2,
CH=CH, etc.), each with a different chemical shift ranging from 0.9-5.7ppm,
with the most
abundant resonance coming from CH2 in the aliphatic chain which occurs at
6=1.3ppm.
[PARA 1031 Assuming use of a 3T machine, since the Gyromagnetic ratio of
Hydrogen is
y=42.576MHz/T, the chemical shift values are in the range of 166Hz (for CH2)
to 600.3Hz
(for H20). As long as a (single sided) receiver bandwidth in excess of 600.3Hz
is used, the
H20 peak will pass. Assuming baseband sampling, this implies a sampling rate
>1.2kHz
(note, if complex base-band sampling is used, this could theoretically be
reduced by about
1/2.) The point here is that a narrow bandwidth can be used by this method,
and sample rates
on the order of 800us. Noise on the signal is thereby reduced and multiple
repeats of the k-
value acquisition data are acquired in milliseconds, thereby making the
acquired data
immune to patient motion. For comparison, a single imaging acquisition is made
with a TE
of -30ms, and TR on the order of 500ms-2000ms. To acquire the repeats
necessary for
signal averaging can take minutes¨a temporal range wherein respiratory,
cardiac, and
twitching motion limits resolution through motion-induced blurring. The
claimed method
enables acquisition of values in regions of k-space which have very low signal
levels, such
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as would be found for higher k-values (shorter textural-wavelengths) ¨the fine
texture
range that has hitherto remained elusive.
[PARA 1041 To maximize the signal, the non-zero frequencies of abundance are
selected.
In general, this does not correspond to a mere averaging of all of the samples
acquired.
Instead it is akin to a matched filter which is "tuned" to the frequency of
interest,
corresponding to the specific chemical species of interest.
[PARA 1051 As a side note, the full NMR spectrum may be extracted (without any
phase
encoding gradients: just volume selection) to obtain a baseline of the
underlying signal
strength (and associated frequencies), which in turn will be spatially
modulated, providing
insight into textural wavelengths through knowledge of the chemical species
expected in the
textural elements under study.
[PARA 1061 The isochromats of interest can be extracted by acquiring N samples
of the
echo, then taking the Fourier transform. Since the echo is being played out
with no
gradient, the strength of the resulting signal at the Isochromat of interest
will correspond to
the (complex-valued) k-value coefficient of interest.
[PARA 1071 Given the goal is to extract the relative magnitude of textural
wavelengths,
just the magnitude vs. textural wavelength measurement is the required
information.
However, in order to extract sufficient signal strength and differentiate it
from the
underlying noise floor, the complex phasor values must be preserved until the
end.
[PARA 1081 The relationship between the noise floor, the signal strength (at a
specific
isochromat where there is an abundance of chemical species), the number of
samples
required, and the max tolerated error can be approximated as
N> a2(0.4)
Al e2
2 Al2
Where u represents the noise variance, represents the squared magnitude of
the
isochromat(s) of interest, and 0 < e <1 represents the allowable error of the
estimate.
Further assuming that the noise is mostly sourced from the biological sample,
this can be
further approximated as:
NF =k TB
N> eff õ
IA12 e2 (0.5)
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Where NFeff is the effective noise figure of the receiver, kB is Boltzmann's
constant, T is the
temperature in Kelvin of the biological sample, and B is the receiver
bandwidth. In this
case, N can be used as a guide to the number of samples that need to be
acquired within a
given acquisition in order to create a reasonable estimate.
[PARA 1091 If the number of samples required exceeds the number available in
one
acquisition, combination of measurements from a single acquisition may be
needed to
maximize the signal, prior to spatial reregistration between acquisitions. A
reasonable
estimate and displacement correction between the two or more acquisition sets
is needed.
Combination of measurements at a single k-value from a single TR block can now
be used
to boost the SNR such that reregistration between successive TRs has a much
greater
chance of success.
[PARA 1101 While the entire set of samples acquired in an echo or entire TR
could be
allocated to the estimate of one coefficient in k-space, if acceptable values
can be estimated
using fewer than the maximum number of echo samples, it opens up the
possibility of being
able to acquire more than one coefficient in k-space within a specific echo or
TR.
[PARA 1111 Various pulse sequences are provided for exemplary implementations
of the
embodiments disclosed herein. The examples may be combined with each other and
with
other MR imaging techniques, in parallel and in integrated forms, to obtain
the desired
micro-texture imaging associated with investigation of diseases having various
pathologies.
FIG. 3 shows an example timing diagram for a pulse sequence for data
acquisition using
the embodiments disclosed herein. RF pulses included in trace 302 are employed
to excite
selected volumes of the tissue under investigation, as in typical MR imaging.
A first RF
pulse, 304, is transmitted coincidentally with a gradient pulse 308 on the
first magnetic field
gradient, represented in trace 306. This excites a single slice, or slab, of
tissue the
positioning of which is dependent on the orientation and magnitude of the
first gradient, and
the frequencies contained in the RF pulse. The negative gradient pulse, pulse
310, rephases
the excitation within the defined thickness of the slice or slab.
[PARA 1121 A second RF pulse 312, at twice the magnitude of first RF pulse
304, is
transmitted coincidentally with gradient pulse 316, on a second gradient,
represented in
trace 314, exciting a slice-selective refocus of spins, this second tissue
slice intersecting
with the first. (As this second RF pulse 312 tips the net magnetic vector to
antiparallel to
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Bo, it results in inversion of spins and subsequent refocusing, thus leading
to a signal echo at
a time after the 180 degree RF pulse equivalent to the time between the 90
and 180 RF
pulses.) An initial higher value gradient pulse, 318, at the start of gradient
pulse 316 is a
crusher, or "spoiler" gradient, designed to induce a large phase wrap across
the tissue
volume. A similar gradient pulse, 322, at the trailing end of pulse 316, as it
comes after the
180 degree RF inversion pulse, unwinds this phase wrap. In this way, any
excitation that is
not present prior to the 180 degree RF pulse, such as excitations from
imperfections in the
180 pulse itself, will not have this pre-encode so will not be refocused by
the second
crusher, hence will not contribute to the signal. In summary, the second RF
pulse, in
combination with the applied second gradient, provides slice selective
refocusing of the
signal in a region defined by the intersection of the first slice and the
second slice set by this
second gradient.
[PARA 1131 An encoding gradient pulse 326, on trace 314, sets an initial phase
wrap,
hence k-value encode, along the direction of gradient pulse 326. In general,
the k-value
encode can be oriented in any direction, by vector combination of the machine
gradients but
for ease of visualization is represented as on the second gradient.
[PARA 1141 A refocusing third RF pulse 328, applied in combination with
gradient
pulse 332 on a third gradient, represented by trace 330, defines a third
intersecting slice
selective refocus to define the VOI. Gradient pulse 332 again employs crusher
gradients.
[PARA 1151 The negative prephasing gradient pulse 326 winds up phase such
that, at the
signal echo following the second 180 RF pulse, signal acquisition starts at
high k-value,
which may then be subsequently decremented (or incremented or varied in
orientation) for
further acquisitions, as will be described below. As energy density in the
signal is generally
proportional to k-1, this method ensures k-values with lower SNR are acquired
first, before
T2 effects have caused much overall signal reduction.
[PARA 1161 With all gradients off, a receive gate 333 is opened to receive the
RF signal,
which is shown in FIG. 3 as pulse 334 on trace 336. The RF signal in trace 336
is a
representation showing only the signal present in the receive gate window
without showing
the actual details of the RF signal outside the window. Sampling occurs as
represented by
trace 338 beginning with the initial k-value, 340a, seen on trace 324. Note
that, at the scale
of the drawing, the sampling rate is high enough that the individual triggers
of the analog to
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digital converter (A/D) have merged together in trace 338. (The expanded time
scale in FIG.
4 described below shows the individual A/D triggers.)
[PARA 1171 In regions of k-space where the corresponding coefficients are
sufficiently
large that they can be well-estimated using a small subset of the samples of
one echo,
acquisition of another k-value, obtained by applying a gradient pulse 342a
shown on trace
314, to select a new k-value, during the time the echo is being recorded, is
accomplished.
After a suitable settling time, another set of samples of the echo (now
derived from the new
k-value coefficient) can be collected. This process can be repeated, acquiring
multiple
samples at each of a select set of k-values within one TR. A plurality of
samples are taken at
the initial k-value 340a. A k-value selection gradient pulse 342a is then
applied and the
resultant k-value 340b is sampled. (Though shown in the figure as a negative
pulse on the
second gradient, decrementing the k-value, in practice this pulse and
subsequent k-value
gradient pulses can be designed through any vector combination of gradients to
select any
k-value or orientation.) Similarly, the k-value selection gradient pulse 342b,
selects a third
k-value 340c which is sampled by the A/D. Each gradient pulse changes the
phase wrap,
selecting a new k-value. Application of a k-value selection gradient pulse
(342c ¨ 342f)
followed by multiple sampling of the resultant k-value coefficient is repeated
as many times
as desired. While data is being acquired throughout, the samples of interest
are acquired
when all gradients are off. The gradient orientations for slice and k-value
select may be
coincident with the machine gradients, which are aligned to lie coincident or
orthogonal to
the Bo field. Alternatively, the acquisition directions and k-value encodes
may be selected
using gradients that are a vector combination of all three machine-gradient
axes.
[PARA 1181 In the circumstance where it is desired to measure a low SNR k-
value the
prewinding encoding gradient pulse can be set such that the fist k-value to be
measured is
the desired low SNR k-value. Alternatively, the prewinding gradient pulse can
be set to
zero so that the first k-value measured is kO. A measurement of k0 may be
desired for the
purpose of determining the systems receiver sensitivity to the particular VOI,
determining
the relative prevalence of isochromats (e.g., water vs. lipids) irrespective
of texture in the
VOI, or for the purpose of establishing a reference value for normalization of
the other k-
values measured in a VOI or for comparison with k-values from other VOI.
Furthermore a
strategy for gathering a specified set of k-values for a VOI may include
measuring the low
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SNR k-values (typically the higher k-values) in a first set of multiple TR and
then
measuring k0 and other higher SNR k-values in other TRs while remaining in the
same
vol.
[PARA 1191 As is shown diagrammatically in FIG. 3, the signal reaches a
maximum at
the time of the spin echo. It is also shown diagrammatically that the signal
is varying
throughout the acquisition of the multiple RF measurements of a k-value and
more so
between successive blocks of measurements of k-values. Alignment in time of
the
measurement of the low SNR k-values with the highest echo signal enhances the
SNR of the
k-value measurement, alternatively alignment of higher SNR k-values with lower
echo
signal allows gathering additional useful k-value acquisitions during the
echo. The term k-
value measurement is understood in the art to be a "shorthand" term for
measurement of
signal at a k-value.
[PARA 1201 FIG. 4 shows a close-up of the pulse sequence of FIG. 3 during the
initial
portion of the RF sampling window 338 between 7.25 and 8.00 msec. Multiple
samples of
the same k-value, taken in rapid succession with all gradients off, provide
the input for
signal averaging to reduce AWGN when SNR is low. In a first block 344a of the
sampling
window 338, multiple samples 346a are taken of the first k-value 340a. During
application
of the k-value selection gradient pulse 342a, transition samples 348a are
taken. When the k-
value selection gradient is switched off, multiple samples 346b are taken at
the second k-
value 340b. Application of k-value selection gradient pulse 342b then occurs
with
associated transition samples 348b, and subsequent acquisition of samples 346c
of the third
k-value 340c after the gradients are switched off. The underlying signal is
minimally
impacted by motion due to the very short time window used to acquire data at
each given k-
value. Since the data is acquired with gradients off, there is no issue with
chemical shift and
the effective T2* is longer, boosting the signal value.
[PARA 1211 The sampled values of the echo, acquired while the k-value
selection
gradient pulse is ramped up, held steady, and then ramped down to zero, will
necessarily be
influenced by the applied gradient. These transition samples may provide other
interesting
information, but are not used in the consideration of a straight measurement
of the k-value
coefficient; only those samples which are recorded when there is no gradient
currently
active are used for this.
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[PARA 1221 A consistent number of samples at each k-value can be acquired, or
an
alternative sequence may be employed where, as k-values decrease, hence
increasing in
signal amplitude, fewer samples are acquired. A pulse sequence designed for
this type of
acquisition is illustrated in FIG. 5. Multiple samples of each k-value
targeted in the
acquisition are acquired in rapid succession, with all gradients off. These
repeats provide the
input for signal averaging in low SNR signals. As with the pulse sequence,
depicted in
FIGs. 3 and 4, the underlying signal is minimally impacted by motion due to
the very short
time window in which data is acquired for a given k-value.
[PARA 1231 Samples within the portions of the sample window 344a ¨ 344g
outlined on
FIG. 5 correspond to the number of samples acquired for a given k-value 340a ¨
340g each
induced by an unwinding pulse 342a ¨ 342f of the k-value selection gradient.
Nk, the
number of samples associated with a given k-value, can be selected based upon
expected
SNR, tissue contrast, contrast to noise, pathology, texture size, and/or
texture bandwidth.
For the example in FIG. 5 it can be seen that a decreasing number of samples
are taken for
progressively smaller k-values (larger textural features). This is because, as
previously
discussed, to first order signal amplitude increases with decreasing k-
value¨energy density
is generally proportional to k-1. For this same reason, larger k-values are
acquired first in
this scheme, when T2 effects are least, the longer wavelength, higher signal
strength, k-
values being recorded later in the acquisition.
[PARA 1241 Refocusing the echo, and/or a new TR can be used to build up a
library of k-
space samples. Acquisition of multiple k-values within one TR can be
facilitated by
application of multiple refocusing gradients and/or RF pulses, to increase the
time over
which the additional k-values can be sampled within a TR. These later echoes
would
presumably be used to acquire the coefficients of the lower k-values in the
selected set, as
their energy density in the continuum of values is generally higher so the
effect of T2 decay
on overall signal will not affect them as severely as it would the higher k-
values. In this way
a larger portion of the required k-space filling can be accomplished over
fewer TRs,
allowing more rapid data acquisition, minimizing the need for repositioning
the VOI.
[PARA 1251 FIG. 8 shows an extension of the basic sequence of the embodiments
disclosed herein, using spin-echo refocusing to extend the record time for the
TR.
Application of a refocusing RF pulse 802 with an associated gradient pulse 804
results in
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slice-selective refocusing. After an appropriate settling time, a second
sampling window
806 is opened by the receive gate 808. Multiple k-value selection gradient
pulses 810 are
applied to increment the selected k-value and, after switching off each
successive gradient
pulse, multiple samples of the selected k-value are acquired in the sampling
window. A
second slice-selective refocusing RF pulse 812 with associated gradient pulse
814 again
inverts the spins and, after application of each in the multiplicity of k-
value selection
gradient pulses 820, data is acquired in a third sampling window 816, opened
by the receive
gate 818. As shown in the drawing, an increasing number of k-values may be
sampled with
each refocusing. Refocusing can be repeated until the decrease in signal level
from T2 and
other effects makes further signal acquisition ineffective. Another method to
extend the
record time by exciting multiple signal echoes, is to use one, or a series of,
gradient recalled
echoes (GRE). GRE are different from the SE in that they cannot refocus the
effects of
stationary inhomogeneities, so T2* effects limit the number of repeats.
[PARA 1261 In addition to the tissue contrast available, the k-values
associated with
particular pathology will be part of the determination of the number of
samples needed for
signal averaging, Nk. In liver fibrosis, as an example, the wavelength of
pertinent textures is
in the range of 400 microns, i.e. a k-value of 2.5 cycles/mm. This is similar
to the textural
spacings seen in fibrotic development in many other diseases, such as cardiac
fibrosis. The
spacing of elements in trabecular bone varies a lot, but the minimum spacing
of interest is
the width of trabecular elements, which are approximately 80 microns, setting
a maximum
k-value of 12.5cyc1e5/mm. In neuropathology, many of the textures of interest
are very fine,
on the scale of 50 microns, equivalent to a k-value of 20 cycles/mm.
[PARA 1271 Each pathology will dictate what exactly is needed as quantitative
data, i.e.
what part of the continuum of k-values needs to be monitored, and with what
resolution and
sensitivity. In some pathologies, short (long) wavelength features increase at
the expense of
long (short) wavelength features (e.g. liver fibrosis). In other pathologies,
an amplitude
decrease and broadening of short wavelength features indicates disease
progression¨e.g.
degradation of the ordered formation of cortical neuronal minicolumns
(approximately 80-
micron spacing) with advancing dementia. In bone, with increasing age, first
the highest k-
value features disappear in the structural spectrum. Next the major structural
peaks shift
slowly towards lower k-values with advancing osteoporosis, the pace of this
shift
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accelerating as an increasing percentage of trabecular elements thin to the
point that they
break.
[PARA 1281 The signal level obtainable will depend on anatomy to some extent.
For
instance, though the resolution needed is highest in brain, the proximity of
the cortex to the
surface of the head ensures that use of a surface coil will provide
significant signal boost for
cortical structures. Lower resolution is required in liver, as the structures
of interest are on
the order of several hundred, rather than tens of microns. But, the organ is
deeper (further
from the coil) reducing the measured signal. Using the in-table coil for spine
data
acquisition yields modest signal level and good stabilization. Also, bone is a
high contrast
target, so the SNR requirement is not as stringent. For all these reasons, the
exact number of
repeats needed for averaging depends on more than the k-value range targeted.
[PARA 1291 FIG. 6 shows a simulation demonstrating that the ability provided
by the
claimed method to acquire many repeats of a targeted k-value within a single
TR, or single
excitation pulse, enables robust signal averaging to boost SNR. Assuming a
subject
displacement rate (which has in practice been measured clinically over the
course of several
scans) of 30um/sec, and a sampling rate = 33.3kHz (ATsample = 30 us), 90
repeat samples
for averaging can be taken rapidly enough that, even up to a k-value of 20
cycles/mm
(texture wavelength = 50um), the acquisition remains immune to motion effects.
[PARA 1301 FIG. 7 shows that, for comparison, using the conventional approach
of
acquisition of a spatially encoded echo, even assuming a relatively fast
gradient refocus
sequence, which would provide a sampling rate of about 67Hz (ATsample =
15msec),
subject motion over the time needed for 90 repeats would severely degrade the
signal, and
any ability to improve SNR by signal averaging. The situation is actually
worse due to the
fact that to acquire 90 repeats using conventional spatially-encoded echoes
would require
several TRs, making the acquisition time significantly longer, and the signal
degradation
due to motion much more severe. With the exception of the very lowest k-
values, the
potential SNR gain due to multiple sample combination has been nullified by
the effects of
motion.
[PARA 1311 By acquisition of a large enough selected range of k-values,
construction of a
structural profile in one or more dimensions becomes a possibility. As
discussed above,
refocusing echoes within a single TR or excitation pulse, or multiple
TRs/pulses, can be
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used to build up a library of k-space samples. Phase coherence might not be
maintained
between different k-values if they are acquired in TRs separated temporally
such that
displacement has occurred between them. If a primary interest is in the
relative strength of
signals at particular k-values, this is not a problem. If creation of a
profile or an image from
this library of values is desired, the necessary post processing will have as
input the high
SNR measure obtained within each TR using the embodiments disclosed herein.
These
measures can then provide robust input for any required reregistration between
echoes or
TRs towards constructing a profile. As an example selection and measurement in
a first TR
of a set of selected k-values may be accomplished with at least one having a
low k-value. In
a subsequent TR, selection of the same set of k-values will allow re-
registration of the data
between the two TRs since even if significant motion has occurred the phase
change in the
low k-value phase shift will be less than for higher k-value textures and may
be correlated
between the two TRs. Basically, the higher the k-value, the greater the phase
shift due to
subject motion. Acquiring signal from successive encodes with a large
difference in k-value
enables a better estimate of phase shift by careful comparison of the apparent
phase shift for
each.
[PARA 1321 This is very similar to x-ray diffraction, wherein the magnitude-
only
information (no phase) obtained presents the challenge of determining a best
estimate of the
corresponding structural profile based on this magnitude-only information.
Algorithms exist
towards solving the problem, the chance of success depending on the range of k-
value
coefficients obtained, the SNR of each averaged coefficient, and the width of
values
contained in a nominally single-valued acquisition of k-value. The chance of
success in this
effort is greatly increased using the claimed method due to its immunity to
subject motion.
[PARA 1331 The ability to reconstruct a profile from k-value data depends
somewhat on
the spectral broadness of each single-k-value acquisition. While this is
influenced by the
VOI (Volume of Interest) size and shape, it is also influenced by k-value and
pathology, as
degradation of tissue often tends to lead to more textural randomness within
tissue.
[PARA 1341 Selection of the VOI¨shape, dimensions, orientation, and
positioning within
an organ/anatomy affects the data measured and its interpretation. The VOI
shape can be
chosen to maximize usefulness of the acquired data. Data can be acquired in
different
directions, and at different textural wavelengths (k-values) within a VOI
enabling
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assessment of textural anisotropy. Texture can be sampled in multiple VOIs,
either
interleaved within a single TR/excitation pulse, or in successive TRs, towards
assessment of
pathology variation across an organ. Standard interleaving processes for the
VOI may be
used within a TR to provide additional data by applying additional encoding
pulses on
vector combination gradients and associated k-value selection gradient pulses
for k-values
in the interleaved VOI. As previously described, additional excitation RF
pulses with
associated slice selection gradients may be repeated within the same TR by
exciting a
volume of interest with a gradient set in each repeat having at least a first
gradient with an
alternative orientation from the first gradient pulse 308 applied initially in
the TR, to define
an additional VOI for excitation in new tissue, that does not overlap any
previous VOI in
the TR (fourth, fifth and sixth gradients in a first repeat and succeeding
incremental
gradients in subsequent repeats). This response can be mapped, or the several
measures
taken and averaged, whatever is appropriate for the targeted pathology. This
is similar to the
multi-positioning of tissue biopsy. However, in the case of tissue biopsy, the
number of
repeats is limited due to the highly invasive nature of the technique. The
minimum number
of structural oscillations to be sampled at a specific k-value dictates a
minimum VOI
dimension in the direction of sampling¨the length required varying inversely
with targeted
k-value.
[PARA 1351 To ensure adequate sampling of structure when targeting a range of
k-values,
the VOI dimension in the sampled direction can be held constant for all k-
values in the
targeted range, with the result that the number of structural oscillations
sampled will vary
with k-value. This is a simple solution, requiring the sampling dimension be
set by the
lowest k-value (longest wavelength structure). Using this approach, the
sampling dimension
of the VOI is larger than required for the highest k-value in the range, thus
providing less
localization within the tissue than would be otherwise possible.
[PARA 1361 Alternatively, data at widely differing k-values can be acquired in
successive
TRs, using changing VOIs tailored to the specific k-value targeted. Or, the
dimensions of
the VOI can be selected such that acquisition in different directions within
the VOI will be
tailored to sampling in a specific textural frequency (k-value) range.
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[PARA 1371 Similarly, the VOI may be held constant and the vector combination
gradient
for the encoding and k-selection pulses may be altered from TR to TR for
assessing feature
size.
[PARA 1381 In some instances, it is desired to localize tightly in the spatial
domain, to
broaden the localization in k-space. By defining a non-cubic acquisition
volume, it would be
possible to acquire data from differing k-values along the different
(orthogonal or other)
directions within the VOI, within one TR. The elliptical cross-section VOI 902
in FIG. 10
is one such possibility. Acquisition along any radial direction, as well as
along the axis of
the shape, would be possible within one TR.
[PARA 1391 Additionally, the flexibility of the embodiments disclosed herein
may be
used to sample k-space in a linear or in a curved trajectory. For example,
texture could be
sampled along radial lines, or along an arc or a spiral, to extract
information of textural sizes
along different spatial directions. These methods can be used to determine the
anisotropy of
texture, or the sensitivity to alignment in structures that are semi-
crystalline, such as cortical
neuronal columns, or to more rapidly build up a library of k-values within a
targeted extent
of tissue in an organ.
[PARA 1401 During one TR (i.e., one 90 degree excitation) k-value encodes can
be
applied in multiple directions by changing the applied vector combination
gradients for
encoding and k-selection pulsing. The exact form of the VOI and sampling
direction can be
used to yield much textural information. For instance, the organization of
cortical neuron
fiber bundles is semi-crystalline, as the bundles in healthy tissue form in
columns. Because
of this, the measure of textural spacing perpendicular to the bundles is very
sensitive to
orientation. When the orientation is exactly normal to the columns, a very
sharp signal
maximum is expected, the signal falling off rapidly as the orientation varies
on in either
rotational direction away from this maximum. One way to measure the spacing
and
organizational integrity (a marker of pathology) would be to "rock" the
acquisition axis
around this maximum looking for a resonance in signal intensity. This approach
of looking
for "textural resonances" by looking for signal maxima can be applied in any
tissue region.
As pathology degrades the organizational integrity, the sharpness of this peak
will degrade
and the signal maximum will be reduced.
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[PARA 1411 Similarly, the randomness of the spacing in certain textures can be
assessed
by varying the length of tissue sampled in a specific, or in multiple
directions, with
subsequent change in acquisition length. The selected value for that length
can be varied
over multiple TRs to test the sensitivity of the measured coefficient to this
parameter.
[PARA 1421 The VOI can be selectively excited by a number of methods, for
instance
intersecting slice-selective refocusing, selective excitation using phased-
array transmit in
combination with appropriate gradients, adiabatic pulse excitation to scramble
signal from
the tissue outside the region of interest, as examples. Parameter selection
for the various
methods can be done with SNR optimization in mind. For instance, the VOI
generated by a
slice selective excitation and two additional mutually- orthogonal slice
selective refocusing
pulses, as by VOI 904 in FIG. 9. Through careful RF pulse design, the shape of
the VOI can
be designed so that the edges are smooth and more approximate a windowing
function, as
shown in FIG. 10. These windowing functions provide the volume selection
without
adverse impact on the spatial frequencies. Recall, in Fourier theory, each
spectral line is
smeared by the convolution of a Fourier transform of the window function. It
is desirable to
minimize this smearing of the underlying spectrum, as it decreases the energy
spectral
density, and adversely impacts SNR.
[PARA 1431 Importantly, as has been discussed previously, the VOI can be moved
from
place to place within an organ or anatomy under study to measure the variation
of
texture/pathology. This response can be mapped, or the several measures taken
and
averaged, as appropriate for the targeted pathology. This is similar to the
multi-positioning
of tissue biopsy. However, in the case of tissue biopsy, the number of repeats
is limited due
to the highly invasive nature of the technique.
[PARA 1441 Different diseases and conditions affect tissue in different ways.
Generally,
pathology advancement entails: 1) a loss of energy density in specific regions
of k-space,
and/or 2) a shift in textural energy density from one part of k-space to
another, both effects
being accompanied by 3) changes in the width of existing peaks in the
continuum of textural
k-values. Using trabecular structure as an example¨with decreasing bone health
the
average separation of trabecular elements widens (texture shifts to lower k-
values) and
becomes more amorphous (broader peaks in k-space), while in parallel the
structural
elements thin (a shift to higher k-values in a different part of the
spectrum). Other
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tissues/organs are affected by diseases that have their own individual
signatures in tissue
texture.
[PARA 1451 Using the embodiments disclosed herein, k-space is probed to reveal
texture
in such a way as to eliminate the loss of signal resolution that results from
subject motion
blurring. Instead of measuring the large continuum of k-values needed to
create an image,
the focus here is on acquisition of a select few k-values per TR, with
sufficient repeats of
each to yield high SNR. Each of the individual acquisitions is centered on a
single k-value.
While the spatial encode is, to first order, a single spatial frequency
sinusoidal encode, there
are a number of factors which have the effect of broadening the spatial
frequency selectivity
of the k-value measurement. One significant factor affecting the broadness, or
bandwidth, of
the k-value measurement is the length of the sampled tissue region. A longer
sampling
length encompasses more textural wavelengths along the sampled direction,
which has the
effect of narrowing the bandwidth of the k-value measurement. (This is the
inverse
relationship between extent of a measurement in real and in k-space.) Hence,
an aspect of
the claimed method is the ability to set the bandwidth of the k-value
measurements by
appropriate selection of the sampling length determined by the VOI dimensions
or
determined by the acquisition dimensions. Using this method, the bandwidth of
the
measurement can be set according to the desired k-space resolution appropriate
to the tissue
being evaluated. (Need both high k-values for good texture resolution, and
high resolution
in k-space for sensitive monitoring of pathology-induced changes.) For highly
ordered
structures one could choose a set of narrow bandwidth measurements distributed
over the
expected range of texture wavelengths, whereas in a more randomly ordered
structure, such
as the development of fibrotic texture in liver disease, one could choose to
use a single, or a
few, broadband k-value measurements to monitor development of the fibrotic
texture.
[PARA 1461 A measure of both the relative intensities of the various textural
k-values
present in a tissue and the broadness of the peaks along the continuum of
textural k-values
present within the texture under study is needed. As such, data acquisition
can be designed
to probe specific region(s) of k-space, with parameter selection that will
enable
measurement of the relative width of peaks arising from the underlying tissue,
rather than
that resulting from experiment parameters. It is necessary to recognize the
interaction of the
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two components, and design experiments to yield the best measure of pathology-
induced
tissue changes.
[PARA 1471 It is desirable to obtain a good measure of texture by acquiring
multiple
measures of signal amplitude at specific k-values close in time before motion
blurs the data,
taking repeat measurements in minimal time to allow best inter-measure
correlation for
averaging. An alternative to acquiring many repeat measures at one point in 3D
k-space, is
to acquire data with a gradient on, such that the k-value is changing
continuously across the
acquisition, the extent in k-space being determined by the height of the
gradient and its
pulse width. In addition to varying the magnitude of the k-vector, its
direction over the
course of data acquisition can also be varied. Combination of direction and
magnitude
changes across an acquisition result in a curvilinear trajectory through k-
space. If this
deviation is small enough that the k-values remain correlated to some extent,
they can be
combined more effectively to increase SNR than if they were simply averaged.
Gradient on
acquisition can therefore be used to intentionally vary the direction and
magnitude of the k-
vector, for the purpose of smoothing signal speckle¨which manifests as a time
varying
signal over the data acquisition, resulting from interference of the
individual spin signals'
varying phases and amplitudes. The selected variation in k-value direction and
magnitude
across the acquisition is chosen to provide sufficient combined measures to
get an
estimation of the representative power within a neighborhood of k-space.
[PARA 1481 Varying the k-value to reduce speckle can be accomplished within a
single,
or multiple, echoes. For a sphere in k-space, defined by the magnitude of the
k-value under
study, the k-value can be varied by keeping the magnitude of k-constant but
sweeping the
vector over the surface of the sphere, or the same angular orientation may be
maintained,
and the magnitude of k varied, or both can be varied simultaneously
[PARA 1491 For the purpose of reducing speckle effects, these variations would
usually
be small enough deviation from either the k-magnitude or direction that there
is a
meaningful correlation between the measurements for the particular tissue
under
investigation.
[PARA 1501 The major components of the spatial frequency will be the same in
all those
measurements (they are correlated) unless the measured tissue is a highly
crystalline texture.
But the normal diffraction pattern for a micro-crystalline or amorphous
structure has a lot of
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speckle. Consequently, by sampling a number of points in the same region of k-
space they
can be combined in various ways, selected to provide optimal smoothing to
reduce the
speckle-pattern. A better and more robust measure, from averaging out the
fluctuations, is
the result.
[PARA 1511 A number of approaches to "dither" k-value to reduce speckle or to
tailor
width in k-space may be employed. A first approach employs constant k-
magnitude plus
sweeping through a range of angles by keeping gradients on during acquisition
and
combining the measures using correlative information to eliminate speckle.
Alternatively,
the same direction in k-space may be maintained but the magnitude varied by
leaving
gradient on during acquisition and combining the measures using expected
correlation. As
yet another alternative, both magnitude and direction may be varied
simultaneously or over
an acquisition series, essentially performing the other two alternatives
simultaneously to
both reduce noise and provide a better assessment of the representative k
magnitude in a
structure in a "small" region around a specific k-value, i.e., to reduce
speckle.)
[PARA 1521 For combining the measures at different k-magnitudes, for noise
reduction
averaging, there is a phase shift from one radius (magnitude) to the next from
the gradient
wind-up. Rephasing may be accomplished before averaging.
[PARA 1531 Combining the different magnitude measures in an amorphous
structure is
more well-known than combining different angled measures. Now in addition to
the scheme
of reducing thermal noise by rapid sampling the fluctuations due to speckle
(which though
real signal confounds good assessment of the spatial frequency) may be
reduced.
[PARA 1541 A dynamic k-space acquisition is therefore employed. The
acquisition mode
is dynamically chosen based upon the Signal to Noise Ratio (SNR) of the signal
at various
k-space locations. The gradient, applied during signal acquisition, post-
acquisition receive
bandwidth, and estimation algorithms used are dynamically adjusted based upon
the
expected SNR values in k-space to optimize acquisition time and post-processed
SNR. In
regions of high SNR, a single sample at a given k value may be a sufficient
estimator. This
requires a relatively wide receive bandwidth to accommodate the relatively
rapid signal
variations in the receive chain as k is changed rapidly (due to the large
gradient).
[PARA 1551 In regions of moderate-to-low SNR, the gradient magnitude is
decreased so
that, subsequent samples, while not taken at identical k values, are
correlated, which in turn
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can be used to improve estimates of the underlying signal values within that
range of k-
values.
[PARA 1561 Correlation may be introduced in k-space due to selected windowing
in
profile space. To enable combination of sequential samples from the ADC so as
to improve
SNR, correlation among successive samples will be increased by proper choice
of
windowing in profile space, a shorter window driving a greater correlation
distance across
sequential values in k-space and a longer window resulting in lower sample to
sample
correlation. Inducing correlation of neighboring points in k-space by
windowing in profile-
space is a mathematical tool that can, in many cases, help to measure the
underlying texture
in a low SNR environment. Basically, windowing blurs the data so that the k-
value power
spectrum is smeared out through k-space, so that sequential measures can be
averaged/combined more easily to increase SNR.
[PARA 1571 In a very high SNR environment as large a window as possible is
used
because a measure of the actual textural power distribution across a range of
k-space is
desired. The longer the sampled region in real (profile) space the more
accurate the
measurement when measuring amorphous textures. Reducing the sampled region by
windowing to induce correlation in k-space actually obscures the specific
desired
measurement point.
[PARA 1581 However, while facilitating measurement, inducing correlation
through
windowing does blur to a greater or lesser degree the underlying relative
power density
profile in k-space arising from the underlying texture, which is the target of
the
measurement. As the sample-to-sample spacing (determined by the analog to
digital
converter speed and gradient height) in k-space decreases, there will be
increased
correlation, which can be used in post-acquisition processing to form better
estimates.
Additionally, the receive bandwidth in these regions can be decreased, which
further
decreases the noise floor.
[PARA 1591 In regions of very low SNR, multiple acquisitions of the measured
signal
level at a specific k value can be taken, with zero (and/or non-zero) gradient
during
acquisition. The multiple acquisitions can then be optimally combined to
provide an
estimate for specific k values.
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[PARA 1601 An example Hybrid pulse sequence is shown in FIG. 11. This
particular
sequence is an example of a rapid acquisition with refocused echoes (RARE)
type sequence
wherein three different levels of gradient are used for acquisition as
illustrated across three
separate echoes. The shown pulse sequence for selecting the desired VOI and
initial phase
wrap for k is as described in FIG. 3 and is numbered consistently in FIG. 11.
While this
exemplary pulse sequence for establishing the VOI is employed in the various
examples
disclosed herein, the determination of VOI may be made by any of numerous
approaches
including, as an example, time varying RF pulses with commensurately time
varying
gradients applied. Similarly, during or after the pulse sequence employed for
determining
the VOI, the encoding gradient pulse may be applied for selection of the
initial k-value. The
data recording starts with acquiring values in regions where Ikl>>0, given
that the signals
are smallest there and should be acquired first. The second echo samples
values associated
with Ikl>0, but whose signal levels are still relatively small and require
combination of
multiple measures to provide robust SNR. The final echo samples values
associated with Ikl
in the neighborhood near Ikl ¨ 0 where the corresponding signals are largest.
Note that this
is just one example of how this hybrid approach, using both zero and non-zero
gradient in
one acquisition, could be used. Different amounts of k-value windup (as
determined by the
gradient height and pulse duration) can be acquired in one echo rather than in
multiple
echoes as will be described subsequently. Multiple combinations of the
differing k-value
windup also can be acquired within one echo. Additionally, while refocusing is
disclosed in
the drawings as employing an RF pulse, gradient refocusing may also be
employed.
[PARA 1611 Details at expanded scale of the pulse sequence/signal acquisition
are shown
in FIG. 12. In this sequence, the first echo from RF pulse 328 with gradient
pulse 332 uses
the a pulse sequence similar to that previously described with regard to FIG.3
and
acquisition of multiple samples with gradient = 0 and incrementing of k values
with k value
selection gradient pulses 1100a, 1100b and 1100c. This samples multiple values
of a given
location in k-space, which values are then optimally combined. This is
appropriate for
regions of k-space whose values are very small and therefore have very low
SNR. This
typically occurs in regions where Ikl is large.
[PARA 1621 The second echo from RF pulse 1102 with gradient pulse 1104 is
acquired
with a small non-zero gradient 1106 acting as a time dependent phase encode. A
small
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gradient may be defined as: a gradient that induces samples in k-space which
will be
sufficiently closely spaced so that the samples are highly correlated. These
samples can
then be post-processed by an estimator which takes advantage of the high inter-
sample
correlation to improve the resulting SNR. Quantitatively an exemplary "small"
gradient
might be up to 20% of the magnitude of the encoding gradient pulse. As seen in
the figure
samples of multiple values in a relatively small neighborhood, Ak, in k-space
are obtained.
The spacing of Ak can be chosen such that, due to the windowing of the VOI,
there is high
correlation between neighboring samples. The correlation is exploited in the
estimation
algorithm to generate an optimal estimate of the signal levels across a
neighborhood in k-
space. This is appropriate for regions of k-space which have low SNR, but
whose values,
because of the correlation induced by the windowing function, vary slowly
across k-space
in a small neighborhood.
[PARA 1631 A third echo from RF pulse 1108 with gradient pulse 1110 is
acquired with a
relatively larger time dependent phase encode gradient 1112. The higher
gradient employed
herein creates sequential measurements in k-space which have a lower degree of
correlation
across the neighborhood of k-space under study. In this case, there is lower
inter-sample
correlation available for SNR improvement. A higher gradient may be employed
for
sampling k-space locations whose values have a high SNR to begin with (such as
would be
seen in lower textural frequency (low k-value) regions). As seen in the figure
samples
relatively widely spaced across k-space, well outside the inter-sample
correlation imposed
by the window function, are generated over the entire pulse. This is
appropriate for rapid
acquisition of values in k-space whose signal levels are relatively high and
enable high SNR
recording. In this case, a single sample at a given point in k-space provides
a high enough
signal. However, in such higher SNR regions, the higher gradient may be
employed and
selected bursts of data samples may be rapidly recorded. Each of these bursts
will have
substantial inter-sample correlation within the burst and may allow
computation of results
similar to that described for the lower gradient acquisition discussed above.
This may be
viewed as transitioning along the K-space line with the higher gradient while
sampling
blocks of data at closely spaced k values to maintain correlation.
[PARA 1641 Non-zero gradient acquisition allows sweeping across a curvilinear
path in k-
space. By a judicious choice of time increment, At, gradient magnitude, G,
w(X), and the
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total number of samples acquired, N, the neighborhood of "high" correlation
can be adjusted
to be M < N. This in turn would allow estimation of a multiplicity of distinct
values within
k-space by using a subset of M samples for each output estimation. Textural
data from
within a tissue region defined by the VOI can be acquired with the non-zero
gradient to
enable determination of the local distribution of power density of k-values
within a
neighborhood in k-space. The extent of k-space sampled with a gradient pulse
played out
during acquisition is determined by the gradient height and the gradient pulse
width (pulse
duration). The spacing between signal samples in k-space is determined by the
gradient
height and the sampling rate (limited by the maximum speed of the analog to
digital
converter). The correlation between sequential samples in k-space is
determined then by the
spacing between samples, by subject motion, by the window used to bracket the
acquisition
in physical space, and by the underlying texture.
[PARA 1651 A useful method for selecting the acquisition parameters is with
reference to
the degree of correlation needed within a set of values to be combined. The
wavelength of a
repeating structure (texture) is defined as the inverse of the k-value
associated with that
texture, X, texture = liktexture= To be able to combine a set of values
[measurements] to yield
improvement in SNR, the underlying textural signals must not be shifted in
phase by a
significant percentage of k texture relative to each other. In exemplary
embodiments, the
phase shift across the set of samples to be combined should be no greater than
80% of 2n.
[PARA 1661 Resolution in MR imaging is limited by subject motion during image
acquisition. This limitation can be very severe with non-compliant patients.
In addition to
patient motion/compliance, the resolution achievable in MR imaging which is
exemplary art
comparable to the present invention, depends on several factors, such as
tissue contrast,
organ, coil type, proximity to coil. Robust imaging of structures below about
5mm in extent
is problematic, and anything below about lmm is outside the realm of routine
clinical
imaging. This is a clear shortcoming as many tissue textures in the range of
about 5mm
down to 10um develop and change in response to pathology development, hence
measurement of these textures can provide much diagnostic information¨these
tissue
changes are most often the first harbinger of disease. It is this textural
wavelength range,
from about 5mm down to 10um that is targeted with the presently disclosed
method.
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[PARA 1671 To measure tissue texture, the range of wavelengths in real space
which can
be resolved, i.e. the wavelengths of the textures pertinent to the particular
pathology, are in
the range of several mm down to microns. This is the range made inaccessible
(blurred) in
imaging due to patient motion. As k is defined as 1/wavelength, a range of k-
values from
about 0.2 mm-1 to 100 mm-1 is employed in exemplary embodiments to define the
textures
of interest. This brackets the region of k-space of interest, and defines the
gradient height
and duration of the encoding gradient pulse to induce phase wrap to create a
spatial encode
for the specific k-value and orientation as well as for the non-zero gradients
applied for
measurement of the neighborhood around the initially selected k-value. The
method of the
embodiments herein for sample acquisition and post processing may all be
conducted in k-
space. The only localization in real space is the positioning of the VOI. Just
enough of the
neighborhood around a point in real-space is sampled to measure texture¨i.e.
to determine
the power distribution within a neighborhood in k-space around the selected
point in real
space.
[PARA 1681 The exact range needed varies with the targeted pathology. For
example:
[PARA 1691 Osteoporotic development in bone microarchitecture. As examples,
the
variation in average trabecular spacing (TbSp) from healthy to osteoporotic
bone brackets a
wavelength range of about 0.3mm to 3mm; the equivalent range of k-values is
0.34mm-1 to
3.4mm-1. With fibrotic liver disease monitoring change in liver tissue texture
from the
healthy collagen-highlighted vessel-to-vessel spacings to the diseased state
in which the
lobule-to-lobule spacing becomes the prominent tissue texture. Vessel-to-
vessel range is
0.4mm to 1.5mm translating to k-values of 0.67mm-1 to 2.5mm-1 while lobule-to-
lobule
spacing of approximately lmm to 4mm, translating to k-values from 0.25mm-
itolmm-1.
Angiogenic vasculature development around a tumor site typically changes from
the healthy
vessel texture spacing of around 100um; k = 10mm-1. Due to its chaotic nature,
the spacings
in angiogenic vasculature cover a broad range from about 10um to lmm, or lmm-1
to
100mm-1. Diagnostic assessment of dementia-related changes to the cortical
neuronal
spacing involves measuring high k-values, the healthy structure being about
100 um
spacing or k = l0mm-1. Variations of about 10-20% of this value, with
increasing
randomness in structure, mark the disease.
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[PARA 1701 Therefore, the acquisition parameters can be chosen such that (1)
the
gradient height/duration generates a range of k-encodes spanning the
neighborhood of k-
space over which it is desired to inspect the power density present in the
targeted tissue
texture, (2) the samples to be combined must occur close enough in time that
there is no
significant blurring due to subject motion across the acquisition time of a
block of samples
to be combined, and (3) the tolerable amount of motion depends on the
neighborhood of k-
space under investigation (i.e., the wavelength).
[PARA 1711 Acquisition of textural data from within a targeted VOI with the
non-zero
gradient enables determination of the local variation of power density of k-
values within a
neighborhood of the initial k-value in k-space. The extent of k-space sampled
at each
gradient pulse is determined by the gradient height and the pulse width.
Spacing between
the samples in k-space is determined by the gradient height and the sampling
rate.
[PARA 1721 These parameters are selected (1) to allow acquisition of
sufficient data for
combining toward significant SNR improvement, before subject motion can blur
the data
significantly relative to the texture to be measured, (2) to ensure sufficient
correlation across
the blocks of k-values from the acquisition to be combined to maintain a SNR >
0.5dB, and
(3) to set the extent of k-space over which the power density of k-values
present in the
texture is desired.
[PARA 1731 Blocks of sequential signal samples to be recombined for SNR
improvement
can be non-overlapping, or overlapping by a selected number of points, or a
sliding block
used so as to combine, for example, measures 1-4, 2-5, 3-6 and so on as will
be described
subsequently. Additionally, the number of samples in each block may be varied
from block
to block across the extent in k-space of the acquisition, this variation in
number of samples
to be combined being determined by the requirement for sufficient correlation
to maintain
SNR sufficient to provide a robust measurement. The approximate noise level
can be
determined independently by several methods well known to the industry
including
measuring noise in the absence of signal input.
[PARA 1741 Acquiring data with different magnitude gradients within one echo,
TR, or
scan may be accomplished with the successive gradient heights being selected
to enable best
SNR of the combined signal at the various targeted regions of k-space. To
enable
combination of sequential samples from the ADC to improve SNR, correlation
among
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successive samples can be increased by proper choice of windowing in profile
space, a
shorter window driving a greater correlation distance across sequential values
in k-space
and a longer window resulting in lower sample to sample correlation. The
window width
selected is defined by both the desire for correlation across many samples in
k-space, which
dictates a shorter window, and the need to sample a sufficient extent of
texture in real space
to provide robust measure, especially when measuring highly amorphous
textures.
[PARA 1751 Post-acquisition combination of the signals acquired in k-space in
blocks, the
number of samples to be combined determined by the requirement that the
correlation
between the individual signals to be combined be sufficient to achieve a SNR?
OdB (the
level of correlation is determined by subject motion, gradient height,
sampling rate,
window shape, and the underlying texture.)
[PARA 1761 Use of non-zero gradient acquisition may be employed to
intentionally vary
the direction and magnitude of the k-vector over a range during data
acquisition, for the
purpose of smoothing signal speckle¨which will manifest as a time varying
signal during
the data acquisition¨that results from interference of the varying phases and
amplitudes of
the individual spin signals. The selected variation in k-value direction and
magnitude during
data acquisition is chosen to provide sufficient combined measures to get a an
estimation of
the representative power within a neighborhood of k-space, with a SNR of OdB,
where the
neighborhood is within 20% of the 3D orientation and magnitude of the centroid
of the
neighborhood.
[PARA 1771 Correction for change in k across a set neighborhood created by
application
of a non-zero gradient may be accomplished by employing proscribed k encodes
for a
specific set of k measurement. Additionally, correlation within a set of k
measurements
acquired within a time period and from a selected VOI can be induced by
selecting the time
period such that the biological motion is sufficiently small that the phase
shift in the data
induced by patient motion is less than 50% of the wavelength corresponding to
the targeted
textural k-value range. Alternatively, a windowing function may be selected
such that there
is sufficient correlation between individual measurements and the set estimate
that a desired
SNR can be achieved.
[PARA 1781 Details of the very-low SNR acquisition mode at even further
expanded scale
are shown in FIG. 13. In this portion of the sequence, the k-value is constant
at an initial
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value 1114a, a second value 1114b induced by k value selection gradient pulse
1100a, a
third value 1114c induced by k value selection gradient pulse 1100b and a
fourth value
1114d induced by k value selection gradient pulse 1100c in the region 1116
where the
sample gate is open thereby producing samples 1118. This is the previously
described pulse
sequence where multiple repeats of signal at the same k value are rapidly
sampled, all of
which are then combined into one estimate.
[PARA 1791 Details of the low SNR acquisition at the further expanded scale
are shown
in FIG. 14. In this portion of the sequence, notice that the k-values do
change as shown by
trace segment 1120, albeit slowly, due to the non-zero time-dependent phase-
encode
gradient 1106 present during the recording of the region 1122 when the sample
gate is open.
However, the range of samples1124 across k-space is a relatively compact
neighborhood
where the values are highly correlated due to the windowing function.
[PARA 1801 Details of the high SNR acquisition at the further expanded scale
are shown
in FIG. 15. In this portion of the sequence, the k-values again change as
shown by trace
segment 1126, due to the non-zero time-dependent phase-encode gradient 1112
present
during the opening of the sample gate in region 1128. The range of samples
1130 across K-
space is still a relatively compact neighborhood, but outside the inter-sample
correlation
imposed by the window function.
[PARA 1811 The low SNR and high SNR acquisition modes with non-zero gradient
are
distinct from a standard frequency-encoded MRI sequence as the applied
gradient is not
used to establish a position, i.e. frequency encoding, but as a time dependent
phase encode
to rapidly acquire a number of individual samples across a relatively broader
neighborhood
of k-space.
[PARA 1821 As previously asserted, gradient acquisition can be acquired in one
echo
rather than in multiple echoes as seen FIG. 16. Again the illustrated pulse
sequence for
selecting the desired VOI and initial phase wrap for k is as described in FIG.
3 and is
numbered consistently in FIG.16.
[PARA 1831 As seen in FIG. 16 and at larger scale in FIG. 17, the k-value is
constant at
an initial value 1614a, a second value 1614b induced by k value selection
gradient pulse
1600a and a third value 1614c induced by k value selection gradient pulse
1600b. Note that
the k values are decremented as opposed to incremented in the example of FIG.
12. This is
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again the previously described pulse sequence where multiple repeats of signal
at the same
K value are rapidly sampled 1622a, 1622b and 1622c, all of which are then
combined into
one estimate.
[PARA 1841 In a second portion of the sequence, within the same echo, the k-
values do
change as shown by trace segment 1620, albeit slowly, due to the non-zero time-
dependent
phase-encode gradient 1606 present during sampling. However, the range of
samples1624
across k-space is a relatively compact neighborhood where the values are
highly correlated.
[PARA 1851 In a third portion of the sequence, again still within the same
echo, high
SNR acquisition is conducted. The k-values again change as shown by trace
segment 1626,
due to the non-zero time-dependent phase-encode gradient 1608 present during
the opening
of the sample gate. The range of 1628 across k-space is still a relatively
compact
neighborhood but outside the inter-sample correlation imposed by the window
function.
[PARA 1861 As seen in FIG. 18 (and in larger scale in FIG. 19), where the
illustrated
pulse sequence for selecting the desired VOI and initial phase wrap for k is
as described in
FIG. 3 and is numbered consistently in FIG. 18, a low non-zero magnitude
gradient 1802
acting as a time dependent phase encode is applied and data samples 1804 are
taken from an
initial k-value 1806 for slowly time varying k-values, seen in trace segment
1808, having
high correlation as previously described. The initial phase wrap may be
selected to provide
an initial k-value with a magnitude corresponding to a low SNR region.
[PARA 1871 Similarly, as seen in FIG. 20 (and in larger scale in FIG. 21), a
pulse
sequence for selecting the desired VOI and initial phase wrap to set the k-
value region is
illustrated and is as described in FIG. 3 and is numbered consistently in FIG.
20. A higher
non-zero gradient 2002 acting as a time dependent phase encode is applied and
data samples
2004 are taken from an initial k-value 2006 for more rapidly time varying k-
values, seen in
trace segment 2008. The initial phase wrap may be selected to provide an
initial k-value
with a magnitude corresponding to a higher SNR region. The encoding gradient
326 may be
employed to wind up to the lowest or highest k-value in a targeted texture and
the non-zero
magnitude gradient pulse is imposed in the necessary direction (increasing or
decreasing k)
to reach the other limit in k-space to define the texture.
[PARA 1881 The acquired samples may be outside the inter-sample correlation
imposed
by the window function. However, signal levels for the k-values are relatively
large and
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have high SNR. Additionally as previously described, rapid acquisition of
samples in
subsets 2010a, 2010b, 2010c and 2010d as exemplary, may be accomplished in a
manner
that the samples within the subset may remain sufficiently correlated and may
provide
desired data in structures having predetermined or anticipated texture. One
can combine
however many sequential values are correlated enough to yield an improvement
in SNR via
the combination (averaging being one simple form of combining). Then, the set
of
combined data points is used to characterize the power distribution across the
entire
acquisition, to get a better measure of the underlying texture within the VOI.
[PARA 1891 As previously discussed, rephrasing separate low and high k values
based on
low phase change in a second 90-180-180 excitation (TR). SNR is maximized with
gradient
ON acquisition by smart combination of successive k-value samples through
reregistration
of successive acquired signals. Data is acquired across a range of k-space for
which the
wavelengths are sufficiently long that subject motion can be easily corrected
for by
reregistration¨i.e. the phase shift induced in the measure in this k range is
much less than
the textural wavelength. The low k-value signal is sampled in alternate
refocusing
sequences, or sequential excitations (TR), with the acquisition of the signal
from the higher
k-value range of interest. TE long wavelength measure is used to determine the
motion-
induced phase shift across the measurements. That phase shift is then applied
to the higher
k-data prior to reregistration.
[PARA 1901 A number of correlations are implied by spatial windowing.
[PARA 1911 If g(x) corresponds to a 1D (real-valued) signal, the corresponding
function
in K-space is given by the Fourier transform as:
G(27-ck) = f g(x)e -J2n-xk dx (1)
Which is frequently expressed as a Fourier pair as
g(x) <=> G (27-ck) (2)
[PARA 1921 Windowing is the process of limiting the extent of g(x) to a finite
region of
compact support, but doing it in such a way to minimize spectral artefacts due
to
discontinuities (artificially) introduced by the truncation.
[PARA 1931 Despite the specific shape used of the window function, there is an
inverse
relationship between the width of the window, and its spectrum. This is due to
the Fourier
relationship
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h(ax)
(2n-k
<=> ¨
lal a
[PARA 1941 Multiplying two functions has the effect of convolving their
respective
spectra, i.e.
f (x):= g (x)h(x) F (27-ck) = G (27-ck) * H (27-ck) (4)
[PARA 1951 The convolution can be thought of as a linear filtering of the
spectrum as
though the spectrum was the input signal
[PARA 1961 The term H (-2n-k) acts like a low-pass filter to the G (27-ck)
spectrum, which
a
tends to smooth out the signal: the larger the value of a, the narrower the
low-pass filter.
This creates a significant correlation between adjacent values of F (27-ck).
[PARA 1971 Estimators which observe noisy samples of a filtered input are well
studied
and can be applied to generate optimal estimates; Weiner filters, Kalman
filters, etc.
[PARA 1981 Dynamic acquisition modes may be employed wherein:
X corresponds to a 3D vector in image space,
g(X) corresponds to the value of the image at a given 3D spatial location,
K corresponds to the 3D vector,
G(K) corresponds to the value in k-space of the image g.
[PARA 1991 For initial simplicity, the time-dependency of this signal is
ignored which in
turn depends upon Ti, T2, T2*, as well as signal contribution due to differing
isochromats
(different chemical species within the Volume) etc. In the sequel the effect
of these is taken
into account
[PARA 2001 Basic Principles relied upon are:
Generally, SNR of G(K) is highest at Ik1=0, then decreases with increasing Ikl
The rate at which SNR decreases is typically expressed as SNR oclkr where a is
in the
range of 1-3.
The sampling rate, combined with the magnitude of the gradient will set the
sample spacing
(Ak) density in k-space for a given VOI.
As the gradient magnitude is decreased, the sample density increases (i.e. Ak
decreases).
Depending upon the size of the windowing in image space, there is a
corresponding
correlation implied.
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[PARA 2011 For a generalized case the simplified MRI relationship between
spatial
coordinates and K-space given by
S (k) = fll (r)e-32zkgrdr
(6)
Where
r represents the real valued 3-Dimensional spatial coordinates with units of
meters (m).
1(r) represents the image which is a non-negative Real function of spatial
coordinates r.
k represents the real valued 3-Dimensional k-space coordinates with units in
cycles/meter
(m-1)
S(k) represents the Fourier Transform of (r) and is generally a complex-valued
function
of k
And the integral is over the entire 3-Dimensional spatial plane.
[PARA 2021 In words, S(k)represents the corresponding value in 3-dimensional k-
space
of the image function/(r).
[PARA 2031 The k-space coordinates, in turn, are a function of time and have
the general
form
g(r)dr
-00 (7)
Where
t is the proton gyromagnetic ratio with value 42.576MHz/T
g(t) is a real-valued 3-Dimensional function of time representing the gradient
strength with
units in T/m. This function, is a design input as part of the pulse sequence
whose purpose is
to manipulate the proton spins in some desired way.
[PARA 2041 The integral in equation (7) indicates that the value of k(t) for a
given value
of t, is computed as the integral of all previous history of the gradient
function. While
technically correct, it is often more convenient to express this as
k(t)=-24 g(r[dr+k(to)
to (8)
[PARA 2051 Where now to represents a convenient starting time, k(to)is the
corresponding
k-value at to, and the lower limit of the integral starts at to.
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[PARA 2061 Making the dependence on time more explicit, equation (6) can be
expressed
as
S[t]= RI/ (r)e-'27`k(t)wdr t to
(9)
S (t)
represents the complex-valued baseband signal one might obtain during an MRI
echo
experiment which is played in conjunction with a gradient sequence encoded
ing(t).
[PARA 2071 Without loss of generality, k, g and r can be decomposed into
Cartesian
components as
k = pcõkykziT
g=[gxgy g ziT
r = [rx ry rz 1T
(10)
And express 9 as
SW=1 P(rx,ry,rie j2z(kx(orx+ky(ory-Fkz(orz) drxdrydr, t
(11)
[PARA 2081 In general k(t) represents a curvilinear path within K-space as a
function of
time.
[PARA 2091 Initially, to facilitate explaining the initial concept, evaluation
is confined
along a single dimension by assuming k(t) = [k(t) 0 01T. Equation (11) then
simplifies to
S(0= f P(rx)e¨J22rk-(t)r-drx t to
(12)
Where
00 00
p(i)= f/(rx,ry,Odrydr,
(13)
And equation (8) reduces to
Icx(t)=1LS g x(r)dr+kx(t0)
to (14)
Define
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R(k):=,9-{p(x)}(k)= f p(x)e-12'ladx
(15)
Which is occasionally expressed as
R(k)<=>p(x)
(16)
To concisely indicate that R(k) andp(x) are Fourier Transform pairs. By
comparing (12)
() .
to (15), it can be seen that S t is Just a time dependent progression across
various Fourier
coefficients represented by
S(t)=R(k(t))
(17)
Where the mapping between the time-value t and the corresponding K-space
coordinate is
given by equation (14).
[PARA 2101 To generally model the receive signal In an actual MRI machine, a
combination of the desired signal and noise received by the antenna. That
signal is then
filtered, amplified, down-converted, sampled, and quantized.
[PARA 2111 The specific details are machine-dependent, but a simple model can
be
developed to represent the output of the machine as follows:
Y(t)[PARA 2121 Let
represent a combination of the signal of interest, and a noise signal
as
Y(t)=S(t)+W(t)
(18)
Where
S (t) is given in equation (17) and
w(t) is a complex-valued zero-mean, Additive White Gaussian Noise Process with
2 E{W(t)} =0 EIW (t)W* (t r)) =o-w2o(r)
variance cr- , i.e. and
[PARA 2131 The received signal Y (t) is then uniformly sampled
Yn = (t)It=n At = R (IC (t))1
t=n At -EW
t=n At (19)
Which can be expressed more simply as
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(20)
Where the sequence Ic is given by
1(0 = k
t=0
(n+1)At
kn+i=kn lL f g(r)di-
nAt (21)
[PARA 2141 Define
(n+1) At
Ak n, = j=L g (2)di-
nAt (22)
Then Error! Reference source not found. (21) can be simply expressed as
1(0 = k
t=0
kn+1= kn+ Akn,
(23)
[PARA 2151 In words, then, the sequence k- is defined by a sequence of
increments which
is determined by the integral between samples of the gradient function.
[PARA 2161 Equations (20), (22) and (23)may be employed to describe the
signals under
different gradient conditions disclosed herein.
[PARA 2171 Collecting samples of an echo which has been "pre-phased" through
some
gradient activity before-hand, but now the gradient is no longer held to zero
as described
above with respect to FIGs. 12, 14 and 15 can be analyzed as follows.
[PARA 2181 The signal is then given by equation (20) as
(24)
k And is given by equation (21) as
ko = k
t=0
(n+1)At
kn+i=kn+1` f g(r)d'r
nAt (25)
[PARA 2191 Since, measurement is occurring in a non-zero gradient regime, the
integral
term is no longer zero, which implies that the sequence Ic is no longer
constant, and in turn
R[ki the sequence is no longer constant.
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[PARA 2201 Since no assumptions have been made on the underlying structure of
I(r)
it cannot be implied that there is any particular structure or relationship
amongst the values
R(k)
of . This puts us at a distinct disadvantage when wanting to estimate
useful signals
in a very low SNR environment.
R(k) by applying a
[PARA 2211 A structure may be imposed on the values of
multiplicative window function in the image domain. This is accomplished by
leveraging
two Fourier Transform identities:
Multiplication in one domain corresponds to convolution in the reciprocal
domain.
Define the following Fourier Pairs:
v(x)<=>N[k]
p(x)<=>R[k]
c(x)<=>Z[k]
(26)
Then, the product in one domain corresponds to convolution in the reciprocal
domain:
V[x]=p[x]c[x] <=>N[k]=R[k]*Z[k]
(27)
[PARA 2221 Scaling in one domain corresponds to an inverse scaling in the
reciprocal
domain.
c(x) <=>Z(k) then
x
¨ <=>lalZ (ct 1c)
(28)
[PARA 2231 Windowing functions are typically used to limit the image space to
a finite,
compact region of interest, while at the same time, minimizing the adverse
consequences on
the corresponding image spectrum due to the window itself. Those skilled in
the art will
appreciate there are a wide variety of window functions which have been
developed, each of
which have their own particular set of characteristics.
[PARA 2241 For sake of illustration, consider the most basic window function:
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1
0
rect(t)=II(t)= i
Id' i
1 Itl<1
2 (29)
The corresponding Fourier transform is given by
F{11(t)} = f II (t) e-12dt = sin (RI) = sine ( f )
RI
(30)
which is frequently expressed as the Fourier pair
II(t) <=>sinc(f)
(31)
Using equation (28) a slightly generalized version and its Fourier pair is
r \
H ¨t <=> VI sine (Tf )
\l' , (32)
Using equation Error! Reference source not found. (27), the windowed profile
and
Fourier pair is
(x
v(x)=H ¨ p(x)<=> N(k)=IXIsinc(Xk)*R(k)
(33)
Using equation (24) as a reference, the sampled MRI signal can be expressed as
Y,=N(k,)+W,
(3) (34)
Which, using (33), can be expanded as
Yõ = f IXIsinc(Xq)R(kn ¨q)dq+14in
-00 (35)
Where the convolution integral has been specifically expanded.
[PARA 2251 The value of the convolution integral taken at IC is no longer a
function of
k
just one point of R(k) . For each point - the convolution integral computes a
weighted
sum of the values of R(k) centered around k- The extent of the neighborhood in
k-space is
inversely proportional to the parameter x : Smaller values of x increase the
width of the
neighborhood in k-space.
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[PARA 2261 For the embodiments herein the extent of the domain of values of
interest
correspond to the collection of k-space values k0, k1, k 2,Lk N-I Define
kmm = mm
k ¨ max k
max
n n (36)
Which in turn are functions of the time interval At and the function g(r)
er)
[PARA 2271 For example, making a simplifying assumption that g = Gwhere G is a
positive constant, then Ic is just a uniform sampling across a portion of k-
space, and is
given by
k=k0+nGAt
(37)
Then kmin and kmax is given by
km,. =1(0
k. = ko+(N ¨1)GAt
(38)
[PARA 2281 While a simple sampling of k-space may be chosen, it is not
specifically
required. Indeed there could be applications where non-uniform and/or even non-
monotonic sampling strategies could be useful.
[PARA 2291 Ideally, the parameter X (and the window function) are chosen so
that the
resulting weighted sum across the neighborhood of k- is "wide enough" so that
N(10
where C is a complex-valued constant, but not so wide so as to lose
significant spectral
resolution
[PARA 2301 For purposes of the disclosed embodiments herein a "small" non-zero
gradient may be determined based on selection of desired windowing. From
equation
Error! Reference source not found. (10)
R(k):=,9-{p(x)}(k)= f p(x)e-12'ladx
(41)
[PARA 2311 Assume that the nominal center point of the profile has shifted to
be centered
around a point x0. This results in
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Rxo(k):= f p(x¨x0)e¨J27dadx= e¨ J2A-kvoR(k)
(42)
Which indicates that each point in k-space is rotated in complex space
proportional to the
offset xØ
[PARA 2321 It can be assumed that the gradient is a positive constant, then,
by equation
(39)
ko+nGAt
(43)
Substituting in Error! Reference source not found. (42) produces
Rx0(k,)=e-J6be-Jth R(kn)
(44)
0
Where the initial phase offset 0 and the phase increment A0 is given by
00 := ¨27-ck0x0
A 0 := ¨27-t-GAtx0
(45)
[PARA 2331 In the event that, due to the application of a properly specified
windowing
function, 1(1(n) Ca complex constant within the neighborhood, the post-
acquisition
inAG
estimator would first multiply an offsetting phase increment e to each
acquired sample
R (k)
of j'o before combining and generating the final estimate.
[PARA 2341 An estimate of A can be obtained from a sequence of k-space
samples
taken of the windowed profile over lower k-values (where the SNR is higher).
[PARA 2351 Correlation may be induced by windowing as one parameter as
discussed
previously. Multiplication of the profile p(x) by a real-valued window
function C(x)
corresponds to convolution in k-space by the Fourier relation
V(x)=p(x)c(x)<=>N(k)=R(k)*Z(k)
(46)
()
[PARA 2361 Z k is treated as an impulse response of a linear filter which is
applied to
()
the complex-valued signal R k in k-space to produce a complex-valued output
signal
N(k)
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[ 1, 2 PARA 237] The
autocorrelation function of the output signal RNN can be
,
RR 1 2
expressed as a function the autocorrelation of the input signal R ), and
the impulse
z(k)
response as
RNN (1C1,1(2) = f RRR - a, Ic2 - 18) Z(a) Z(18) d ad ig
-00-00 (47)
[PARA 2381 Equation (47) is inconvenient because the autocorrelation function
of the
underlying signal R
RR k, ) is not usually known. A simplifying assumption is made
that R(k) is a white-noise, wide-sense stationary process, and express the
autocorrelation
as
RRR(1c, 1(2) = 0-R2 5(1( 1 2 (48)
With this assumption, (47) reduces to
(49)
Where Rzz (k) is the autocorrelation function of the impulse response z(k) and
is given
by
Rzz[tc]:= f Z[k]Z(k+Odk
-00 (50)
[PARA 2391 The mapping of k vs time is mapped as follows.
k=ko+nGAt
(51)
The normalized correlation
(NGAt):=RNN (1(0,1(0 NGAt) = R (NGAt)
zz
RNN(1(0,1(0) Rzz (0) (52)
measures the degree to which the underlying sample points are correlated. In
low SNR
regimes, a high correlation is desired across all of the samples and therefore
establish a
lower bound:
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Rzz (NGAt)
iLin ________
Rzz (0)
(53)
[PARA 2401 Equations Error! Reference source not found. (50) and
Error! Reference source not found. (53) provide the defining relationship
between the
window function impulse response z(k) , the gradient strength G , the sample
interval
the number of samples N, and the correlation lower bound 1min.
[PARA 2411 For example, assume that the window function is defined to be
r s\
x
-
X1 (54)
Where is .
here is a standard so-called rectangular function defined below, and x is
a
constant.
1 0
1 Ixl<1
2 (55)
Z(k) [PARA 2421 The impulse function is given by the Fourier transform
Z(k)=XI sin( X/c)
__________________________ =IXIsinc(X1c)
7-t- Xk (56)
11(k) i [PARA 2431 The corresponding normalized correlation function s
given by
11(1C) = sinc ( XI()
(57)
[PARA 2441 Restricting the correlation to be lower bounded by 71min ¨ 0.95
then, by (53)
the condition arises that
qmin sinc (X =N=G= At)
(58)
This can be approximated using the first two terms of a Taylor series as
sin (6) i 02
____ ,-..--= 1--
0 3! (59)
Which can be inverted and applied to (58) to produce
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N = G = At V3!(1¨ rimn )
(60)
Which now explicitly expresses an upper bound on the product of the gradient
strength G,
the sample interval A t , and the number of samples N .
[PARA 2451 Typically the sample interval At ,and the number of samples N are
determined by other considerations. Taking these as given, the maximum
gradient level is
then given by
G< __________
ic X = N = At (61)
[PARA 2461 For a non-zero gradient data acquisition in this case, as long as
the gradient
G is below the calculated upper bound, the samples acquired will have the
defined
correlation level. This condition as defined for purposes herein as a "small
gradient" level.
[PARA 2471 Sampling past this limitation will result in lower sample
correlation and
therefore have less of a potential post acquisition SNR gain. A "higher"
gradient may be
defined as operating in this condition. Gradient determination is affected by
a number of
parameters including (1) choice of the window function (e.g. rectangular,
Tukey,
Hamming, etc.) which influences the shape (and to a certain extent, the width)
of the "main
lobe" in the impulse response, (2) choice of window extent (the larger the
extent in the
profile domain, the narrower the "main lobe" in the impulse response), (3) the
impulse
response which may create an autocorrelation function, (4) the desired level
of correlation
which determines the effective width in k-space, within which the samples must
be
contained, and (5) sampling rate*Number of samples*gradient size which
determines the
actual sampling neighborhood size (note, as long as this number is bounded by
the number
contained in element (4)) the gradient remains in the "lower gradient level"
regime.
[PARA 2481 An exemplary embodiment maintains a constant ratio of textural
wavelength
to length of VOI acquisition axis. As the targeted k-value varies, the length
of the VOI
acquisition axis is varied such that the ratio of the corresponding textural
wavelength to the
acquisition length remains constant. The aim here is to keep the number of
textural "cells"
sampled constant. In this way, the differential broadening observed at
specific points in k-
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space, Ak, is expected to arise from sources other than sampled length in real
space, such as
the finite width of the RF pulse or the edges of the gradient pulse.
[PARA 2491 MR-based diagnostic techniques may be combined. Certain MR-based
techniques designed to look at very fine tissue structure provide data that
may be difficult to
interpret in certain pathologies, as they provide only an indirect measure of
the underlying
structures. Diffusion weighted imaging and Magnetic Resonance Elastography
(MRE) are
two such techniques. The method of this provisional filing is a direct measure
and hence
would provide, in many cases, a better measure of fine texture, and in some
cases provides
complementary data to increase diagnostic capability. Combining acquisition
techniques
can provide more robust measure of texture, and hence of pathology.
[PARA 2501 The embodiments disclosed may be used in combination with Magnetic
Resonance Elastography (MRE). Currently, the main application of MRE is as a
diagnostic
for liver disease to determine therapy response, progression, need for biopsy,
etc. Though
the targeted pathology is fibrotic development, the technique measures this
indirectly,
through measurement of tissue stiffness. In many cases, it is difficult to
distinguish fibrotic
development from other stiffness-inducing conditions such as portal
hypertension and
inflammation. Further, hepatic iron overload, which often results from a
compromised liver,
will lead to low signal, hence inadequate visualization of the induced
mechanical waves.
[PARA 2511 The embodiments disclosed can provide direct measure of fibrotic
development in the liver and, as such, would provide additional data on
disease progression
or response to therapy in the case of the various triggers of fibrotic liver
disease. It provides
a local measure within the targeted anatomy for calibration of other, indirect
measures, such
as MRE, DTI, DWI, etc.
[PARA 2521 The embodiments disclosed may be used in combination with, or
replacement for, diffusion weighted imaging in tumors. The ability to detect
the edge of
tumors with high accuracy would facilitate accurate surgical removal. Using
the
embodiments disclosed, data can be acquired in VOIs along a selected direction
across a
tumor region, looking for the edge of the region of angiogenic vasculature.
[PARA 2531 The ability to measure inside of tumors to gauge therapy response
would
help in targeting intervention. As an example of the latter, immunotherapy
treatment of
melanoma tumors induces swelling of the tumor due to T-cell infiltration
which, on a
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structural MR scan, looks similar to malignant tumor growth. Hence, it is
difficult to decide
whether to continue the therapy. The ability to look at the state of the
vasculature within and
surrounding the tumor would enable discernment of whether the growth is
cancerous or is
due to immune system response. The embodiments disclosed would also provide
local
calibration of the currently used DTI measures, which are often difficult to
interpret.
[PARA 2541 The embodiments disclosed may be used as a bone degradation measure
in
oncology. It is well known that radiation and/or chemotherapy often compromise
bone
health. A measure of changes to bone resulting from cancer therapy would help
in tailoring
therapy and to determine if there is need for intervention to protect bone
health.
[PARA 2551 Currently, as a follow-on to surgery and treatment for breast
cancer, patients
are routinely put in the MR scanner to image the breast tissue. The sternum is
within the
field of view for such exams, enabling easy application of a short add-on
sequence of this
method to measure changes to trabecular bone and thus obtain a measure of bone
health.
[PARA 2561 As further examples of potential use of the embodiments disclosed
in
oncology, the embodiments disclosed may be used to measure and quantify
hyperplasiac
development of mammary duct growth in response to tumor formation and
development or
to measure and quantify angiogenic growth of vasculature surrounding tumors to
stage
development, type, and response to therapy. Ongoing treatment after breast
surgery often
involves reducing estrogen levels, further compromising bone health and, as
such, referral
for MR scans for bone monitoring is common; use of the method disclosed herein
would
enable robust and detailed evaluation of bone health by direct measurement of
the trabecular
bone structure.
[PARA 2571 The disclosed embodiments are also complementary with Big Data and
machine learning schemes. The method disclosed complements the trend towards
use of
comparison among large aggregates of medical data to learn more about disease,
increase
predictive power for individual patients and for specific diseases, and note
trends across
various populations. Benefits of using the method of this filing in
conjunction with Big
data/ machine learning include:
Output measurements from application of the embodiments disclosed can be
compared over
a population of unknown pathology, for example, the variation in the power
spectrum across
targeted k-values, could be compared to the occurrence of femur fracture in
the same
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population; the textural power distribution in cortical neuron bundles can be
measured and
correlated with performance on Alzheimer's mini-mental state exams, MR imaging
of brain
atrophy, or other assessment of AD or other cognitive pathology; the texture
power
distribution vs. k-value at various locations in liver can be compared with
other inferences
of liver disease, such as biopsy, physical exam, blood test, MR imaging, or
MRE, over a
huge population;
use of machine learning over large populations enables determination of
specific biomarkers
in pathology;
the ability to make useful correlations using big data and machine learning
gets much better
with high SNR measure input such as that provided by the method of the
embodiments
disclosed;
use of machine learning can indicate, for example, if a disease is defined by
appearance of a
strong signal at a specific k-value appearing in the diseased tissue.
[PARA 2581 In advance of the macroscopic pathology attendant with disease
development, pathological changes occur near the cellular level in affected
tissue. For
instance, in bone diseases, fracture is often the downstream effect of ongoing
progressive
thinning of the trabecular elements. In soft tissue diseases, such as liver
disease, fibrotic
structures develop over a long time in the affected organ, leading eventually
to cirrhosis.
And in neurology, tissue textures in the brain, in both white and grey matter,
change in
response to disease onset and progression. The ability to measure the early-
stage changes in
disease, those affecting fine tissue textures, will enable early stage
diagnosis, thus enabling
earlier treatment, subject targeting for trial inclusion, and sensitive
monitoring of therapy
response.
[PARA 2591 The embodiments herein enable this direct and sensitive measure of
disease,
through their ability to provide clinically robust measure of the pathologic
changes in tissue
textures attendant with disease onset and with early-stage progression,
providing the needed
diagnostic capability.
[PARA 2601 One of the most valuable features of the disclosed method is that
it can be
used in conjunction with most contrast methods applied in MRI. As the method
results in a
texture measurement, as opposed to an image, it needs only to have contrast
between the
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tissue textural elements. This contrast can be generated in many ways,
selected to optimize
tissue contrast in specific pathologies. The tissue texture measurement yields
high spatial
resolution due to its high immunity to subject motion. As the acquisition time
for the
methods previously disclosed herein is short, the textural data can be
acquired interspersed
with image acquisition in various sequences.
[PARA 2611 For instance, in bone, for which there is effectively no signal
from the
trabecular bone elements themselves, a standard Ti pulse sequence, which
yields high
signal from fat, provides the requisite high contrast between bone and marrow.
Thus, the
texture measurement employing the methods herein can yield high sensitivity in
bone when
applied with Ti contrast. T2 contrast can be used to highlight fluid towards
determining if a
bone lesion is lytic or sclerotic, as there may be little fat remaining around
the calcified
bone to provide signal. T2 weighted imaging has a host of applications,
including
abdominal lesion imaging, imaging of iron deposition in the brain, and cardiac
imaging,
hence use in conjunction with the methods previously disclosed herein enables
highlighting
of tissue texture in these organs/pathologies.
[PARA 2621 MRI contrast generation has become increasingly sophisticated over
time. In
addition to exogenous contrast agents, such as gadolinium, there are the
standard Ti, T2,
T2*, proton density contrast, and Inversion Recovery sequences. Several
techniques can be
used for fat suppression in imaging. Many new contrast techniques, often
dependent on
functional contrast, have been developed to highlight different tissues
involved in
pathology. MR angiography, a method of visualizing vasculature and blood flow,
makes use
of MR signal saturation, or induced phase contrast in flowing blood, to assess
vascular
density and permeability. BOLD (Blood Oxygenation Level Dependent) contrast
uses
metabolic changes in blood to image active brain regions. Diffusion weighting,
both DWI
(Diffusion Weighted Imaging) and DTI (Diffusion Tensor Imaging), is used to
assess
pathology in an increasing range of diseases, providing a signal reflective of
the
microscopic state of the targeted tissue. ASL (Arterial Spin Labelling),
traces the diffusion
of magnetically-labelled blood (endogenous contrast) through the brain to
assess pathology;
perfusion imaging is used to assess blood microcirculation in capillaries,
another measure of
functional response. In both these contrast schemes, the time-course of blood
flow, is
followed to assess the state of the vasculature near a tumor, as this is a key
feature in the
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diagnosis of gliomas and other tumors. Blood vessels are present in higher
numbers within,
and surrounding tumors than in normal brain tissue, and they tend to have a
larger blood
volume. Higher-grade tumors also tend to have higher blood volume, and the
degradation
and remodeling of extracellular matrix macromolecules results in loss of blood-
brain barrier
integrity, which is seen as contrast leakage. These measures can capture the
degree of tumor
angiogenesis, an important biologic marker of tumor grade and prognosis,
particularly in
gliomas. Application of the methods disclosed herein near the peak of the
signal contrast
would provide a direct measure of the density and size of blood vessels
providing direct
measure of the fine-scale vasculature texture as correlational data robustly
measuring the
degree of angiogenesis within or in the vicinity of the tumor. Combined in
this way, a
robust measure of pathologic vasculature development for staging
neuropathology such as
stroke and tumor can be made.
[PARA 2631 As an example, diffusion weighting in its simplest form, DWI, uses
the
random Brownian motion of water molecules to generate contrast in an MR image.
The
correlation between pathology (histology) and diffusion is complex but,
generally, densely
cellular tissues exhibit lower diffusion coefficients. Obstacles such as
macromolecules,
fibers, and membranes also affect water diffusion in tissue. Water molecule
diffusion
patterns can therefore reveal microscopic details about tissue state. By
measuring the
differential rate of water diffusion across a region of tissue, a map of
diffusion rates,
reflecting local pathology, can be produced. Diffusion weighting is
particularly useful in
tumor characterization, vasculature typing, and diagnosing/monitoring cerebral
ischemia,
among other pathologies. Ischemic infarcts within the brain, abscesses, and
certain tumors
result in highly restricted diffusion; cysts and edema offer little
restriction to diffusion.
[PARA 2641 Diffusion imaging presents several problems with data
interpretation, the
most salient of which are: 1) the long diffusion gradients increase the echo
time, TE,
reducing SNR, 2) the high diffusion gradients required result in eddy currents
in metal
surfaces in the scanner, which cause signal distortion, 3) the low signal
amplitude
necessitates use of a relatively large voxel, on the order of 2.5mm on a side,
hence low
resolution, 4) the sequence, by design, is highly sensitive to motion, so data
recording must
be very fast; hence, the ability to increase SNR by averaging signal from
multiple
acquisitions is limited. Additionally, as at least six different directions
are needed to
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determine fractional anisotropy (FA) the motion sensitivity can lead to
difficulty in data
interpretation across the course of the acquisition. 5) Interpretation of
diffusion weighted
signals is not straightforward. The measured diffusion coefficients can arise
from many
sources, as the exact mechanisms governing water diffusion processes in
tissues, especially
in the brain, are not clearly understood. What is inferred from the
measurement regarding
the barriers and restrictions to free diffusion is based on certain
assumptions about the
underlying tissue pathology. This can take many forms, involving cell
membranes,
organelles, cell spacing, axon density, glial density, myelin state, etc. 6)
Each DWI voxel
represents an average, the standard voxel size being on the order of 2 to
2.5mm on a side. In
order to interpret changes in the Average Diffusion Coefficient (ADC) within
the voxel,
certain assumptions are made, such as tissue homogeneity and type of structure
causing the
diffusion variation.
[PARA 2651 The methods disclosed herein entail acquisition within either a
single VOI or
within multiple, interleaved VOIs, within one TR. Data is acquired without use
of a spatial
encoding gradient to form an image. This significantly shortens the
acquisition time and,
combined with the narrowly targeted acquisition in k-space, enables
acquisition of the
requisite data fast enough to provide immunity to subject motion. Though
multiple
measures of single volume acquisition can be mapped across the anatomy under
study, each
measure is acquired rapidly within a single volume. High SNR is assured by
this single
volume technique as, before motion effects set in, there is time for repeat
measure of each
targeted k-value; the VOI moves along with subject motion when acquiring data
across a
single TR. The number of repeats and number of/range of k-values for which
data is
acquired is limited by the requirement to keep the acquisition fast enough to
provide the
requisite motion immunity.
[PARA 2661 The high motion sensitivity of standard DWI, the fact that it is an
indirect, or
inferred, measure, and its low SNR combine to make it not as robust a measure
as would be
desired in a clinical setting. Use of the embodiments disclosed herein for
data acquisition
when using diffusion contrast can mitigate the motion problem as, though the
echo time is
still long, data acquisition is fast enough that the motion blurring of the
signal is minimized.
Further, additional data can be acquired by the disclosed methods during the
scan, relying
on contrast such as Ti or T2 weighting. The standard DWI images, diffusion
weighted
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acquisition by the methods disclosed herein, and data acquired by those
methods using
contrast, can all be input into a machine learning algorithm, to determine
correlation
between the measures, and to correlate all three measures against pathology
data and
outcomes. Though correlation with outcome data takes time, it will provide the
best
assessment of the capabilities of the embodiments disclosed. Two possible ways
to combine
the embodiments disclosed with contrast mechanisms, such as diffusion
weighting, which,
in addition to changing the timing of the elements of a pulse sequence, add
extra RF and/or
gradient pulses: 1) acquisition of, for example, a diffusion weighted
intensity in a single
voxel, or in a region mapped by voxels which region is then overlaid by the
textural
measures obtained using the embodiments disclosed with any other standard
contrast
weighting, or 2) use the novel contrast mechanism, such as diffusion
weighting, to provide
contrast for acquisition of the textural measures using the embodiments
disclosed. This
acquisition can be done in a single VOI or, again, in VOIs across a region of
tissue.
[PARA 2671 Due to the long echo time and sensitivity to patient motion, most
diffusion
weighting is done using fast Echo-Planar Imaging pulse sequences to provide
relatively fast
data acquisition. However, chemical shift artifact is highlighted by single
shot EPI (around
pixels of shift).
[PARA 2681 Further, as there is not much motion of water through fat,
resulting in a
bright DWI signal that can obscure lesions, fat suppression is often used as
part of the DWI
data acquisition.
[PARA 2691 In one embodiment of the methods disclosed herein described in
greater
detail subsequently, crusher gradients are used on either side of the 1800
gradients to
eliminate focusing of noise signal generated during the 180 pulse. Replacing
these crusher
gradients with diffusion weighting gradients, allows acquisition of both the
diffusion
weighted signal as well as the subsequent restricted k-value signals. As such,
the diffusion
weighting would be a measure in the VOI.
[PARA 2701 DTI (Diffusion Tensor Imaging)¨in highly oriented tissues, such as
nerves
and white matter tracts, diffusion occurs preferentially along one direction,
diffusion along
the nerve/axon tracts being much preferred to that of the across-track
orientation. The
degree of directionality, or anisotropy, in tissue is an indicator of
pathology, as many
neurologic conditions degrade the order of neurologic structures, such as the
minicolumns
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ordering of cortical neurons, or lead to order degradation through
demyelination of the
axons that form the white matter tracts in the brain. In anisotropic
diffusion, the value of the
diffusion constant varies with direction. As this anisotropy is a measure of
pathology
advancement, measurement of the diffusion constant in multiple directions can
be used to
yield the "fractional anisotropy" arising from the tissue structures and hence
provide a
measure of pathology advancement. In practice, at least six non-collinear
gradients are used
to measure fractional anisotropy, leading to a symmetric 9 x 9 matrix, the
"diffusion
tensor", the eigenvalues of which yield the major diffusion axes in the 3
orthogonal
directions.
[PARA 2711 Along with using the anisotropy of diffusion to diagnose and
monitor
pathology in the brain, the diffusion tensor mapped across the brain can be
used to delineate
the path of white matter tracts. This is called tractography. A possible
application of the
methods disclosed herein is to measure texture in the white matter tracts
affected in multiple
sclerosis (MS) using standard Ti or T2 contrast or using the embodiments
disclosed in
conjunction with diffusion weighting to determine anisotropy of the measure
for
correlational input for machine learning with standard DTI acquisitions.
[PARA 2721 The methods disclosed herein provide the ability to obtain tissue
texture
using contrast that may be applicable to the particular tissue form being
examined. A
contrast is applied using any one of the previously described mechanisms
enhancing the
contrast between the component tissue types in a multiphase biologic sample
being
measured. As described subsequently in greater detail, the contrast mechanism
and its
application may occur at various locations within the NMR inducing pulse
sequence. Using
pulse sequencing such as that described with respect to FIGs.3 and 8 a volume
of interest
(VOI) is selectively excited employing a plurality of time varying radio
frequency signals
and applied gradients. An encoding gradient pulse, as also previously
described with respect
to FIGs. 3 and 8, is applied to induce phase wrap to create a spatial encode
for a specific k-
value and orientation, the specific k-value determined based on anticipated
texture of the
tissue within the VOI. A time varying series of acquisition gradients is
initiated to produce a
time varying trajectory through 3D k-space of k-value encodes as previously
described with
respect to, for example, FIGs. 8, 11, 16 or 18, with the k-value set being a
subset of that
required to produce an image of the VOI. Multiple sequential samples of the
NMR RF
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signal encoded with the specific k-values are simultaneously recorded. The
recorded NMR
signal samples are then post-processed to produce a data set of signal vs k-
values for the k-
values in the set determined by the trajectory.
[PARA 2731 A first tissue for application of the methods herein is bone.
Though the effect
on quality of life is huge, no accurate and non-invasive method for sensitive
assessment of
bone fracture likelihood exists. The current gold standard measure, DEXA,
which relies on
x-ray absorption, measures areal density of bone. The main determinant of bone
strength
which predicts fracture, is trabecular microarchitecture, a measure of which
is not currently
available in vivo. The embodiments disclosed enables this measure.
[PARA 2741 Bone degradation occurs due to several factors, including disease,
cancer
therapy, eating disorders, and aging/life style. As trabecular structure
erodes in bone, three
main morphometric figures vary¨the trabecular element thickness, TbTh, the
repeat
spacing of trabecular cells, TbSp, and TbN, the trabecular number, which is a
redundant
figure that can be determined from TbSp and TbTh. TbTh decreases continuously
with bone
degradation. Eventually, as the trabecular elements, or struts, break, there
is a discontinuity
in the local value of TbSp. Bone degrades anisotropically over time, the
anisotropy driven
in large part by the effect of load bearing stresses. With progression of bone
disease, the
TbSp increases faster along the primary load bearing direction than it does
normal to this
direction, the variation between the two measures being a marker for bone
degradation. In
addition to the developing anisotropy of bone morphometry, variability in the
measure of
the trabecular spacing, TbSp, increases, due to the thinning of the trabecular
struts to the
point where they break, causing discontinuities in the measure of TbSp.
[PARA 2751 The most definitive measure of bone health is the thickness of the
trabecular
elements, which is currently impossible to measure directly in vivo, due to
the spatial
resolution required of the measure.
[PARA 2761 The embodiments disclosed enable this measure, as it provides the
needed
resolution for measuring TbTh in thinning trabeculae, down to tens of microns,
near the
range where the sudden discontinuities in TbSp can then be measured to assess
further
degradation. As the trabeculae thin to the point of breaking, a sudden shift
to increasing
TbSp should be visible in the signal distribution vs. k-value, due to the
breaking trabecular
elements. Additionally, the degree of anisotropy of TbSp can be used as a
correlative
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measure for fracture likelihood Data can be acquired using the embodiments
disclosed by
positioning the VOI within the targeted bone region, acquiring data within a
single TR or
across multiple TRs to enable acquisition across the pertinent range of k-
values spanned by
TbTh and TbSp. Data acquired quickly within one TR can be averaged to improve
SNR, the
only requirement being that the data is acquired from similar bone tissue.
[PARA 2771 The requisite contrast is between bone and marrow. Ti weighting
provides
high signal in marrow, bone offering negligible MR signal. Alternatively, an
IR sequence,
which results in heightened Ti contrast, can be used. Some work has been done
using
diffusion weighting to image bone¨this is a possible contrast mechanism when
using the
embodiments disclosed.
[PARA 2781 In healthy bone, TbSp and TbTh are closer in magnitude than they
are in
diseased bone. This can be seen in FIGs. 22A and 22B, by comparing the image
of healthy
bone, FIG. 22A, with the image of highly osteoporotic bone, FIG. 22B. The
exact form of
this relationship varies somewhat, the difference in these two morphometric
parameters
being higher in spine, for instance, than in the hip across pathology. The
increasing
difference in measure of these two morphometric parameters provides a marker
of disease
development.
[PARA 2791 Because of this increasing separation of the two measures, to
measure both
TbSp and TbTh in osteoporotic bone involves acquiring signal data in two
spatially separate
regions of k-space. In healthy bone, if gradients are used to select a region
in k-space, it
may be possible to define a region that encompasses both TbTh and TbSp in the
distribution
of signal vs. k- value in some skeletal regions. With progressing bone disease
the variation
in the measured values of TbSp becomes wider, as does the percentage variation
of TbTh;
additionally, TbSp becomes larger (wider spacing) with disease progression,
and TbTh
comes narrower. Hence, the center of each of these distributions separates
progressively
with increasing pathology. Using either gradient on or gradient off
acquisition, broadened
by gradient height or VOI windowing, or a combination of the two, the general
shape of
these distributions can be determined. These broadened distributions can be
used in real
time to determine the regions of k-space to sample more finely in successive
TRs.
[PARA 2801 As desired, data can be acquired using multiple interleaved VOIs
within each
TR. This method allows determination of the variability in signal at specific
k-values within
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a region of bone. The data acquired from the different VOIs can be mapped, a
value/color/icon assigned to signal or peak k-value in the distribution, or to
the difference
between the k-values associated with, respectively, the peak position of TbTh
and that of
TbSp. The specific k-value and k-value set established by the time varying
gradients may
encompass a range of TbSp and TbTh from 0.01 mm to 5mm in exemplary
applications.
[PARA 2811 The methods disclosed herein can be applied within and around the
location
of a bone lesion identified with, for example, conventional Ti, T2 or proton
density
contrast, or flow or diffusion contrast MR imaging, to assess the state of the
trabecular bone
in the region. What is of interest here is a determination of the lesion type;
is the lesion
indicative of an erosive tumor, or is the lesion in a region of inflammation/
degraded bone
surrounding a fracture. Some lesions are dangerous and erosive tumors, some
lesions are
benign. Acquiring data by the embodiments disclosed in multiple VOIs in the
area of the
lesion would enable a determination of whether the trabecular structure is
degraded in the
vicinity, and how progressed the degradation is, both spatially and
temporally. Further
biomarkers can be derived by inputting the MR images of lesions to machine
learning
algorithms and correlating them with the trabecular data of TbTh, TbSp, TbN,
anisotropy
and measure variability. By this novel method, the diagnostic content of MR
images can be
improved, as the appearance of the lesion on the image can be tied to a
specific degree of
bone pathology.
[PARA 2821 T2 contrast can be used in conjunction with the embodiments
disclosed to
highlight fluid in an oncological bone lesion to type it as either lytic or
sclerotic. In such
pathology there may be little fat/marrow remaining around the calcified bone
to provide
signal. In an oncologic bone lesion there is usually a mix of fluid, and of
marrow in various
states of inflammation. To yield a signal from outside the hard bone, proton
density can be
used. Alternatively, diffusion weighting would return signal based on
diffusion of water
molecules in the fluid imbued marrow phase.
[PARA 2831 Use of the embodiments disclosed to acquire signal vs. k-value data
in bone,
applying the various methods disclosed above, yields several biomarkers for
assessing bone
health. Measurement of TbSp, TbTh, and TbN in multiple VOIs at different
locations in the
bone, and with different orientations of the textural encode gradient, yields
information on
the magnitude and variation of the morphometric parameters, their variation
with direction
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relative to the load bearing axis of the bone, and the variability in the
measures locally and
across larger bone regions. While, for instance, a measure of TbTh in one
orientation locally
in advanced disease will clearly reflect pathology, a more sensitive marker
for bone health
can be derived by combining the sum of the data acquired.
WARittaMEHowever, correlational data is required for feature/biomarker
development or
extraction. The highest content predictor of fracture likelihood is fracture
history. Bone
biopsy is also very sensitive but, as it is a highly invasive biomarker, this
procedure is rare.
Though DEXA is the current gold standard for bone health, it measures areal
bone density,
and is not sensitive to trabecular architecture; hence, it is at best a
mediocre predictor of
fracture likelihood. However, with a large enough sample, this metric can
provide increased
correlation for diagnostic definition. Taken together, DEXA data and patient
fracture history
provide a high-level learning framework for correlation with the entirety of
the output data
derived by the embodiments disclosed, enabling definition of a sensitive
diagnostic tool
from the embodiments disclosed.
[PARA 2851 Rather than try to derive biomarkers from the acquired data by
individual
comparison to look for feature extraction, machine learning algorithms will
provide the best
correlational data between multiple measurements and, when used in an
unsupervised mode,
can be compared to extract features from the data for biomarker development.
[PARA 2861 Further, besides correlation between TbTh, anisotropy data, and
measure of
TbSp and its variance, fracture data from subjects will be correlated with
these measures,
also using machine learning algorithms, to develop markers for fracture
likelihood.
Biomarkers can be derived directly from the signal vs. k-value data by
inputting it into
machine learning algorithms and correlating it with, for instance, fracture
occurrence data
from the same patient, DEXA measurements, or bone biopsy report data.
[PARA 2871 Liver tissue or other tissues subject to fibrotic invasion provide
a second
example of the use the disclosed embodiments. Although the underlying causes
of liver
disease are complex and varied, the salient feature of the disease is the
development of
fibrotic depositions within the liver. Disease onset and progression are
marked by increasing
accumulation of proteinaceous deposits, mainly collagen fibers, on the hepatic
structures.
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Though fibrotic development can, in the short term, promote healing, if the
disease is left
untreated, the healing response itself becomes a problem, the excess of
proteinaceous
substance impeding the normal functioning of the organ. In the case of liver
disease, this
process, left unchecked, advances to cirrhosis¨with attendant carcinoma and/or
liver
failure. For this reason, diagnosing liver disease early on, when a range of
management
options are available, is optimal. Use of the embodiments disclosed to assess
disease-
induced pathologic changes to liver tissue would provide a low- cost, non-
invasive, fast
add-on to a routine MR exam that would be ordered to check liver health. While
the focus
here is on liver fibrosis, the pathology, and the application of the
embodiments disclosed, is
similar to that of a range of diseases characterized by fibrotic invasion. A
partial list of these
diseases¨cardiac fibrosis, cystic fibrosis, idiopathic pulmonary fibrosis,
pancreatitis,
kidney disease. Additionally, pathologies such as prostate disease, lose
proteinaceous
deposits in response to disease progression. Though the mechanism is reversed,
the tissue
texture assessment needed for diagnosis and monitoring is the same.
[PARA 2881 Though biopsy is the current gold standard for liver disease
diagnosis,
sampling errors within an organ, significant read errors, and non-negligible
morbidity, and
even mortality, make this other than an optimal diagnostic. For sufficient
statistics, many
samples would be required, given the high spatial variability of fibrotic
development within
the liver; however, only a small number of samples can be taken due to the
highly invasive
nature of the biopsy technique.
[PARA 2891 Though what is needed is an accurate assessment of the advancement
of
fibrosis, currently nothing exists that can measure this directly, aside from
pathology. By
the time liver disease is diagnosable with liver function tests, ultrasound,
or MR imaging, it
is well advanced. What is needed is a diagnostic that can track the
development of the
disease in the early stages, when it is still reversible. Application of the
embodiments
disclosed to measurement of fibrotic structure offers a direct and non-
invasive measure,
enabling multi-sample, longitudinal monitoring of disease onset, progression,
and response
to therapy.
[PARA 2901 In liver, as in other fibrotic diseases, collagen accumulates in
specific
patterns within the organ, "decorating" the underlying structural elements.
Liver tissue is
composed of a multiplicity of adjoining units, or "lobules", the structure of
which is
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delineated by a central vein, and portal veins that form in a hexagonal
pattern around it (see
diagram below). As such, in the healthy liver (absence of fibrotic development
= FO stage)
the salient dimensions that would appear in a signal distribution of tissue
texture feature
sizes arise from the spacing between these elements¨a range of approximately
0.5 mm to
0.7 mm. With disease onset, fibrotic development starts on the portal triads,
then progresses,
eventually forming bridges linking the portal triads to the central veins
(Stages Fl through
F3). These bridges enlarge and coalesce, forming islands of regenerative
tissue surrounded
by fibrotic deposition. In this process, vessel to vessel structural spacing
in tissue texture
becomes gradually replaced by lobule to lobule spacing (Stages F3 to F4).
Thus, a clear
marker of disease progression is the shift in distribution of textural
wavelengths from
shorter to longer wavelength (decreasing k-value), this shift being from about
0.5mm to
about 2mm, and often longer. As collagen accumulates on the surfaces of the
lobules in
response to ongoing disease, and even the intra-lobule hepatocytes become
decorated, the
lobule itself becomes the main textural feature, and the inter-lobule repeat
spacing the
salient repeat width in a power distribution in k-space. This change happens
gradually over
the course of disease progression, shifting the power density in textural
wavelengths
(inverse k-values) from the healthy range out to approximately 2-3mm feature
sizes. The
salient textural features involved in this textural wavelength shift are well
known from
histology studies.
[PARA 2911 In order to diagnose liver disease in its early stages, the shift
of the signal as
a function of k-value from that expected for healthy liver to longer
wavelengths (lower k-
values) indicative of disease onset and progression, can be tracked using the
embodiments
disclosed. This measurement at a particular point in time along the arc of
disease
progression and response to therapy, can be made either by acquiring
successive samples at
individual k-values over the desired range of textural wavelength or,
alternatively, a
gradient can be applied during data acquisition to span the desired range in k-
space. A
hybrid combination of these two acquisition methods can also be used.
[PARA 2921 Contrast between the collagen decorating the various hepatic
structures and
the underlying tissue can be achieved using either endogenous or exogenous
contrast: signal
from fibrosis is dark in standard Ti imaging, and can be bright in T2 imaging,
due to the
large water content in the fibrotic structure. Use of Gd contrast agent
shortens Ti such that
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on Ti weighting the fibrosis shows up bright against the background tissue.
Higher contrast
makes for a more robust measure. However, although these contrast mechanisms
provide
contrast between fibrosis and underlying tissue, standard MR imaging is not
capable of
sufficient resolution to discern the pattern of fibrotic development on the
various hepatic
structures, which characterizes early stage disease. Patient motion blurs the
image, even
when using breath-hold imaging or respiratory triggering. Using standard MR
imaging, liver
disease can only be assessed at the more advanced stages, when the liver may
be
irreversibly damaged. Though advanced disease is diagnosable, what is needed
for therapy
justification and response monitoring is early stage diagnosis.
[PARA 2931 Diverse MR imaging based techniques have been used in assessment of
liver
fibrosis, besides conventional Ti and T2 contrast with or without contrast
agents. MRE
(Magnetic Resonance Elastography), diffusion-weighted imaging (DWI), and MR
perfusion
imaging, can yield some information on liver disease, though none are capable
of robust
diagnosis in the earlier stages of the disease. A major difficulty with them
is that they rely
on surrogate markers for fibrotic development. MRE relies on stiffness
measurement,
perfusion imaging measures blood perfusion parameters, DWI (Diffusion Weighted
Imaging) measures the ADC (Apparent Diffusion Coefficient) of water in the
liver tissue.
These parameters all vary in response to many factors besides fibrotic
development, such as
inflammation, portal hypertension, steatosis, edema, iron overload, and
hepatic perfusion
changes. Currently, there is no direct way to measure fibrotic development in
early stage
disease. By providing the ability to achieve robust resolution at the k-values
pertinent for
measuring fibrotic development, using standard MR contrast methods, the
embodiments
disclosed enables assessment of disease state in early stage liver disease.
[PARA 2941 One of the features of the embodiments disclosed that makes it
novel, is that
it can be used in conjunction with most contrast mechanisms. One application
of this
method is its use in conjunction with diffusion weighting, using diffusion-
weighted contrast
(see FIG. 23 below), but acquiring signal only in the k-value ranges of the
fibrotic deposits
in early stage disease rather than the entire image acquired in standard DWI.
By using the
embodiments disclosed for signal acquisition, the data is acquired with a much
finer spatial
resolution than is possible with diffusion-weighted MR imaging. The texture
being
measured is on the scale of the fiber-decorated structures, between actual
fiber clumps,
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rather than an averaged measure affected by partial volume imaging. Fibrotic
deposition
lowers the diffusion coefficient for water, the lower ADC (apparent diffusion
coefficient) in
areas of fibrosis making for a brighter signal than the surrounding tissue. By
using diffusion
weighting in conjunction with the embodiments disclosed the structural signal
obtained will
measure highly localized water diffusion. Hence, as the lobular unit
transforms from one
with no delineated boundaries to collagen decoration of the hexagonal boundary
and then to
filling in the entire lobule, water diffusion at the boundaries will be
impeded, increasing
diffusion weighted signal intensity. As pathology increases, then a textural
signature can be
obtained by using diffusion contrast. Two approaches to positioning of the
diffusion
weighting gradients are as shown in FIGs. 24 and 25. The shown pulse sequence
for
selecting the desired VOI and initial phase wrap for k is as described in FIG.
3 and is
numbered consistently in FIGs. 24 and 25. FIG. 24 shows positioning of the
diffusion
weighting gradients 2402, 2404 on either side of the second 1800 slice
selection pulse, while
in FIG. 25, the diffusion weighting gradients 2502, 2504 are positioned before
the first, and
after the second, 180 slice select pulses to provide more diffusion time for
the same TE
than would be available when placing the pair either side of the last 180
slice-selection
pulse.
[PARA 2951 The integrated pulse sequence described with respect to FIGs. 24
and 25 may
be repeated with the diffusion gradient applied along multiple axes, similarly
to DTI
(Diffusion Tensor Imaging). The output dataset would then allow development of
a
diffusion tensor, enabling determination of the FA (Fractional Anisotropy), a
reflection of
water flow pathways in the tissue which reflect cellular-level changes.
[PARA 2961 Fibrotic texture development can also be assessed using the methods
disclosed herein in conjunction with contrast, such as Ti or T2 weighting,
with or without
exogenous agents such as Gd. By use of localized sampling in both real and k-
space, the
methods disclosed herein enables fast acquisition of signal at the requisite k-
values,
enabling robust assessment of pathologic tissue texture at a specific location
in the liver¨
providing a measure of the textural frequencies present at that location,
immune from the
subject motion-induced blurring that limits current MR imaging methods. Using
the
methods disclosed herein, the problem of respiratory motion is circumvented by
the speed
of acquisition of the requisite data. To sample pathology variability within
the liver, VOIs
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2602a-2602d can be positioned at various locations in the liver as represented
in FIG. 26,
using either interleaved acquisition within one TR, or measurements in
separate multiple
TRs. By use of the methods disclosed herein, texture coherence is maintained
within each
defined VOI throughout the data acquisition at specific k-values or k-value
ranges, so as to
enable SNR maximization through signal averaging. If desired, repeat sampling
can be
made in subsequent TRs at the same location, to obtain an average measure of
the degree of
fibrotic invasion at that location, for assessment of stage of disease
progression. All of the
various MR-based measures of fibrotic development in liver disease can be
combined with
blood-based, biopsy, MRE, physical exam data across a population, using
machine learning
to both correlate data towards biomarker development for the embodiments
herein for
textural measure, as well as using machine learning in an unsupervised mode
applied to the
textural data for feature extraction towards biomarker development.
[PARA 2971 While the application to liver disease has been called out in some
detail here,
assessment of other fibrotic diseases is enabled by the embodiments disclosed.
Fibrotic
development is the hallmark of lung disease (e.g. cystic fibrosis, idiopathic
pulmonary
fibrosis), myocardial fibrosis, muscle fibrosis, pancreatic fibrosis and
kidney disease.
Additionally, as mentioned previously, some diseases, such as prostate
disease, induce
reduction in proteinaceous deposits.
[PARA 2981 Lung disease diagnosis is stymied by the large range of forms the
disease can
take, each with a different underlying etiology, prognosis, and required
therapy. Idiopathic
Pulmonary Fibrosis (IPF) is a chronic, ultimately fatal disease of the lung
characterized by
progressive decline in lung function. Scarring of the lung and formation of
fibrotic tissue in
the interstitial lung spaces between the air sacs are the primary injury
associated with
disease development. The peripheral airways and vessels may also be affected.
Diagnosis
currently is by ruling out other pathologies, pulmonary function testing,
stress tests, blood
gas analysis, patient history, in conjunction with imaging using High-
Resolution Computed
Tomography (HRCT). IPF diagnosis can be confirmed with lung biopsy, but the
histology
shows striking variation from one region to the next leading to high sampling
errors.
Further, biopsy is a highly invasive procedure, the tissue insult compounded
if there is need
for multiple samples.
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[PARA 2991 IPF is believed to be the result of an aberrant wound healing
process
resulting from repetitive injury of the alveolar epithelial cells. This
triggers action of
fibroblasts that results in abnormal and excessive deposition of collagen in
the pulmonary
interstices, forming pockets of "ground glass," fibrotic tissue formation,
associated with
alveolar degradation. Minimal inflammation is a defining characteristic of the
pathology,
distinguishing it from COPD.
[PARA 3001 Histologically, IPF is characterized by the presence of differing
proportions
of interstitial inflammation, fibroblastic foci, and established fibrosis and
honeycombing, all
coexisting with areas of normal lung parenchyma. This heterogeneity makes
diagnosis by
biopsy problematic. Characteristic high-resolution CT (HRCT) findings of IPF
include
textural changes, including honeycombing, and architectural distortion,
involving mainly
the lung periphery and the lower lobes. In approximately 50% of cases, HRCT
scans are
sufficient to allow a confident diagnosis of IPF, obviating lung biopsy. In
the remaining
50% of patients, the HRCT findings are relatively nonspecific and may mimic
those of other
interstitial lung diseases. Souza et al., "Idiopathic Pulmonary Fibrosis:
Spectrum of High-
Resolution CT Findings", American Journal of Radiology, Dec. 2005
[PARA 3011 An earlier diagnosis of IPF is a prerequisite for improvement of
the long-
term clinical outcome of this progressive disease. When treated early, IPF has
better
outcomes than other forms of interstitial pneumonias. Idiopathic Interstitial
Pneumonia can
either take the form of IPF, or can be classed under several other forms of
pneumonia, with
various presentations and prognoses. A major diagnostic need is to
differentiate lung disease
between IPF and these other forms of lung disease. The ability to more
sensitively image
lung pathology would enable earlier diagnosis, as well as subsequent therapy
monitoring.
But the resolution available from Computed Tomography is limited. Even
acquisition
during a single breath hold is severely compromised by cardiac pulsatile
motion and
noncompliance to breath hold.
[PARA 3021 MRI has recently emerged as a clinical tool to image the lungs.
Along with
enabling tunable tissue contrast and the ability to obtain functional
information, it is a non-
invasive measure, allowing multiple and repeat measurements. In cases of
pediatric
imaging, pregnant patients, or for research purposes, the lack of need for
ionizing radiation
makes MRI the preferred modality.
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[PARA 3031 However, the physical properties of lung parenchyma are very
different from
those of tissue such as liver or brain. The low density of the tissue, and the
susceptibility
differences between tissue and air, lead, respectively, to very low signal and
to rapid
dephasing. This dephasing, resulting from the highly inhomogeneous local
magnetic fields
at the edges of parenchyma, results in T2* values as short as 2msec or less,
at 1.5T. Thus,
the pulse sequences in use rely on GE (gradient echo) refocussing to enable
short TE values.
However, the short T2* also allows a brief signal acquisition. While this is
good from the
standpoint of resolution, the recorded signal is low, making MRI of lung
parenchyma highly
challenging.
[PARA 3041 CT cannot resolve the underlying structure of the ground glass
regions that
appear on imaging of diseased lung tissue.
[PARA 3051 A clear indication of pathology is the appearance of regions within
the lung
that have a hazy, mottled, appearance, often termed "ground glass" pathology.
In some
types of lung disease, under the general term COPD, the appearance of this
patterning in the
lung tissue is associated with inflammation. In IPF, it is associated with
development of
fibrosis in the alveoli. Though the fine tissue textures underlying the
macroscopic
appearance in imaging of ground glass regions can help distinguish the various
forms of
pathology and help set therapy, neither MRI nor CT offer the requisite
resolution. This
distinction can, however, be made using the embodiments disclosed.
[PARA 3061 At the microscopic scale, tissue changes associated with
inflammation in
lung disease are relatively homogeneous within the cloudy patches, showing no
spatial
texture on the sub mm scale. IPF, however, a disease presentation that usually
occurs with
minimal associated inflammation, displays at the microscopic scale a mottled
textural
signature, with a repeat on the order of 0.5-1mm. Though the signal from the
fibrotic
collagen development that causes this texture is low, contrast between the
collagen, which
contains fluid, and the underlying lung tissue, which is very low density
being mainly air
sacs, enables imaging. Hence, as part of an exam workup, the embodiments
disclosed can
be used as a microscope to reveal the fine textural signature underlying the
macroscopically
revealed pathologic regions within the lung.
[PARA 3071 As a non-invasive measure towards determination of lung disease
type, the
embodiments disclosed would provide data on a scale unachievable through
imaging.
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Combination via All machine learning algorithms with current diagnostic
measures¨
pulmonary function testing, stress tests, blood gas analysis, patient history,
imaging, and, if
applied, biopsy¨would enable biomarker extraction from the embodiments
disclosed, and
calibration of the varying textural signatures embedded in the data set.
[PARA 3081 Measure of the healthy alveoli using the embodiments disclosed
would be
difficult, due to the fact that the alveolar walls are on the order of 10 um,
the center of the
air sacs generate no signal, and the susceptibility difference at the air wall
interface would
lead to very rapid dephasing. However, when All machine learning algorithms
are used to
optimize data interpretation, as above, there is a clear chance that the
embodiments
disclosed could be used for direct measure of the alveoli, and the textural
variation inherent
in disease progression.
[PARA 3091 Further, towards diagnosis of lung cancer, the embodiments
disclosed can
evaluate the state of vasculature in the vicinity of lung nodules, suspected
of malignancy.
Current use of CT to evaluate lung nodules in high-risk populations has been
found in
recent analysis to have a false positive rate of over 97%, leading to
unnecessary follow-up
procedures and concern. Castellino, "Lung Cancer Screening¨Benefits Few, May
Harm
Many", Medscape Jan 30 2017.
[PARA 3101 While the lung moves significantly during free-breathing, and even
breath-
hold motion is large compared to the very fine textural features that can
discriminate disease
forms, the embodiments disclosed are immune to this motion, once the VOI is
defined. The
speed of data acquisition with the embodiments disclosed (< 1 minute),
combined with the
ability to run the method in a free-breathing mode, makes the procedure easy
to incorporate
in a standard MR lung scan. Wild et al., "MRI of the lung" (1/3), Insights in
Imaging 2012.
[PARA 3111 Given the myriad forms of lung disease manifestation, and the need
to
distinguish them in order to determine appropriate therapy and monitor
response, more
information on pathology is needed. While there are several symptomatic level
tests,
medicine weights highly the variability across CT and MR images. However, the
ability to
look at tissue changes at the microscopic level, the earliest harbinger of
disease onset, has
until now not been available. Use of the embodiments disclosed provides such
capability.
[PARA 3121 To obtain the most information from this measurement, diagnostic
information obtained by current methods can be used as training sets by
incorporating this
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data in a machine learning algorithm together with the data obtained by the
embodiments
disclosed. Measures of pulmonary function, stress tests, images, blood gas
analysis, patient
history, and biopsy, are all suitable candidates, though the main driving
element of the
learning set is disease outcome when correlated with the new data.
[PARA 3131 One of the most common histologic features of the failing heart is
myocardial fibrosis, diffuse replacement or invasion of the myocardium by
fibrous
connective tissue. This fibrotic development, which results in wall
stiffening, reduced
contractility, and impaired overall heart performance, is a significant global
health problem
associated with nearly all forms of heart disease. Cardiac fibroblasts, an
essential cell type
in the heart, is responsible for the healthy extracellular matrix. However,
upon injury, these
cells transform to a myofibroblast phenotype and contribute to cardiac
fibrosis, generating
excessive deposition of connective tissue in the interstitial space in the
cardiac muscle.
Fibrosis has been shown to be a major independent predictive factor of adverse
cardiac
outcome. However, there is a lack of accurate clinical tools to precisely
phenotype patients
with heart disease.
[PARA 3141 Assessment of cardiac fibrosis can be made by biopsy and staining
techniques. However, biopsy is highly invasive, sampling errors limit its
sensitivity, and the
entire left ventricle cannot be sampled, hence limiting accurate clinical
pathology
assessment.
[PARA 3151 Use of cardiovascular magnetic resonance (CMR) for the non-invasive
imaging for patients with compromised heart function has increased over the
last decade.
The two main methods currently in use are Late Gadolinium Enhancement (LGE) MR
and
Ti mapping, also based on Gd contrast.
[PARA 3161 LGE of myocardial fibrosis is based upon the prolonged washout of
Gd that
results from the decreased capillary density within the myocardial fibrotic
tissue. The
increase in gadolinium concentration within fibrotic tissue causes Ti
shortening which
appears as bright signal intensity in the CMR image based on conventional
inversion-
recovery gradient echo sequences. This provides discrimination between
fibrotic
myocardium and normal myocardium.
[PARA 3171 In multi-site clinical use, LGE has absolute signal level problems.
Its
accuracy for absolute quantitation is limited also due to an over-sensitivity
to image settings
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such as intensity threshold. While currently LGE CMR is the most accurate
method to
measure myocardial replacement fibrosis, its sensitivity is limited for the
assessment of
diffuse interstitial fibrosis.
[PARA 3181 The second MRI measure of fibrotic development is based on the
variation in
post Gd contrast Ti relaxation times that result from variations in the
molecular
environment of the water molecules in tissue. Post-contrast Ti values of
scarred
myocardium are significantly shorter than those of normal myocardium due to
the retention
of gadolinium contrast in fibrotic tissue. As such, Ti mapping can accurately
differentiate
pathologic fibrosis from normal myocardium, and can quantify fibrotic
development. Ti
relaxation times vary significantly from one type of tissue to another, but
also within the
same tissue depending on its physiopathological status, i.e. whether
inflammation, edema,
or fibrosis is present in the tissue under study. Hence, mapping of Ti across
a region
provides information on the spatial distribution of pathology. Specific
properties of the
target tissue determine the level of Ti shortening induced by the gadolinium
contrast agent,
generating specific differences in signal intensity.
[PARA 3191 However, while post-contrast Ti value of myocardial fibrosis is
significantly
different from that of normal myocardium, Ti distribution can be significantly
scattered and
this limits its sensitivity for disease states with less severe fibrosis.
[PARA 3201 Though clinical data to date is scarce, the combined application of
Ti
mapping with CMR-LGE may help to provide more precise assessment of the heath
of
myocardial tissue, and to stratify cardiovascular risk in patient populations,
detecting
subclinical myocardial changes before the onset of serious heart dysfunction.
But the
shortcomings of these techniques, combined with the recent reticence to inject
Gd due to its
retention in the brain, make the potential role of the patented method in
assessment of
myocardial fibrosis timely. The embodiments disclosed can provide the fine
tissue
characterization that will help improve therapeutic strategies and enable a
more direct
monitoring of their effect, thus improving clinical outcomes. Such enhanced
measure would
assist in the search for much needed therapies.
[PARA 3211 Further, spatial resolution of the amount of cardiac fibrosis in
early stage
heart disease using MRI is seriously hampered by cardiac pulsation over the
time of the
measurement. As motion is, unlike Gaussian noise, a non-linear effect, it
can't be averaged
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out¨there must be sufficient signal level to allow reregistration before
averaging for
electronic noise-reduction. A more sensitive (higher SNR), non-invasive
technique, capable
of assessing textural changes throughout the range of development of cardiac
fibrosis, from
onset to advanced pathology, is needed to enable diagnosis and monitoring of
therapy
response. The embodiments disclosed provide this capability Travers et al.,
"Cardiac
fibrosis¨the fibroblast awakens", Circulation Research, March 2016; Bronnum
and
Kalluri, "Cardiac fibrosis: cellular and molecular determinants", Chapter 29
of Muscle,
Vol.1; Konduracka et al., "Diabetes- specific cardiomyopathy in type 1
diabetes mellitus: no
evidence for its occurrence in the era of intensive insulin therapy". European
Heart J. 2007;
Mewton et al., Assessment of myocardial fibrosis with cardiac magnetic
resonance:, Journal
of the American College of Radiology, Feb.2011
[PARA 3221 MRI of the heart enables multiple and repeat measurements to track
therapy
response. Pertinent morphological information, such as wall thickness, edema,
scarring of
the myocardium, and perfusion (e.g. at rest and during stress) are all
macroscopic measures
of health. The basic imaging protocols to obtain these measures, such as LGE
and Ti
mapping, have helped to establish CMR. However, a figure that is missing from
current
diagnostics is the ability to track the onset and progression of pathologic
changes at the
microscopic level, specifically the development of fibrosis within the heart.
[PARA 3231 To provide a much stronger diagnostic for heart disease, the data
sets from
both techniques can be combined. Use of the embodiments disclosed would allow
direct
measure of the fine texture signature. While it could be used in conjunction
with Ti
decay/Gd injection measures, the recent push to eliminate use of Gd may make
use of
endogenous contrast, such as Ti, IR, and diffusion weighting, necessary. Ti
mapping data,
again using either Gd or endogenous contrast can be compared to the textural
measures
acquired in multiple VOI positioned to cover the same area as that mapped.
Application of
the two techniques as interleaved sequences would ensure that the two measures
are
acquired at the same tissue location. In this way, the textural signal
acquired by the
embodiments disclosed can be compared with the Ti mapping, enabling
determination of
both the direct textural measure and the molecular environment, both measures
providing
assessment of the level of pathologic fibrotic development. The data from the
two
techniques, along with other measures of cardiac function from physical exams,
serum
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concentrations of collagen-derived serum peptides, as well as quantitative
analysis of
echocardiograms can together be fed into machine learning algorithm, providing
a much
clearer assessment of disease progression and therapy results.
[PARA 3241 Gd-based LGE could provide a third layer of assessment of the
developing
pathology. For combination with the other diagnostics for optimization of
information
extraction by machine learning. This combination of techniques, given the high-
resolution
direct measure provided by the patented method, can provide an enhanced
assessment of
disease development, enabling therapy response determination and disease
prognosis.
[PARA 3251 Prostate cancer is the second most common cause of cancer death in
American men. In current practice, prostate biopsy exams are recommended for
men for
whom blood tests show high prostate specific antigen (PSA) serum levels, or
who
demonstrate other symptoms related to prostate dysfunction on exam. Biopsy is
painful,
risks serious complications such as infection and bleeding, and is
diagnostically fraught due
to read and sampling errors. The procedure involves twelve needle samples,
taken at
random from the prostate using trans-rectal ultrasound (TRUS) guidance. Often
several
tumors of various sizes are present in the organ. Due to the random nature of
the sampling,
a tumor may not be intersected by any of the needles, making it problematic to
quantify
aggressiveness of suspected cancer. TRUS-guided needle biopsy misses 25% to
30% of
clinically significant tumors because anterior prostate cancer lesions are
occluded, making
detection difficult until the tumors are quite sizeable. 3 MRI can help avoid
unnecessary
prostate cancer biopsies, AuntMinnie.com 1/25/17
http://www.auntminnieeurope.com/index.aspx?sec=sup&sub=mri&pag=dis&ItemID=6139
2614 El Sevier, Ltd. open access articles, H.U. Ahmed et al., 1/19/17,
http://dx.doi.org/10.1016/S0140-6736(16)32401-1.
[PARA 3261 Further, the highly invasive biopsy procedure is often prescribed
when there
is no cancer present- recent research indicates that over a quarter of the men
sent for
prostate biopsy did not need the procedure. Also, men with no cancer, or with
benign
cancers, are sometimes given the wrong diagnosis and are then treated even
though this
offers no survival benefit and has serious side effects. Overdiagnosis and
overtreatment
have increased in diagnostic practice. A Quarter of Prostate Cancer Biopsies
May Not Be
Necessary, Xuan Pham, Lab Roots, Jan 2017,
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https://www.labroots.com/trending/cancer/5107/quarter-prostate-cancer-
biopsies.12 Imaging
Guided Prostate Biopsies Miss Apical Cancer Lesions, Medscape 1/25/17
http://www.medscape.com/viewarticle/874873?src=wnl_edit_tpal&uac=156182MR . As
a
result, the US Task Force on Preventive Health Care has taken the position
that the risk
from PSA testing outweighs the benefits. Clearly, additional information is
needed here to
make the correct diagnoses and avoid unwarranted and invasive procedures and
treatment.
[PARA 3271 Recent studies have focused on the benefit of multi-parametric MR
imaging
(mp-MRI)¨scans that use multiple types of tissue contrast¨before biopsy. mp-
MRI has
been found to rule out the need for needle biopsy in as many as a quarter of
cases referred
for MRI. When it is used in conjunction with medically necessitated biopsy, it
can correctly
diagnose a high percentage of aggressive prostate cancers, with higher
sensitivity than
provided by standard transrectal ultrasound-guided (TRUS) biopsy. An inverse
correlation
exists between Gleason score, a marker of tumor aggressiveness, and ADC
(Apparent
Diffusion Coefficient, as measured with DWI (Diffusion Weighted Imaging) in
MR. In
addition, the mp-MRI scans also demonstrate ability to more precisely locate
and gauge the
size of tumors, improving detection of aggressive cancers. However, it is
often hard to
distinguish some benign abnormalities, such as fibrosis, prostatitis, and scar
tissue from
lesions.
[PARA 3281 While combining mp-MRI with biopsy yields better results than does
ultrasound-guided biopsy alone, this is still not 100% accurate. It is
required that men still
be monitored after their mp-MRI scan and biopsy. Biopsies will still be needed
if a later
mp-MRI scan shows suspected cancer, but the scan could help to either rule out
the need for
biopsy, or guide the biopsy so that fewer and better biopsies are taken.
[PARA 3291 Because of the highly invasive nature of biopsy, as well as its
diagnostic
shortcomings, prostate care would be progressed if a less-invasive, high
information content
diagnostic procedure was available. As the additional information provided pre-
biopsy by
mp-MRI appears to be of clear value, adding high density information to this
procedure
would be of huge benefit, as the patient is already in the scanner.
[PARA 3301 Accurate tumor localization and typing within the prostate would
enable
focal therapies such as cryosurgery, intensity modulated radiation therapy,
brachytherapy,
or high intensity focused ultrasound to ablate just the tumor rather than
having to use a more
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global therapy. Xu, et al., Magnetic Resonance Diffusion Characteristics of
Histologically
Defined Prostate Cancer in Humans, Magnetic Resonance in Medicine 61:842-850
(2009).
[PARA 3311 For a patient referred for mp-MRI, in addition to the basic 3
studies¨T2-
weighted, diffusion-weighted, and DCE (dynamic contrast enhanced) scan, the
embodiments disclosed would be used as described above, to measure the state
of the
microvessels across the prostate, and to measure the tissue texture through
the organ, as this
reflects the health of the organ.
[PARA 3321 Properly implemented, the embodiments disclosed can be used to
provide
high value diagnostic information towards localizing and typing prostate
tumors for size and
aggressiveness, while distinguishing them from BPH (Benign Prostatic
Hyperplasia), a
common pathology associated with aging. This procedure would involve both
measuring
and mapping the density and form of the microvasculature across the prostate,
as an
indication of tumor localization and aggressiveness, as well as an assessment
of pathologic
changes in the prostate tissue across the organ.
[PARA 3331 Mp-MRI uses typically three different contrast mechanisms¨T2
contrast,
diffusion-weighted contrast (DWI) and DCE, which follows the time course of
the tissue
image following injection of an exogenous contrast agent such as gadolinium.
Higher
resolution measurement of the microvasculature can be provided by using the
embodiments
disclosed in conjunction with the mp-MRI DCE measure. As data acquisition
using the
embodiments disclosed is extremely fast, data to map the high-resolution micro
vessels
across the organ could be acquired as part of the DCE sequence.
Additionally/alternatively
endogenous flow contrast, such as arterial spin labelling, could be used to
provide contrast
to the vessels for measure of their volume, density and sizes across the
organ.
[PARA 3341 As discussed previously, one of the features of the method is its
ability to be
used in conjunction with most any contrast mechanism, thus enabling high
resolution
measure of the tissue textures brought into relief by the contrast. The speed
of the measure,
along with the fact that the targeted volume of interest (VOI) can be sized
and moved as
desired, enables coverage of the entire prostate in a < 1-minute exam,
allowing assessment
of changes in the micro-vasculature and matrix tissue with disease
progression. Ti, T2, and
DWI contrast can be used in conjunction with the embodiments disclosed to
provide
assessment of changes in the tissue texture across the prostate from the
epithelial region and
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through the stromal areas. This could replace the need for a physical biopsy,
providing a
non-invasive tissue pathology assessment.
[PARA 3351 Further capability is provided by use of DTI (Diffusion Tensor
Imaging)
contrast for which varying the direction of the DWI diffusion gradient allows
tracking of
directional change in tissue resulting from microscopic changes affecting
water diffusion
through the tissue. Use of DTI for application of the embodiments disclosed,
allows
assessment of the anisotropy of tissue changes.
[PARA 3361 At the microscopic level, normal prostate has a branching duct-
acinar
glandular architecture embedded in a dense fibro-muscular stroma. In prostate
carcinoma,
tightly packed tumor cells disrupt the duct¨acinar structure leading to the
decreased ADC in
tumor due to the cellularity induced diffusion restriction. The tissue texture
in healthy
fibrous tissue has a repeat distance (wavelength) on the order of a few
hundred microns,
while that in tumors is on the order of tens of microns. Fine tissue texture
is also visible in
regions of BPH on histology, but the overall patterning is less isotropic and
varied in size
than that from tumor regions, an anisotropy that can be measured by varying
the direction of
applied gradients used in the measure. Hence, a clear variation in textural
signal is available
to differentiate the fibrous tissue to allow diagnosis of cancerous vs. BPH
vs. healthy
prostate. The embodiments disclosed is the only non-invasive diagnostic
capable of directly
measuring these tissue variations.
[PARA 3371 Benign Prostatic Hyperplasia (BPH) has fibrous, muscular and
glandular
components¨the fibrous tissue is laid in irregular patterns, as with the
muscular element,
giving the appearance of nodularity. The increased cellularity in the region
of tumors leads
to a lowered apparent diffusion coefficient (ADC), a measure obtained from DWI
in the
region indicative of development of prostate cancer. If a form of diffusion
contrast which
highlights anisotropy in microscopic components of tissue texture is used to
provide
contrast for the acquisition of data by the embodiments disclosed, FA
differentiates stromal
from epithelial BPH.
[PARA 3381 Standard diagnosis of disease state is often made by comparison
between
various diagnostic measures, such as symptoms, serum markers, pathology
measures, and
patient outcomes. This last figure, patient outcome, is, of course,
retrospective. The huge
increase in processing capability in today's computers has changed the way
this comparison
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is made, as current electronic storage and processing capability enable more
detailed
comparison among multiple diagnostics. But, more than just using the various
diagnostic
measures towards a more robust diagnosis, the ability of computers can enable
maximum
extraction of data from new diagnostics through use of machine learning
techniques. In
applying the embodiments disclosed to prostate disease, rather than trying to
extract
biomarkers as specific features from the power spectrum obtained as
characteristic of the
vessels and surrounding tissue in the organ, the highest information content
can be obtained
from the data by using the entire data set. Anecdotal notes on disease
progression and
outcomes, along with diagnostic data obtained from the mp-MRI scans, can serve
as a
training set to "teach" a machine learning computer algorithm to assign a
diagnostic
meaning to the data obtained by the embodiments disclosed, stage disease and
diagnose
disease progression and aggressiveness. Computers are more capable of making
the
comparisons, depending on the huge amount of information contained in the
various data
sets to draw conclusions and stage disease. By this method, the maximum amount
of
information can be extracted from the data.
[PARA 3391 Given the tissue variability across the prostate from the
epithelial to the
stromal region, and locally due to developing pathology/tumors, use of the
disclosed
embodiments as a diagnostic such that it covers most of the prostate is best
poised to assess
the health of the organ. Hence, the workflow to provide this disease diagnosis
and to track
progression would look something like:
[PARA 3401 Patients that are in for mp-MRI because of symptoms or PSA testing
indicating suspicion of prostate disease would also be scanned using the
embodiments
disclosed in conjunction with various contrast techniques.
[PARA 3411 Scout images as described herein may be employed for preliminary
assessment and calibration.
[PARA 3421 The embodiments disclosed would be used to measure
microvasculature¨
density, volume, and vessel size, in stepped VOIs across the prostate, using
the exogenous
contrast used for mp-MRI, interspersing this measure with the time course
imaging
acquisition done as part of the mp-MRI, or using endogenous contrast such as
ASL, to
provide contrast for measure of vasculature.
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[PARA 3431 If the exogenous contrast used in mp-MRI were to be used to
generate vessel
contrast for measure by the embodiments disclosed, mp-MRI and the embodiments
disclosed could be interspersed in time as the contrast reached a maximum and
then
decreased as the labelled bolus of blood moved out of the prostate. Otherwise,
endogenous
contrast such as ASL could be used for this purpose. As endogenous contrast
does not
involve injection of Gd, it is in line with the non-invasiveness of the
embodiments
disclosed.
[PARA 3441 In addition to the other mp-MRI imaging, the embodiments disclosed
would
be used to acquire textural signature using Ti, T2, diffusion-weighted
contrast, though, as
desired, additional contrast methods could be applied. Gradients for these
techniques would
be applied in varying directions, to learn something about the directionality
of the
underlying tissue structure as this is a component in associating structure
with pathology.
[PARA 3451 For each patient, comparison of the data obtained from the various
applications of the embodiments disclosed would be compared with all other
diagnostics to
"train" the method to yield the highest disease-specific information. The
various input
information sources include all other diagnostics, such as the mp-MRI data,
serum
measures, biopsy, physical exam, ultrasound, CT (Computed Tomography, and PET
(Positron Emission Tomography). In addition, pertinent patient/disease history
would be
included in the training set. By this method, textural signatures specific to
an individual
pathology course can be identified.
[PARA 3461 Many neurologic diseases and conditions have a vascular component
that
may serve as a marker for disease onset and progression, allowing diagnosis
and therapy
tracking which provides another exemplary implementation of the methods
disclosed
herein. The ability to sensitively assess changes in micro-vessels would
enable monitoring
of pathology progression in a number of diseases which are often not diagnosed
until
pathology is well advanced.
[PARA 3471 Angiogenesis, formation of new blood vessels from pre-existing
micro
vessels, is necessary for tumor growth and metastasis. Rather than the ordered
formation of
vessels that exist in healthy tissue, pathogenic angiogenesis tends to form
chaotic, tortuous
vessels, replete with blocked, dead end structures¨see FIG. 23. Vessel
diameter and wall
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thickness are highly variable in angiogenic micro-vessels, with marked vessel
permeability
in places.
[PARA 3481 For example, tumor aggressiveness is closely correlated with
neovascular
density, as angiogenesis is needed to supply the tumor with oxygen and
nutrients. The
ability to assess the amount of angiogenic development at a tumor site, and to
characterize
the vascular morphology, would enable assessment of tumor aggressiveness. By
determining the extent of the angiogenic vasculature within and surrounding
the tumor, it is
possible to determine the necessary boundaries for surgical removal. Likewise,
therapy
response may be trackable in part by measurement of vasculature as it reverts
to a more
normative state. Degree of angiogenic vasculature development can be assessed
to some
extent using serum markers, or by biopsy. But biopsy is highly invasive, and
prone to
sampling errors and read variability.
[PARA 3491 As another example, several forms of dementia, most notably
Alzheimer's
disease (AD), are now recognized as having a large vascular component with
pathogenic
vessel development. Additional forms of dementia, such as Huntington's disease
(HD),
Parkinson's disease (PD), and Frontotemporal dementia are also found to have
compromised vasculature. In some cases, the salient cause of the dementia
appears to be
pathogenic vasculature in the brain, such as CVD.
[PARA 3501 Chronic inflammation is another important factor that can lead to
abnormal
neurovascular structure, exhibiting permeability and hemorrhage. Some
microvasculature
pathogenesis is linked to permeability of the blood brain barrier. Multiple
Sclerosis, a brain
disorder with pathology associated with inflammation and axonal demyelination,
exhibits
microvessel disruption. Stroke and the resultant ischemia result in
development of
angiogenesis modifying the capillary network, as the body attempts to heal the
damage. As
angiogenesis features increased vascularity, involving both structural and
functional
alterations within the neurovascular system, this increased density, and the
high variability
in vessel spacing, is a promising biomarker that can be used for the
characterization of
ischemic conditions in the brain following stroke. For all of these
conditions¨tumor
development, ischemic stroke, and brain pathology in dementia, a means of
assessing the
micro-vasculature in brain tissue, is needed¨both for determination of
pathology
advancement, and for assessment of therapy response.
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[PARA 3511 Currently, MRI assessment of the health of microvasculature is most
commonly made by perfusion imaging. Perfusion is the irrigation of tissues via
blood
delivery through the microvessels. Because the state of the vessels changes
the dynamics of
blood flow, such measure can be used to assess vascular health. For perfusion
MR imaging,
either endogenous or exogenous contrast is used. Exogenous contrast is most
commonly
provided by use of Gd-based contrast agent. Endogenous contrast is obtained
through a
technique known as ASL (Arterial Spin Labelling) in which the blood flowing
into a region
of the brain is magnetically labelled. In both cases, sequential images are
made via fast
imaging techniques, as the contrast moves into, and exits, the imaging plane.
One of the key
features of dynamic imaging, such as perfusion imaging, is that differential
contrast can be
obtained via subtraction of the image obtained with no contrast agent/blood
tagging in the
imaging plane from that obtained when contrast is at a maximum in the imaging
plane.
[PARA 3521 When using a contrast agent for this measure, a bolus of the agent
is injected
intravenously and successive images are acquired as the contrast agent passes
through the
microcirculation. (Opposite to what is observed when no contrast agent is
used, use of T2
weighting results in dark blood when a contrast agent is used, and Ti
weighting results in
bright blood.) In order to enable fast data acquisition to allow multi-image
tracking of the
flow before the contrast agent leaves the imaged tissue region, images are
usually acquired
using a variant of the fast MRI acquisition sequence known as EPI (Echo Planar
Imaging).
To characterize the state of the vessels, various flow related quantities are
measured: (MTT)
mean transit time through the voxels, time to peak signal, CBF (Cerebral Blood
Flow), and
CBV (Cerebral Blood Volume). These quantities, which vary with vascular
condition, are
all measurable via perfusion imaging. In addition to the sequential
acquisition of images
obtained while the bolus of contrast agent is present in the blood, or the
magnetically
labelled blood is flowing in the imaging plane when using ASL, at least one
image is taken
following passage of the bolus, or of the labelled spins, through the
microvasculature, when
contrast between the blood and surrounding tissue is minimal. This image is
then subtracted
from the early images to allow calibration of the absolute signal level from
the
microvasculature. Temporal tracking by acquisition of multiple images enables
flow
characterization and determination of regions of compromised vessels.
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[PARA 3531 Angiogenic vasculature is denser, and more varied in vessel
diameter and
spacing, than are healthy vessels. The high spatial variation in vessel
thickness and spacing
is one of the hallmarks of angiogenic vasculature and hence, along with
increased vessel
density, serves as a marker for angiogenesis-related pathology. However, image
resolution
in perfusion imaging is not high enough to determine detailed vascular
morphology. Flow
contrast highlights locally averaged signal variation due to the pathogenic
flow parameters,
offering indirect assessment of vessel morphometry. However, the methods
disclosed herein
can be used to directly measure vessel density, and vessel spacing
variability, to provide
direct, robust assessment of angiogenic vessel development. Using the methods
disclosed
herein to acquire signal vs. k-value data disclosing tissue texture enables
robust resolution
of the morphometric features of the vessels. This acquisition can be done in
one TR, fast
enough that the sequence can be injected into the multi-image-acquisition
perfusion series.
To provide best resolution, this morphometry acquisition would be done near
peak contrast,
either acquiring data for one TR or for multiple TRs acquired either
sequentially or
interspersed at various time points with the perfusion image acquisitions.
[PARA 3541 Obtaining a differential measure of the vasculature by the methods
disclosed
herein, obtaining signal vs. k-value data both with and without contrast,
provides validation
of the origin of the textural signal as arising from the vasculature. Making
these two
measures as close in time as possible allows best spatial and phase
correlative value
between the two data acquisitions, to accurately highlight the signal arising
from the
vessels.
[PARA 3551 The best way to keep the time between the two acquisitions short
when using
ASL contrast is by 1) acquiring data, by the methods disclosed herein, in a
specified
imaging plane with proton density contrast; 2) immediately following the first
acquisition,
spin labelling in a second plane, upstream in the blood flow, in close
proximity to the
imaging plane; 3) acquiring spin-labelled data in the imaging plane, by the
methods
disclosed herein, the labelling and 2nd acquisition as close in time as
possible following the
first acquisition. Signal vs. k-value data can be acquired using either
gradient on or gradient
off acquisition, or a combination of the two, to provide measure of signal
across the desired
span in k-space
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[PARA 3561 Because the morphometric parameters being measured are expected to
vary
significantly, acquisition of signal across a range of k-values is required,
to determine the
underlying structural signature of the vessels. The broadness of the
distribution of signal vs.
k-value, and where its peak lies in k-space are the critical features of
interest. The peak
assesses the average vessel density and the broadness assesses the variability
of vessel
spacing; both markers for angiogenesis characterization. The acquisition can
be done either
with a gradient on or with the gradient off during acquisition. Appropriate
windowing in the
direction of the acquisition axis of the VOI will allow sampling at a targeted
spread of k-
values, the exact window function determining the degree of correlation across
the k-value
range. Additionally, hybrid acquisition is possible wherein a gradient is on
for some part of
the data acquisition within one TR and off for part of the acquisition. The
aim here is to,
while acquiring a range of k-values, ensure sufficient repeats of, say, a set
of highly
correlated k-values, to allow SNR maximization by averaging, while ensuring
fast enough
acquisition to provide immunity to subject motion.
[PARA 3571 Alternatively, rather than temporally intersperse data acquisition
by the
methods disclosed herein into standard perfusion imaging, the methods
disclosed herein can
be used in conjunction with any blood contrast method to measure vessel
morphology
directly, in regions exhibiting pathogenic flow parameters. Both vessel
spacing and the
variability in this measure are known markers of angiogenesis, the vasculature
spacing
becoming more random with degree of pathology. For contrast, either structural
contrast
such as T2 or Ti weighting, which yield, respectively, bright or dark blood,
can be used.
Additionally, both black blood and bright blood flow contrast can be achieved
by various
standard methods, including arterial spin labelling. This structural measure
of the vessels
can be carried out in as many tissue regions, using as many acquisition
directions, as
desired. Angiogenic vasculature would be expected to exhibit a high degree of
anisotropy,
so varying the orientation of the acquisition axis between acquisitions
provides another
marker of pathology. Correlation of the flow data from the perfusion imaging
with the
structural vessel data from application of the methods disclosed herein, can
be made
through machine learning.
[PARA 3581 For tracking angiogenesis relating to ischemic stroke, or in the
vicinity of a
tumor, acquisition by the methods disclosed herein to assess the vessel
structure, can be
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carried out in the vicinity of lesions. To achieve this, a real time response
to an imaging
scan that defines the location of these lesions would be used to target the
location for the
subsequent vessel structure measurements by the methods disclosed herein.
Acquisition
using multiple VOIs/locations and multiple acquisition orientations can be
done for
correlation with the various lesions appearing on the imaging sequence.
[PARA 3591 To study vascular pathology associated with brain disease such as
dementia,
the VOI can be positioned in the vasculature near the cortical region(s)
implicated in the
dementia. Data can be acquired in one or more VOIs in one TR. Additionally, in
scanners
capable of parallel imaging, multiple VOIs can be defined with simultaneous
record of data
to sample extended areas of the brain vasculature. For example, in dementia in
which
multiple cortical regions seem to be damaged, VOIs can be placed in the
vasculature
feeding these different regions and data recorded simultaneously.
[PARA 3601 The usual path for diagnostic development is feature extraction
from the
output data towards biomarker identification. Though feature extraction may
define a
specific biomarker, often in diagnostic development extended clinical work
(years, dollars)
is needed to strongly correlate biomarkers with pathology. This reliance on
statistical
correlation of individual inspection- derived biomarkers with outcomes is
further stymied
by the small size of initial test populations.
[PARA 3611 Medical data analysis is changing rapidly. Recently developed
analysis
techniques enable efficient determination of the total information content of
data acquired
using new diagnostic methods. In contrast to the previous "hunt and peck"
method, current
pattern recognition and machine learning techniques enable rapid correlation
with other
diagnostic content. In this way, feature extraction and biomarker development
can be done
by machine learning rather than by human observation of data. In effect,
rather than
isolating one characteristic (biomarker) from the entire signal vs. k-value
dataset is
correlated with other patient data to yield strong pathology correlations. As
such, focusing
on the acquisition end of the embodiments disclosed enables highest possible
SNR as input
to the machine learning algorithms used in this effort.
[PARA 3621 Computer programs are now adept at determining patterns within
single
images as well as performing highly efficient correlation of data with other
medical
history/diagnostic information. Data output from application of the
embodiments disclosed,
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when taken in its entirety, provides the highest information content. Rather
than reducing
the information content through front-end feature extraction, the entire
distribution of signal
vs. k-value acquired from each VOI will be fed into a machine learning
algorithm with
pertinent diagnostic data taken by the current standard measures. For example,
correlational
data could be liver disease staging (FO-F5) derived from a doctor's report
derived from
histology images, the current gold standard measure for liver disease, liver
function tests
(liver serum), and physical exam.
[PARA 3631 Alternatively, the correlational data could be the output from any
of those
tests individually. With sufficient number of cases, this would enable finer
gradations to be
defined in disease staging, for instance, steps in between each stage¨ FO and
Fl, between
FO and F2, and between FO and F3, would be possible to define using this
method.
Additionally, outcomes¨progression to more advanced pathology, or therapy-
induced
healing¨can provide correlational data for machine learning algorithms for
correlation with
textural assessment from application of the methods disclosed herein.
[PARA 3641 The assessment stage obtained by the methods disclosed herein can
be
mapped on top of a standard MRI morphology image of the diseased liver. (For
easier
viewing, an icon can replace the staging number.) This will facilitate
visualization of the
disease variability through the organ. Additionally, these staging values can
be correlated by
machine learning with the imaging output obtained on the same patient by MRE,
standard
DWI, or perfusion, for instance to track possible correlations.
[PARA 3651 A final example of pathology assessment employing the methods
herein is
brain tissue. Brain pathology is often problematic to diagnose and treat
because of the
sensitivity of the organ to intervention. Further, changes in cognition and
behavior can
occur over a long time span such that the underlying pathology can go
unchecked for years.
In AD there is a long pre-symptomatic period, with underlying, ongoing
development of
pathology at the molecular and tissue level, leading eventually to neuronal
damage. Though
several have been tested in large clinical trials, there has been a stunning
lack of approval
for new therapies for AD or other forms of dementia. As the population ages,
swelling the
ranks of those afflicted with the disease, the situation is dire. The negative
results of several
of these clinical drug trials highlight the need for targeting subjects
earlier in pathology
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development. However, this requires a diagnostic capable of targeting subjects
in the pre-
symptomatic phase. A test to identify such subjects has remained elusive.
[PARA 3661 Studies indicate that gray matter is affected earlier than white
matter with
dementia onset and progression. The cortical structures found to be earliest
to suffer
degradation are the hippocampus and entorhinal cortex, pathology which leads
to memory
loss and disorientation. Recent research applying image processing to 3 Ti-
weighted MR
images of the brain has indicated a statistically significant correspondence
between textural
features on images of the hippocampus and MMSE (Mini Mental State Exam)
scores. The
texture is not directly measurable with MR imaging, due to insufficient
resolution, but
image analysis metrics correlate specific textural gradations with glucose
uptake reduction
in the hippocampus and subsequent hippocampal shrinkage, a marker for AD, in
addition to
the correlations with reduced MMSE scores. These texture features are not
discernible
except as a result of image processing, and their source is not known.
Research indicates
that these textural changes precede cognitive decline, and track with symptom
onset. Thus,
the hippocampus is a good target for application of the embodiments disclosed
of texture
measurement for pathology assessment.
[PARA 3671 As the exact etiology of dementia within the hippocampus and
entorhinal
cortex is unknown, the embodiments disclosed will be used to gather a
sufficiently complete
data set to provide detailed assessment of tissue texture within the
organ¨either the
hippocampus or the entorhinal cortex. Both textural wavelength content and
variability, as
well as orientation and location dependence will be measured. Signal
acquisition across a
range of k-values, in at least 3 (orthogonal) directions, using a plurality of
contrast methods
(as the origin of the texture is unknown), is requisite to well-characterize
the texture.
Acquisition of data across the organ, by defining VOI dimensions to enable
fitting the VOI
fit into the organ entirely at different locations will enable determination
of the spatial
variability of the texture. By acquiring signal data across a range in k-space
corresponding
to wavelengths of tens of microns out to about 1 - 2mmensures that a large
variety of
textural signals contribute to the information content of the measurement. The
embodiments
disclosed can be used in conjunction with any contrast mechanism, such as
inversion
recovery, a heightened form of Ti weighting, or diffusion weighting.
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[PARA 3681 The predictive value of the novel biomarker provided by the
textural data
acquired by the embodiments disclosed, towards assessment of the degree of AD
pathology,
can be defined by correlation with a range of diagnostic information from the
same patient.
The main correlation marker will be drawn from patient outcomes¨i.e.
definitive diagnosis
of AD or other dementia¨as this has the highest diagnostic information
content, though
definitive diagnosis is well-downstream from the pathology we are assessing.
Additional
correlation will be drawn from patient MRI imaging data on hippocampal
shrinkage, a
proven, and continuous, marker of advancing AD (as well as other forms of
dementia). This
correlation will be made longitudinally with disease progression, if possible.
Again, changes
in tissue texture in the hippocampus are expected to predate noticeable
cognitive decline,
and measurable change in volume via MRI. A third correlative marker is FDG-
PET, as
decline in glucose metabolism is expected to occur early relatively in disease
progression.
As a fourth correlative biomarker, the MMSE (Mini Mental State Exam) provides
longitudinal data on cognitive functioning and decline. Genetic predilection
for AD
provides an additional marker for correlation with the textural measure
acquired by the
embodiments disclosed. While the previous markers provide downstream
correlative value
(on the outcome side), genetic markers exist in advance of any pathology
development.
Correlation of this varied set of biomarkers with the data acquired by the
embodiments
disclosed in the hippocampus and entorhinal cortex, across a broad range of
patients, will
enable a clear definition of diagnostic content of the use of the embodiments
disclosed for
early stage prediction of AD pathology.
[PARA 3691 Current machine learning algorithms are capable of pathology level
classification of non-specific features, as will be obtained from MR data
acquisition by the
embodiments disclosed. As such, the disclosed sources of correlational data
above will be
input into machine learning algorithms to highlight the correlation with
textural features and
disease.
[PARA 3701 Though research has indicated that the hippocampus may be the
earliest
affected cortical structure with AD progression, its depth within the brain
results in lower
SNR due to distance from the MR sensing coil. Texture within the neocortex
provides a
target for assessment of dementia and other brain pathology that, due to its
proximity to the
skull, offers higher SNR. In the healthy brain, very ordered neuronal
architecture is found in
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the neocortex. The neurons form in bundles of approximately 50microns in width
and 80
micron spacing, with about 80 to 100 myelinated neurons grouped together in
each bundle.
This is the minicolumn organization visible in histology of neocortical
tissue. In specific
regions of the brain that are seen by histology studies to be affected early
in AD
progression, this columnar order loses coherence over the prodromal stages.
These changes
happen in advance of general brain atrophy, an often-used marker for AD
progression,
making them a better target for early stage diagnosis. Further, the temporal
progression of
minicolumn thinning and loss of coherence across specific brain regions
reflects the
regionally selective progression of tangle pathology in Alzheimer's Disease
(AD). Hence,
tracking the changes in cortical minicolumns spatially in the brain using the
embodiments
disclosed enables typing of dementia, as each form of dementia follows a
specific spatial
progression through the brain.
[PARA 3711 The structure of these minicolumns in healthy brain can be seen in
FIG. 27, a
histology image stained to reveal myelin 2702¨the coating sheathing the
neurons. FIGs.
29A ¨ 29C are a series of three histology images stained to reveal the
pyramidal neuron
cells in the bundles. FIG. 29A is of neuronal order in healthy brain and FIGs.
29B and 29C
show progressive pathology with AD advancement¨the columnar spacing shrinks
and the
ordered structure becomes increasingly random.
[PARA 3721 Changes in the spacing and order of the minicolumns in these
cortical
regions are early harbingers of disease. Though research in this area is in
early stages, as
with other pathology, the underlying tissue changes must predate symptoms. The
problem is
that currently there is no technique able to reach the resolution needed to
assess these early-
stage changes in the columnar texture. The embodiments disclosed enables this
measurement.
[PARA 3731 There are several methods by which the embodiments disclosed can be
implemented to measure the change in spacing and order of neocortical
minicolumns. For
this measurement to reflect early stage changes, it would be applied to the
regions of the
neocortex that appear to affect behavior earliest in the onset of AD, such as
the temporal
cortex. As these regions are in the neocortex on the outside of the brain,
they will yield a
strong SNR in a brain coil. FIG. 28 is a representation showing possible
positioning of the
VOI 2802a, 2802b, 2802c and 2802d in the neocortex 2804.
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[PARA 3741 As the axonal component of the neurons forming the minicolumn
bundles are
sheathed in myelin, a fatty substance, Ti contrast can be used to highlight
the axon bundles
against the background tissue and hence is a good choice for contrast when
assessing these
structures.
[PARA 3751 A difficulty in measuring the columnar spacing results from the
semi-
crystallinity of these structures. In healthy neocortex, they are highly
ordered in the vertical
(parallel to columns) direction. As a result, because the acquisition axis of
the VOI must
intercept several of these columns to make a measurement, the differential
signal that
contrasts the neuronal bundle from the background tissue is extremely
sensitive to
orientation of the acquisition axis. To measure the columnar spacing, the
acquisition axis of
the VOI is aligned normal to the length of the columns. Slight misalignment
diminishes
contrast¨ precise orientation is required for the measurement. To achieve
proper
alignment, rocking the acquisition gradient angle over incremental angles of
approximately
a degree or two will show a signal resonance at the proper alignment i.e.
consecutively
repeating the time varying series of gradients to produce trajectories through
3D k-space
with resulting k-value sets oriented around the specific k-value to locate
resonance. (The
slight curvature of the cortex would be expected to provide a finite width to
this resonance
of signal amplitude vs. angle.) A trajectory through 3D k-space and a
resulting k-value set
oriented within 10 degrees of the initially encoded specific k-value is
employed in
exemplary embodiments. As the columnar structure loses coherence with
pathology
advancement, the width of this resonance would be expected to broaden, and
eventually
disappear when the structure becomes highly random, as shown in the histology
image in
FIG. 29C.
[PARA 3761 Using the embodiments disclosed, acquisition of signal can be at a
(nominally) single k-value, or over a band of k-values defining in advance of
t the measure
a finite extent in k-space over which to acquire the signal.
[PARA 3771 As the spacing of minicolumns in healthy human brain is
approximately 80
microns, sampling from about 70 microns to 110 microns would encompass the
resonance.
In exemplary embodiments this is accomplished by placing the VOI within the
cortex and
providing the spatial encode in the range of k-values corresponding to spatial
wavelengths
of 40 micrometers to 200 micrometers.
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[PARA 3781 Making measurements in the cortex with the embodiments disclosed
involves these basic steps:1) A contrast mechanism is selected to highlight
the structure. 2)
individual k-values or the span of k-values for which signal is to be acquired
is determined.
3) the timing of the k-value acquisitions¨ how many repeats at each k-value or
spread of k-
values is determined. 4) the size of the VOI and orientation(s) of the
acquisition axis in the
neocortical tissue are selected.
5) A VOI is positioned in the center of the cortex height, aligning the
acquisition axis
parallel to the top and bottom surfaces at the VOI midpoint, as closely as
possible. 6)
Signal vs. k-value data is then acquired with the gradient on or off to
measure the
minicolumn spacing; measurement across a broad range of k-values encompassing
on the
average spacing of the minicolumns as indicated in the literature
(approximately 80um) will
ensure coverage of the width distribution. Signal intensity maximum should
occur when the
acquisition gradient is oriented normal to the columns. 6) The acquisition
gradient is then
rocked in small angular increments to look for the signal resonance¨the
sharpness of the
signal resonance vs. angular deviation reflects the order of the minicolumns.
A sharp
resonance indicates ordered structure. A broad resonance as a function of
angular deviation
indicates columnar degradation has introduced randomness into the minicolumn
order.
7) Aligning the gradient to maximum signal return to sweep through the range
of k-values
look for textural wavelength resonances¨i.e. the peak of the signal vs. k-
value distribution
from the texture distribution. This resonance also can be used to determine
minicolumn
order. A sharp peak (high q-value) in the signal vs. k-value curve indicates
ordered
structure, the broadness of the curve is indicative of the degree of loss of
order. Locating the
resonance in the signal vs. acquisition angle and in the signal vs. k-value
distribution can be
accomplished as an interactive process.
[PARA 3791 Data can be acquired at other positions in the cortex or nearby the
original
VOI, either within one TR or multiple TRs. Optimal VOI dimensions for
characterization of
the cortical minicolumns are determined by 1) the need to fit the VOI entirely
within the
cortex, which is 2-3 mm in height, 2) the requirement to sample sufficient
textural repeats
along the encode axis for accurate assessment of the textural wavelength, and
3) by signal
requirements. Additionally, the
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smaller the height of the VOI along the direction of the columns determines
the sensitivity
to alignment.
[PARA 3801 The fewer repeats sampled along the encode axis, the greater the
broadening
in k-space of the signal acquired by the embodiments disclosed. Data can be
acquired with
the gradient off, relying on selection of the window width to determine the
range of k-
values contributing to the signal. Keeping the spread in k-space small enough
ensures
correlation in the signal output.
[PARA 3811 A change in spacing of neuronal columns indicates pathology
advancement
/aging; this can be determined by longitudinal monitoring of the peak of the
signal-
magnitude vs. K- value distribution.
[PARA 3821 As the structure degrades, the orientation resonance becomes
broader and
more diffuse, spread over a larger span of acquisition angles. Also as the
structure degrades,
the peak in the signal vs. k-value distribution becomes broader and more
diffuse, spread
over a larger span of k-values (wavelengths). Eventually, no peak will be
visible in either
case with progressing degradation of the minicolumn order, a marker of
increasing degree
of dementia. Further, changes in the minicolumns widths with advancing disease
will be
reflected in the column spacing, another marker of pathology.
[PARA 3831 A variation on this disease marker is the degree of anisotropy of
the
columnar order. As the columnar order degrades with progressing pathology, the
degree of
anisotropy of the columnar texture also lessens and the overall cortical
tissue texture
becomes more isotropic. The degree of anisotropy can be measured by use of Ti,
or other,
contrast using the embodiments disclosed, with the VOI 3002 positioned as
above, midway
between the two cortical surfaces 3004 as seen in FIG. 30 and comparing the
signal vs k-
value distribution with the acquisition axis normal to the cortical surfaces
(parallel to the
minicolumns), with the signal vs. k-value distribution obtained with the
acquisition axis
aligned tangential to the cortical surfaces 3004 therefore (normal to the
minicolumns) as
shown in FIG. 30.
[PARA 3841 The use of diffusion weighting in brain pathology, including stroke
and brain
tumors, is increasing. Diffusion weighted imaging (DWI) provides an indirect
measure of
structure at the cellular level, by applying gradients that first dephase and
then rephase
signal in a targeted location.
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[PARA 3851 Stationary water molecules are rephased by the second gradient, but
those
that have moved between the two gradients are not, hence yielding no signal.
The difficulty
with the technique is that, by design, it is extremely sensitive to motion. It
is also low SNR,
due to the late echo time resulting from the need for the long diffusion
gradients. Use of the
embodiments disclosed for data acquisition when using diffusion contrast can
remedy the
motion problem as, though the echo time is still long, data acquisition is
fast enough that the
signal loss and blurring due to motion is minimized. The embodiments disclosed
can be
used with diffusion weighting contrast to assess the spacing and
order/randomness of the
minicolumns. This measure can be made with the diffusion gradients applied
parallel to the
surfaces of the cortex (normal to the minicolumns), and then normal to the
cortical surfaces
(parallel to the minicolumn direction). These two measures enable assessment
of the
anisotropy, which will be highest in healthy brain, pathology then inducing
increasing
isotropy as the columns degrade.
[PARA 3861 Using diffusion weighting, with reference to FIG. 30: applying the
gradient
as indicated by Gradient 1 3006 will yield low signal if minicolumns still
intact. Similarly
applying gradient as indidcat4d by Gradient 2 3008 will yield high diffusion
signal if
minicolumns still intact.
[PARA 3871 As minicolumns degrade, signal with the two different gradients
approach
each other expect diffusion weighted signal to increase overall as minicolumns
become
highly degraded.
[PARA 3881 A refinement on this measure is through application of the
diffusion gradient
in multiple directions for data acquisition and development of the diffusion
tensor similarly
to diffusion tensor imaging (DTI), but with data acquisition being by the
embodiments
disclosed. Development of a diffusion tensor requires using at least 6 non-
collinear
directions of diffusion gradient orientation to yield sufficient data to
generate the tensor, the
eigenvalues of which determine the level of Fractional Anisotropy (FA) in the
cortex,
reflective of the order of the minicolumns. The FA should change, moving
toward more
isotropic organization as columnar organization degrades¨an FA value of 1
indicates
highest anisotropy, and a value of 0 indicates maximum isotropy of the
underlying
diffusion, hence revealing the order of the columnar texture.
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[PARA 3891 As with the other types of contrast mechanisms used in conjunction
with the
embodiments disclosed, the targeted k-values are selected by a combination of
knowledge
of the approximate location in k-space of the minicolumns from the literature
and from
measure when they are still sufficiently ordered to define a clear textural
wavelength
signature, and pre-measure to determine the distribution of signal vs. k-
value. One method
to achieve this is with a gradient on to provide sufficient spread in k-space
during data
acquisition to enable finer sampling on the subsequent acquisition(s), though
this measure
can also be made using gradient off acquisition.
[PARA 3901 In the cortex, the mean diffusivity (MD) of water is found to
decrease with
increasing dementia. Using the embodiments disclosed it can be determine if
this is due to
minicolumn disorder by measuring the spacing, order, and anisotropy of the
minicolumns as
described above. The signal vs. k-value data obtained using the embodiments
disclosed, can
be input into a machine learning algorithm, with correlational data from
cognitive
evaluation tests such as the MMSE exam, downstream neuropathology outcomes,
and
serum and imaging data.
[PARA 3911 In addition to Alzheimer's dementia, changes in, or abnormal
morphology in,
minicolumn structures occur with Parkinson's disease, Dementia with Lewy
bodies,
Amyotrophic Lateral Sclerosis, autism (autism spectrum disorder is reflected
in wider,
hence more tightly packed, minicolumns), and schizophrenia (for which the
normal thinning
of the columns with age does not appear to take place, leading to more widely
spaced
minicolumns), dyslexia and ADHD. Hence, the embodiments disclosed can be used
to
assess degree of pathology in any of these conditions. Correlation for machine
learning to
determine the association between measured data and pathology can be obtained
from
cortical atrophy segmentation, MMSE, doctor's evaluation of degree of
pathology from
observational data, etc.
[PARA 3921 Additionally, the embodiments disclosed can be used with stationary
contrast
mechanism to highlight tissue texture changes and flow contrast to highlight
vasculature
changes in the vicinity of a lesion showing up on an MR image, that may
indicate stroke or
tumor related pathology.
[PARA 3931 Studies indicate that gray matter is affected earlier than white
matter with
dementia onset and progression. The cortical structures found to be earliest
to suffer
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degradation are the hippocampus and entorhinal cortex, pathology which leads
to memory
loss and disorientation. Recent research applying image processing to Ti-
weighted MR
images of the brain has indicated a statistically significant correspondence
between textural
features on images of the hippocampus and MMSE (Mini Mental State Exam)
scores. The
texture is not directly measurable with MR imaging, due to insufficient
resolution, but
image analysis metrics correlate specific textural gradations with glucose
uptake reduction
in the hippocampus and subsequent hippocampal shrinkage, a marker for AD, in
addition to
the correlations with reduced MMSE scores. These texture features are not
discernible
except as a result of image processing, and their source is not known.
Research indicates
that these textural changes precede cognitive decline, and track with symptom
onset. Thus,
the hippocampus is a good target for application of the embodiments disclosed
of texture
measurement for pathology assessment.
[PARA 3941 As the exact etiology of dementia within the hippocampus and
entorhinal
cortex is unknown, the embodiments disclosed will be used to gather a
sufficiently complete
data set to provide detailed assessment of tissue texture within the
organ¨either the
hippocampus or the entorhinal cortex. Both textural wavelength content and
variability, as
well as orientation and location dependence will be measured. Signal
acquisition across a
range of k-values, in at least 3 (orthogonal) directions, using a plurality of
contrast methods
(as the origin of the texture is unknown), is requisite to well-characterize
the texture.
Acquisition of data across the organ, by defining VOI dimensions to enable
fitting the VOI
fit into the organ entirely at different locations will enable determination
of the spatial
variability of the texture. By acquiring signal data across a range in k-space
corresponding
to wavelengths of ten microns out to about 1-2 mm ensures that a large variety
of textural
signals contribute to the information content of the measurement. The
embodiments
disclosed can be used in conjunction with any contrast mechanism, such as
inversion
recovery, a heightened form of Ti weighting, or diffusion weighting.
[PARA 3951 The predictive value of the novel biomarker provided by the
textural data
acquired by the embodiments disclosed, towards assessment of the degree of AD
pathology,
can be defined by correlation with a range of diagnostic information from the
same patient.
The main correlation marker will be drawn from patient outcomes¨i.e.
definitive diagnosis
of AD or other dementia¨as this has the highest diagnostic information
content, though
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definitive diagnosis is well-downstream from the pathology we are assessing.
Additional
correlation will be drawn from patient MRI imaging data on hippocampal
shrinkage, a
proven, and continuous, marker of advancing AD (as well as other forms of
dementia). This
correlation will be made longitudinally with disease progression, if possible.
Again, changes
in tissue texture in the hippocampus are expected to predate noticeable
cognitive decline,
and measurable change in volume via MRI. A third correlative marker is FDG-
PET, as
decline in glucose metabolism is expected to occur early relatively in disease
progression.
As a fourth correlative biomarker, the MMSE (Mini Mental State Exam) provides
longitudinal data on cognitive functioning and decline. Genetic predilection
for AD
provides an additional marker for correlation with the textural measure
acquired by the
embodiments disclosed. While the previous markers provide downstream
correlative value
(on the outcome side), genetic markers exist in advance of any pathology
development.
Correlation of this varied set of biomarkers with the data acquired by the
embodiments
disclosed in the hippocampus and entorhinal cortex, across a broad range of
patients, will
enable a clear definition of diagnostic content of the use of the embodiments
disclosed for
early stage prediction of AD pathology.
[PARA 3961 Current machine learning algorithms are capable of pathology level
classification of non-specific features, as will be obtained from MR data
acquisition by the
embodiments disclosed. As such, the disclosed sources of correlational data
above will be
input into machine learning algorithms to highlight the correlation with
textural features and
disease.
[PARA 3971 Autism Spectrum Disorders (ASD) as a disease class are diagnosed
solely on
the basis of behavior. However, much research has reported anatomical
difference in the
brains of people with ASD. The ability to measure these anatomical variations
in vivo
would enable adding pathology-based diagnosis to that based solely on
behavior, and would
inform an understanding of the underlying etiology of this condition.
[PARA 3981 Along with an enlargement of the frontal cortex, alterations in the
columnar
organization of neurons in various regions of the cerebral cortex are found to
be attendant
with autism. The neuronal tracts that span the middle of the cortex form into
bundles of
approximately 80 neurons, spaced on the order of 50 microns apart, forming a
columnar
organization, the columns running perpendicular to the cortical surfaces. This
organization
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has been shown in numerous histology studies to vary in ASD relative to that
seen with
normal development. As previously discussed the thinning and degradation of
order in these
minicolumns occurs with advancing AD. In ASD, minicolumn width has been found
to be
greater than in normal brains, reducing columnar spacing, and increasing the
neuronal cell
density. This change in columnar width varies spatially throughout the
cortex¨these
variations have been found in some research to be limited to higher order
association areas,
and to spare primary sensory areas, these local differences mirroring
cognitive symptoms.
More work is needed to determine the etiology and correlate pathology with
symptoms, as
ASD appears to be highly heterogeneous.
[PARA 3991 In ASD minicolumns have been found to be on the order of 5% to 10%
wider than in the normal brain. While not a huge difference, across the
hundreds of
thousands of minicolumns in the brain, this variation contributes to a
significant difference
in brain organization. Most significantly, this variation in density should be
resolvable by
the embodiments disclosed.
[PARA 4001 Structural change can be measured with the embodiments disclosed
through
use of contrast that will highlight the columnar organization against the
background of
cortical soft tissue. The most pertinent contrast here would be a fat-
highlighting contrast
such as Ti or IR, to reveal the lipids that sheath the axonal tracts issuing
from the neurons.
This contrast may diminish with advancing pathology as the degradation in
minicolumn
order could be attendant with degradation of the myelin axonal sheaths.
Casanova and
Trippe, "Radial cytoarchitecture and patterns of cortical connectivity in
autism,
Philosophical Transactions Royal Society B", 2009; Chance and Casanova,
Minicolumns,
"autism and age: What it means for people with autism", Autism Science
Foundation,
August 2015; Donovan and Basson, "The neuroanatomy of autism¨a developmental
perspective", Journal of Anatomy, 2017; McKavanagh, et al., "Wider minicolumns
in
autism: a neural basis for altered processing", Brain, July 2015; Opris and
Casanova,
"Prefrontal cortical minicolumn: from executive control to disrupted cognitive
processing",
Brain, 2014.
[PARA 4011 Another contrast mechanism that is applicable to tracking the
variation in
columnar order is diffusion weighting. This contrast mechanism has been
discussed in some
detail above, towards its application in AD diagnosis. Diffusion weighting
measures local
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variations in the diffusion coefficient of water, these variations reflecting
tissue changes at
the microscopic level. In its use in imaging, high gradients must be applied
for many
milliseconds, to allow enough time to create a sufficient dynamic range in the
measure of
diffusion coefficients. This makes for a long echo time and hence a low SNR
due to T2
dephasing. As such, in imaging the minimal voxel sizes are on the order of 2-
2.5mm on a
side to enable sufficient SNR for the measurement.
[PARA 4021 Though the underlying phenomena leading to the change in diffusion
coefficients are on the microscopic scale their resolution is limited in
imaging, the size of
the voxel required to generate sufficient SNR resulting in partial volume
effects. Further,
the technique is indirect in that the underlying mechanism responsible for the
change in
diffusion coefficient, which is averaged over the voxel, is unknown.
[PARA 4031 Use of diffusion weighting as contrast, in conjunction with the
embodiments
disclosed, would enable direct connection between changes in very fine tissue
textures and
the variation in diffusion coefficient with pathology. By limiting the
textural wavelength(s)
(k-values) acquired at each TR, the acquisition would provide sufficient time
to acquire
many repeats at the reduced set of k-values to enhance SNR. If it is desired
to probe the
power at multiple wavelengths/k-values, the measure of a reduced set can be
repeated in
successive TRs. Again, phase coherence between these separate measures of k-
value is not
required. The only requirement here is that the VOI remain in a tissue region
with similar
textural signature. In regions where the tissue texture changes rapidly
spatially, and/or in
measurements for which non-negligible motion is expected, motion correction
schemes can
be used to relocate successive VOIs within the tissue region of interest for
each successive
measurement.
[PARA 4041 A further use of diffusion weighting for contrast, to reveal
changes at the
level of fine tissue textures, is to use the DTI (Diffusion Tensor Imaging)
scheme. In this
method, the diffusion gradients are applied in at least six non-collinear
directions, enabling
determination of the directional diffusion field, to determine the measure of
anisotropy in
water diffusion indicative of a preferential direction/tissue change.
[PARA 4051 In schizophrenia, reduced neuronal density is seen in the cortex,
in the left
hemisphere in females and in the right hemisphere in males. This reduced
density manifests
as enlarged minicolumn spacing, and is attendant with absence of normal aging
effects in
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the columnar organization in the cortex. It is reasonable to assume that many
neuropathologies dealing with altered cognition, consciousness, and
functioning would
exhibit similarly attendant alterations in the cortical columnar order, the
brain region
suffering the attack varying with, and mirroring, the specific disease
presentation. Chance et
al., "Auditory cortex asymmetry, altered minicolumn spacing and absence of
ageing effects
in schizophrenia", Brain 2008 .
[PARA 4061 For measure of cortical minicolumnar order and spacing to track
pathology,
positioning of the VOI is important so as to generate highest contrast between
the columns
and the background tissue. The VOI is first positioned in the cortical region
of interest in the
brain, based on where pathology is expected to strike during disease
progression. Next,
based on what is known of healthy columnar spacing and order, a VOI length,
the
acquisition axis, would be selected long enough that it would allow sampling
of several
columnar repeats. To ensure high contrast from column to background, the
acquisition axis
of the VOI should be positioned as close to parallel to the cortical surfaces
as possible,
resulting in the acquisition axis intersecting the column as close as possible
to normal. This
is difficult to do just by use of the reference image. The optimal method
would be to first
select the region of k-space of interest, a band of values peaked near the
healthy
minicolumnar spacing. Next, position the VOI parallel to the cortical
surfaces. Then, best
alignment can be accomplished by varying the angle of the tilt of the
acquisition axis by a
few degrees on either side of the selected orientation, in steps of about a
degree, looking for
the signal maximum within the selected band in k-space.
[PARA 4071 The cross section of the VOI can be chosen to be on the order of a
few
repeats of the healthy columnar spacing¨smaller cross section favors higher
contrast, but
also reduces signal. The spread in k-space can be set either by acquisition
with a gradient on
to broaden the acquisition in k-space over the time of the measurement, or
with stepped k-
values, by windowing in sample space. The former procedure is preferred as it
allows
sampling of a larger number of textural repeats to better determine where the
power of
interest lies. This measure can be repeated across different ranges in k-space
to determine
the k-space region on which to focus for the diagnostic measure.
[PARA 4081 To optimize the capability of the embodiments disclosed for
diagnosis of
autism and assessment of the level of differentiation of the cortical tissue
morphology from
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normal cortical tissue, machine learning would be applied. Inputs to such
algorithms would
be drawn from a multiplicity of diagnostic measures. Data acquired using the
embodiments
disclosed, with selected contrast and acquisition parameters, would be
combined with
patient histories, cognitive testing, exam results, imaging data, and outcomes
to be analyzed
via AI/machine learning algorithms.
[PARA 4091 MS is a chronic, inflammatory disease of the central nervous system
characterized by immune-mediated demyelination of nerves. It is a leading
cause of
neurologic disability worldwide. Typically, a disease of young adults, the
number of cases
peaks in the fourth decade of life. Compounding the difficulty of
understanding the disease
triggers is the fact that disease course varies considerably from patient to
patient. There is
"relapsing-remitting" course with "clinically isolated syndrome"
presentation¨an incidence
of cognitive symptoms lasting at least 24 hours, "secondary progressive", and
"primary
progressive" courses of the disease. A rare variant is the "progressive
relapsing" course
which presents with progressive course with acute relapses. T2 hyperintense
lesions, a
hallmark of MS, are incidental findings on an MRI exam in an asymptomatic
individual. A.
Katdare and M. Ursekar, "Systematic Imaging Review: Multiple Sclerosis",
Annals of the
Indian Academy of Neurology, July 2015 . What is needed in MS is a diagnostic
capability
that will enable prognosis of developing pathology towards therapy selection,
as well as a
sensitive measure of therapy efficacy. Such measure, though, is complicated by
the various
presentations, and a lack of clear understanding of the various pathology
courses underlying
disability.
[PARA 4101 A growing body of evidence indicates that early intervention is
required in
MS to minimize the risk of permanent neurologic damage, which is often
greatest early in
the disease course. More sensitive measures of disease progression are needed
to help
predict the course of the disease and assess therapy efficacy. A more
sensitive measure of
very early stage disease is clinically necessitated. F. Piehl, "Multiple
Sclerosis¨A tuning
fork still required", JAMA Neurology, March 2017.
[PARA 4111 Magnetic resonance imaging has become a major diagnostic and
research
tool in the study of MS. Advanced imaging techniques help provide a more
accurate
characterization of tissue injury, including demyelination, axonal injury, and
its functional
and metabolic consequences. The common basis for MS diagnosis is the
dissemination of
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lesions in "time and space" in the brain, brain stem, and spinal cord. Gd
(Gadolinium) has
been used to enhance lesions in past, but recent findings of long term
retention of Gd in the
brain of patients has questioned the safety of its use. Alternatives to this
contrast agent are
being sought, as are alternative methods of highlighting lesions.
Additionally, though tied to
a specific temporal variability, white matter lesion load has only a poor
correlation with
symptoms of disability. This is thought to result from the presence of both
focal and diffuse
damage appears in the brains of people diagnosed with MS. A method of
assessing the
diffuse tissue damage, as well as assessing the tissue changes within lesions,
is required to
better understand the underlying pathology, towards prognosis of cognitive
change, and to
sensitively monitor therapy response. However, the imaging resolution needed
to measure
the underlying tissue damage within and surrounding lesions is not directly
available with
standard MR imaging, due to patient motion over the course of data
acquisition. Though
cerebral MRI has progressed the aim of quantifying MS-induced tissue changes,
from WM
lesion assessment to whole brain microstructural changes, the currently
available MRI
metrics still provide no clear explanation for, or diagnosis of pathology,
either on a
population-as-a-whole, or on an individual basis. Clearly more diagnostic
information is
needed.
[PARA 4121 A good portion of the MRI market results from the need for
diagnostics for
nerve and brain disorders. "MRI Market Primed for Growth", Aunt Minnie Europe,
2/27/2017. More than that, the multitude of contrast mechanisms available for
MR scanning
enable acquisition of complementary data from within one modality, to provide
a more
nuanced read of underlying pathology. What has been missing in this, however,
is direct
structural measure at the level of the tissue texture, the biologic fabric
that, in most
pathologies, responds immediately to the chemical changes that drive disease.
In some
diseases, the chemical changes that drive pathology are known and measurable
but, in
others, the earliest measurable change is in the structural fabric of the
tissue. However, this
change is not measurable by imaging due to the resolution limits set by
patient motion.
Therefore, though MRI offers exquisite tissue contrast, it cannot resolve the
sub-mm diffuse
tissue changes that are the early harbingers of disease.
[PARA 4131 In past, MS was characterized as a disease of WM (White Matter)
tracts and
the CNS (Central Nervous System) with the focus on demyelination of the axonal
tracts that
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carry signals from cortical grey matter to other parts of the brain. However,
many recent
imaging and pathology studies point to clear involvement of the grey matter.
Cortical
demyelination, which is seen to be present in early stage MS, may be a
pathologic correlate
of irreversible disability. Gray matter demyelination is extensive in most MS
patients, and
clear association between the lesion load in grey matter and cognitive
impairment has been
made. However, detection of GM lesions is difficult with standard MR contrast
techniques,
hence correlation with clinical symptoms remains problematic. Wegner and
Stadelmann,
"Grey Matter Pathology and Multiple Sclerosis", Current Neurological and
Neuroscience
Reports, 2009; Popescu and Lucchinetti, "Meningeal and Cortical Grey Matter
Pathology in
Multiple Sclerosis", BMC Neurology 2012; A. Katdare and M. Ursekar,
"Systematic
Imaging Review: Multiple Sclerosis", Annals of the Indian Academy of
Neurology, July
2015.. Further, though conventional MR sequences, specifically Ti, T2, and Ti
with Gd
contrast, are sensitive for detecting WM lesions throughout the CNS, patient
motion limits
their ability to assess the underlying tissue damage within and outside the
lesions. The
diagnostic information that is missing here is the underlying changes in the
tissue texture
within the brain¨both in regions of WM tracts and in the cortical regions (GM)
occurring
as a result of disease. Due to its immunity to patient motion, the embodiments
disclosed can
provide this measure.
[PARA 4141 MR techniques that generate contrast originating at a molecular and
cellular
level have been applied to the problem of developing a more complete
understanding of the
tissue changes underlying both lesions and the diffuse tissue damage inherent
with MS.
These non-conventional techniques, such as Magnetization Transfer Imaging
(MTI),
Diffusion-Weighted Imaging (DWI), and Diffusion Tensor Imaging (DTI) provide
an
indirect measure of the effects of disease-induced degradation of WM and GM.
The
difficulty with these measures is that, as they are indirect, changes in the
signal intensities
that they measure can arise from various pathology-related tissue
changes¨assigning a
specific underlying cause is problematic. DTI does a good job of neuronal
fiber tracking
through the brain, showing the macroscopic holes that appear in the tracts due
to pathology
advancement. But it cannot determine the exact source of these changes on the
level of fine
tissue texture¨i.e. the earlier and more sensitive measure of disease
progression. Lesions
are non-specific and may indicate areas of inflammation, demyelination,
ischemia, edema,
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cell loss, gliosis. A better understanding of the fine-scale pathologic
changes resulting in the
observed cognitive impact of MS is needed.
[PARA 4151 Though lesion size, number, and spatial and temporal distribution
are clear
contributors to disease progress evaluation, T2 weighted MRI lacks pathologic
specificity
and specific predictive capability.
[PARA 4161 WM pathology in MS is dominated by an inflammatory response leading
to
degradation of the myelin sheath surrounding the axons comprising the neuronal
tracts that
run through the brain. Direct measure of myelin loss at an early stage, before
the appearance
of well-defined lesions in the WM, would be a most useful diagnostic measure
towards
early treatment. Single axons are on the order of 1um, hence combined
degeneration is
required to produce a measureable signal. The marker sought is the decrease in
myelin
along the tracts as axonal degeneration progresses. The localized degradation
appears as a
WM lesion in Ti and T2 weighted images.
[PARA 4171 Due to the complexity of MS pathology, a single diagnostic measure
would
not be expected to completely unravel the underlying causes of pathology onset
and
progression. The ability of computers to store and process ever larger data
sets has enabled
complex image processing and interpretation, using multiple measures of image
data
obtained under conditions of different contrast, from a single patient.
Further, computation
capability has made it possible to more accurately derive biomarkers from new
MR
diagnostic measures, through use of machine learning algorithms that correlate
data
obtained by differing MR contrast, differing modalities, combined with patient
metadata
and outcomes, across an entire population.
[PARA 4181 Along with the needed development of more sensitive/specific MRI
diagnostic approaches that can provide earlier diagnosis, improved post-
processing is
needed to maximize information extraction. Bakshi et al., "MRI in multiple
sclerosis:
current status and future prospects", Lancet Neurology, July 2008.
[PARA 4191 Combination of diagnostic information has the best chance of
revealing
underlying pathology in MS, especially given the ability to combine these
measurements
using AI/machine learning/deep learning algorithms. However, though the
correlation of
data is more powerful than single measures, high information content inputs to
the
algorithm drive its sensitivity and efficacy. In its ability to directly
measure the very fine
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textural changes in affected brain tissue, the embodiments disclosed provides
a key piece of
data towards enabling sensitive measure of MS progression. Further, it
provides a high-
information-content measure of textural morphology, which can be applied: 1)
as a
correlational measure towards calibration of other indirect MRI pulse
sequences/contrast
methods, such as DWI and MTI, or 2) it can be run in an integrated mode with
any specific
contrast mechanism selected to provide highest tissue contrast, the MR data
being acquired
using the embodiments disclosed with the tissue contrast being provided by
whatever
contrast mechanism provides optimal contrast. This may be standard contrast
such as such
as Ti and T2-weighting, or more advanced measures such as DWI or MTI. (FIGs.
24 and
25 as discussed above, demonstrate an integrated DWI/texture pulse sequence).
[PARA 4201 Inflammation in MS causes both demyelination of neuronal tracts and
axonal
injury. Brain atrophy follows and is reflected in cortical thinning, which can
be measured in
MRI using post acquisition segmentation. However, by the time cortical atrophy
is
measurable, significant neuronal damage has already occurred in the cortex,
atrophy being
the macroscopic mirror of integrated tissue damage. Direct measure of tissue
change/myelin
loss at an early stage, before the appearance of measurable atrophy or well-
defined lesions,
is needed as a diagnostic measure enabling early treatment. Fox et al.,
"Advanced MRI in
Multiple Sclerosis: Current status and future challenges", Neurologic Clinics,
May 2011 .
[PARA 4211 Single axons are on the order of 1 um, hence combined degeneration
is
required to produce a measureable signal. However, in the cortex, neurons
bundle together
in groups of about 80, enabling a sufficiently sensitive measure of tissue
change to track the
degeneration underlying cortical thinning. This is largely equivalent to the
cortical damage
inherent in progressing Alzheimer's disease. Degeneration in these columns, as
indicated by
lack of order, and degrading myelin, is one of the early markers of MS.
[PARA 4221 Another contrast method that has been applied in an attempt to
quantify
myelin degradation is Magnetization Transfer Imaging (MTI). Water bound in
macromolecules such as myelin decay too quickly to allow recording of their MR
signal,
hence measure of signal strength directly is not possible. However, due to
dipole
interactions between free water in the brain and bound hydrogen protons,
changes in the
ratio of the free and bound pool of protons can be measured. Though this is a
sensitive
measure of change of water content, as with difffusion imaging, the underlying
mechanism
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causing the change can be ascribed to multiple pathologic phenomena. MTR
associated with
myelin content but, as it is also affected by water content, from say edema
and
inflammation, you can't tell exactly what is causing the change in MTR. MTR
changes by
both changes in the free hydrogen pool (water content) and the bound hydrogen
pool (e.g.
those bound to proteins and lipids in cellular membranes such as in myelin).
Comparison
with histopathology has shown reasonably good correlations with both
demyelination and
remyelination, and with overall neuronal density. Therefore, association of
changes in MTR
as reflecting myelin content is not at all straightforward. This is the power
of direct
structural measures¨the interpretation relies only on the contrast mechanism.
Vavasour et
al., "Is the Magnetization Transfer Ratio a marker for myelin in Multiple
Sclerosis", Journal
of Magnetic Resonance Imaging, 2011.
[PARA 4231 As with the incorporation of the embodiments disclosed and
diffusion
weighting within one pulse sequence, the embodiments disclosed can be combined
directly
with MTI contrast, to highlight structures whose contrast arises from
variations in water
content.
[PARA 4241 DWI and DTI yield different insights, though both measure the
microscopic
Brownian motion, or diffusion, of water molecules, which is hindered by
cellular structures
and changes with pathology. In healthy axonal tracts water diffuses
preferentially along the
tract, but as inflammation induces axonal degeneration, water diffusion
becomes more
isotropic. The degeneration of these tracts is reflected in the change in the
preferred
direction for water diffusion at the cellular level, or Fractional Anisotropy
(FA) as measured
by DTI. DWI measures the Mean Diffusivity (MD), regardless of direction. In
general, low-
fractional anisotropy (FA) and high-mean diffusivity (MD) are found in MS
lesions, but
values are highly heterogeneous. However, these measures are inferred¨i.e. it
is necessary
to posit a cellular level mechanism for the observed decrease in diffusion
magnitude and
directionality. For example. changes in diffusion can be due to inflammation
or edema, or
byproducts of myelin degradation.
[PARA 4251 Another method to assess demyelination is by multi-echo recording,
which
allows measure of T2 relaxation, a figure which reflects water/fat content.
This is known as
T2 relaxometry, and when used in integrated form with the embodiments
disclosed enables
measure of tissue structure differentiated by water content.
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[PARA 4261 An exemplary MS assessment would include visualization of lesions
in T2
and Ti weighting¨dissemination in space and time. This would be a longitudinal
record.
Combining different MR-based measures, sensitive to different aspects of MS
pathology to
increase understanding of the mechanism underlying accumulation of
irreversible disability.
Employ perfusion MRI to quantify cerebral blood flow and volume; multi-channel
receiver
coils to examine the interior of lesions. Brain atrophy measures are employed
for measuring
cerebral volume changes and look at correlation with WM tract damage.
[PARA 4271 Machine learning algorithms may then be applied to evaluate
combined data
from current diagnostics and data obtained by applying the embodiments
disclosed with
various contrasts and acquisition parameters¨structured or unstructured.
[PARA 4281 To accommodate pathology analysis for the examples provided, a
salient
feature of the embodiments disclosed is that they can be run in an integrated
pulse sequence
mode with other MRI sequences. The basic structure of the embodiments
disclosed¨data
acquisition across a sparsely-sampled k-space trajectory, at one tissue
location at a time¨is
operable in conjunction with most contrast-generating mechanisms.
[PARA 4291 Use of the embodiments disclosed with diffusion-weighting contrast
was
disclosed in FIGs. 24 and 25 which depict an integrated pulse sequence using
the
embodiments disclosed in conjunction with diffusion weighting, with two
different
positionings of the diffusion gradients within the sequence.
[PARA 4301 Such an integrated sequence could be repeated with the diffusion
gradient
applied along multiple axes, similarly to DTI (Diffusion Tensor Imaging). The
output
dataset would then allow development of a diffusion tensor, enabling
determination of the
FA (Fractional Anisotropy), a reflection of water flow pathways in the tissue
which reflect
cellular-level changes.
[PARA 431] Along with application of both DWI and DTI contrast towards typing
MS
lesions and determining underlying pathological tissue changes, a sequence
that enables
assessment of changes in fraction of bound and free water, MTI (Magnetization
Transfer
Imaging), can be used to help assess myelin destruction and regeneration in
progressive
pathology. The aim of the MTI technique is to track changes in bound water vs.
free water
in tissue. The T2 decay time of bound hydrogen protons is too fast to enable
direct signal
recording. Instead, selective RF excitation of the bound pool of protons is
done, resulting in
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subsequent excitation transfer to the free pool via dipole-dipole
interactions. This results in
saturation of the free pool and, hence, signal is reduced following subsequent
application of
a standard RF MRI pulse.
[PARA 4321 As with diffusion weighting, this is an indirect technique and
hence different
interpretations may fit the measured data¨water changes can be due to edema or
inflammation as well as demyelination.
[PARA 4331 One possible technique for pathology assessment is to apply the MTI
technique and then compare the image recorded to textural measure acquired in
the same
tissue region using the embodiments disclosed and a standard tissue contrast,
such as Ti or
T2 weighting. In this case, the embodiments disclosed could provide an
assessment of tissue
degradation, which could be used as a pathology-specific calibration of the
MTI technique.
However, a more powerful alternative would be an integrated pulse sequence, in
which the
embodiments disclosed would be used in conjunction with magnetization transfer
contrast¨i.e. would acquire high-resolution measure of texture with texture
contrast
between structures of differing bound and free water concentrations provided
by the MT
contrast. This integrated sequence technique could be applied in pathologies
for which there
is a clear differentiation between the type of water (free, or bound to
macromolecules)
associated with the measured textural elements. Pathology in many neurologic
diseases,
such as MS and AD, involves changes in the myelin coating neuronal structures.
Myelin
contains bound hydrogen protons; free water moves in as the myelin degrades.
Hence this
integrated sequence could provide a much-needed measure of such pathology.
And, unlike
direct application of the MT imaging sequence, the integrated pulse sequence
measure
applied to tissue informs a clearer understanding of the pathology-linked
changes in tissue
texture.
[PARA 4341 Another target for integrated pulse sequence acquisition would be
relaxometry. Information about pathology-linked changes in tissue can be
obtained through
measure of the decay time of the RF excitation¨either Ti, T2, or T2* ¨which
provides
information about the specific tissue environment. Hydrogen protons in
different chemical
environments exhibit different relaxation times. The most commonly used
figure, T2 decay
time, is dependent on spin-spin interactions. Variations in T2 relaxation can
thus be used to
discriminate among chemical environments in tissue, and can heighten tissue
contrast. For
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instance, as myelin decomposes in diseases such as AD and MS, free water moves
in. This
change is reflected as a change in the local T2 time constant. The T2 decay
constant is
obtained by acquiring data at various times following the spin echo (or even
in advance of
it) as the signal decays. In relaxometry, measurements obtained in different
voxels are often
mapped to form an image revealing spatial variations, often pathology-induced,
in T2 decay
time. This mapping is subject to the resolution limits set by patient motion
and SNR
considerations, and is not capable of resolving fine tissue textures. However,
by using a
pulse sequence to acquire data by the embodiments disclosed, repeating
measures at specific
k-values to track signal decay, an integrated measure of very fine tissue
texture can be
achieved, with T2 relaxation rate providing the tissue contrast. The basic
sequence would
then entail defining and exciting tissue in the VOI, applying a gradient pulse
to wind up to a
specific k-value (point in k-space), and measuring the signal at successive
times as the
signal decays. By this method, information is obtained on the chemical
environment of the
tissue textures contributing to that point in k-space¨i.e. the tissue
environment of textural
structures that repeat with the frequency associated with that k-value. For
instance, as water
has a specific T2, measure of T2 decay rate can be used to gauge changes in
the free water
content of the specific textural structures that contribute to signal power at
that k-value.
[PARA 4351 Alternatively, a range of points in k-space can be sampled in a
single TR, the
various k-values being measured in succession, and then the measure being
repeated
multiple times while the signal decays, to track the signal decay at each k-
value and enable
determination of k-value vs. T2.
[PARA 4361 T2 relaxometry in human brain has been successfully used to
differentiate
normal from abnormal tissues. Research studies have also demonstrated the
potential of
relaxometry for early breast cancer detection and monitoring of therapy
response. The
technique has also been used to identify abnormal breast tissue,
distinguishing adipose from
glandular tissue types by their distinctly different T2 values. Cameiro et
al., "MRI
Relaxometry: Methods and Applications", Brazilian Journal of Physics, March
2006.
[PARA 4371 This measure could be done similarly looking at T2* decay, by using
a
gradient echo sequence to form the echo.
[PARA 4381 Ti relaxometry can be done in integrated sequence combination with
the
embodiments disclosed, though multiple TRs are required to track the Ti decay
constant.
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This means that motion between acquisitions can lead to dephasing of the
measure.
However, as long as the VOI remains in a similar tissue environment, the
measure needed is
simple k-value vs. Ti. Further, if the tissue region under study changes
rapidly spatially,
real time motion correction can be used to ensure that the VOI is repositioned
at each TR to
stay in the same region of tissue. Again, the requirement here is only to
remain in the same
tissue region, not to maintain textural phase coherence.
[PARA 4391 In certain diseases, the embodiments disclosed can provide
complementary
data to improve pathology assessment when used in conjunction with MRS
(Magnetic
Resonance Spectroscopy). MRS is a noninvasive technique that enables detection
of
metabolites, naturally occurring biochemicals that are used in specific
metabolic activities.
Commonly measured metabolites are creatine, inositol, glucose, N-
acetylaspartate, and
alanine and lactate, the latter two being elevated in some tumors. MRS has
been used to
study relative changes in metabolites in brain tumors, as a result of strokes,
seizures, AD
progression, and depression, as well as being applied to study muscle changes
as a result of
pathology. Although an abundance of studies show metabolic changes in the
brain (and
muscles) in subjects with various diseases, at present MRS is little used in
the clinical
evaluation of subjects. This is partly due to a lack of standardized
methodology between
clinical sites and overlap of spectral patterns between different pathologies
(i.e., relative
lack of specificity). Water suppression techniques are required, usually
accomplished
through saturating the water protons, as the ratio of water to metabolites is
on the order of
10,000:1. Combining data obtained by the embodiments disclosed with MRS data
could
help in the calibration of the spectroscopic data and lead to a more powerful
diagnostic by
combination. This endeavor may bring high sensitivity and specificity
metabolic
information without biopsy. While MRI can locate a tumor, information from the
embodiments disclosed combined with MRS can reveal tumor aggressiveness and
type,
allowing therapy targeting and monitoring.
[PARA 4401 As one example, clinical studies have found increased myo-inositol
and
decreased N-acetylaspartate in the brain of patients with suspected
Alzheimer's disease, this
trend continuing with disease progression through MCI (Mild Cognitive
Impairment) into
full-blown AD. As such, correlation of MRS data obtained in the cortical
structures affected
in early disease with the change in tissue texture in the cortex as measured
using the
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embodiments disclosed, would strengthen the role of metabolite levels as a
marker for
pathology advancement, while developing a correlation between specific tissue
damage and
AD stage. Gao and Barker, "Various MRS application tools for Alzheimer's
Disease and
Mild Cognitive Impairment", American Journal of Neuroradiology, June 2014.
[PARA 4411 Research to date has shown that local metabolite levels can
indicate changes
from normal tissue, often correlating with pathology development. For
instance, measure of
metabolite signature spatially across a tumor can be analyzed to measure
tissue
heterogeneity, an indicator of tumor aggressiveness. The embodiments disclosed
can
provide measure of the spatial variation of angiogenic vasculature across that
same tumor to
correlate the degree of vascular density and disorder with the MRS metabolite
read.
[PARA 4421 The disclosed embodiments can be employed to look for heterogeneity
across a region and at changes in k-space power spectrum with reference to
normal
spectrum.
[PARA 4431 Another target for combining the embodiments disclosed with MRS is
for
clinical evaluation of tumor development. Tumor assessment is required for
typing and for
determining target volume for radiation therapy. Brain tumors exhibit markedly
different
MRS spectra from normal brain tissue. Further, tumor regions exhibit clear
metabolite
inhomogeneity, the spectrum from the necrotic core of a high-grade brain tumor
being quite
different from that from the actively growing rim. Peritumoral edema exhibits
a much
different metabolite complement than is found in a region of tumor invasion
into
surrounding brain tissue. MRSI (MRS Imaging) can be used to map out the
metabolite
variability in the region of a tissue. However, the limited spatial resolution
(about 1 cm3)
makes imaging of small tumor regions problematic due to the relatively large
voxel size and
to partial volume effects. While the clear variation in metabolites in tumor
regions point to
the potential for application of MRS, it has not been accepted as a routine
clinical tool.
[PARA 4441 While MRI is without doubt the most sensitive modality available
for the
detection of brain tumors, its specificity is low¨different tumor/lesion
types, can share a
similar MRI appearance. Determination of tumor grade, or differentiating
between
neoplastic and non-neoplastic lesions, is important, as high-grade brain
tumors need to be
treated more aggressively than low-grade tumors. If a lesion can be
confidently diagnosed
as non-neoplastic, an invasive brain biopsy procedure may be avoided and a
different
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treatment course, depending on the lesion etiology, may be considered.
Differentiation
between tumors and non-neoplastic lesions using conventional MRI may be
challenging.
While MRI is a sensitive technique for detection of brain lesions, the
specificity and
capability of conventional MRI for distinguishing between benign and malignant
lesions is
limited. Horska and Barker, "Imaging of Brain Tumors: MR Spectroscopy and
Metabolic
Imaging", Neuroimaging Clinics of North America, August 2010.
[PARA 4451 Data obtained with the embodiments disclosed can be combined with
that
obtained by MRI and MRS, to provide better specificity for pathology
assessment. The
embodiments disclosed, in their ability to measure fine structure, can provide
pertinent
information to assist in tumor typing, by assessing the vasculature within and
surrounding
the tumor to determine the degree of angiogenesis across the tumor from the
center out past
the periphery of the lesion as seen with MRI, part of the measure being
evaluation of extent
of the angiogenic vasculature into the surrounding tissue. In this way,
differentiation
between aggressive/non-aggressive tumors can be made with much better
certainty than can
be achieved using only MRS and MRI. The metabolite variation that arises from,
say,
edema, will be associated with a very different tissue texture, than would
that associated
with angiogenic vasculature. Embodiments disclosed measures angiogenic changes
across
tumor.
[PARA 4461 A typical workflow for application of an integrated pulse sequence,
combining the embodiments disclosed with additional novel contrast methods,
might be
accomplished as shown in FIG. 31.
[PARA 4471 Select a desired contrast needed by identifying tissue types of
interest, step
3102.
Reference images are then generated for localization in anatomy using standard
contrast
such as Ti, T2, T2*, IR, step 3104.
[PARA 4481 Apply the integrated pulse sequence with selected contrast, such as
DWI,
DTI, ASL, MTI, step 3106, and apply the embodiments disclosed for texture
characterization in a single acquisition sequence combining the localized,
sparsely sampled
in k-space, fast acquisition of the embodiments disclosed with the selected
contrast
generating mechanism, step 3108.
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[PARA 4491 The novel features of the embodiments disclosed that enable much
higher
resolution measurement than is achievable with MR imaging are 1) sampling at
targeted
spatial locations, the size of the sampled volume being determined by the
targeted
disease/pathology, and 2) selective sampling in k-space. In MR imaging,
acquisition is
across a large, spatially-encoded 3D volume, and encompasses continuous k-
space measure
from k = 0 up through to the highest k-value of interest. Such a measure
requires acquisition
of a very large data set over a temporal course long enough that patient
motion limits
feature resolution. By localizing the measure to one location in sample space,
and sampling
only highly selective regions in k-space, the embodiments disclosed provides
immunity to
patient motion, thus enabling a high resolution textural measure to be
acquired within one
TR. The measured textural data is similar to that acquired by biopsy, with
none of the
procedural risks. Further, the sampling errors inherent in biopsy/pathology
are obviated
because, using the embodiments disclosed, an entire organ can be covered with
individual,
motion-immune, localized measurements.
[PARA 4501 The k-space sampling required to adequately characterize tissue
texture
varies both with disease, and with the stage of pathology development. Though,
as with
many measurements, a basic knowledge of the disease can be used to select
acquisition
parameters, enhanced parameter optimization can be achieved through use of
"scout"
acquisitions, that provide general information on the distribution of power
through k-space
at a certain location. For instance, as the optimal length of the sampled
volume, the VOI
(Volume of Interest), varies with the textural wavelength(s) of interest in
the tissue under
study, an idea of the approximate range/values of interest in k-space would
help in selecting
VOI dimensions.
[PARA 4511 In bone, say, as the trabecular number decreases with degradation,
it would
be advantageous to vary the VOI length to keep the ratio of feature size
(textural number) to
sampling length relatively constant. In this case, TbN is the feature length
of interest, not
TbSp or TbTh, as TbN determines the repeat number relative to the VOI
acquisition length.
Use of scout images to determine the relative position(s) in k-space of the
peak signal
power, would enable optimal setting of the VOI length.
[PARA 4521 To affect the Scout measure, one method would be to select optimal
tissue
contrast based on what is known of the pathology, and acquire data for
sufficient time and
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TRs to gauge generally where the power lies in k-space for a single
orientation acquisition.
These scout measures can be made using gradient ON acquisition to sweep
through a range
of values, keeping the sweep slow enough to obtain sufficient SNR to gauge
where the
textural power lies in k-space. Thus, scout measure would be accomplished by
first
acquiring signal data across a large enough region in k-space to encompass the
textural k-
values associated with the tissue under study across the range of health into
disease. Real-
time decision on what range(s) demonstrate importance in the initial scan can
then be
selected for more detailed tracking by slower gradient ON acquisition, or
targeting specific
k-values to measure. Data acquisition will then be successively focused on
narrower and
narrower ranges to zero in on k-space regions that hold the highest
information content. In
this undertaking, it must be kept in mind that, regions of little textural
features can, in their
very lack of texture, be of import in disease progression
[PARA 4531 In some cases, where pathology is mirrored in changes across large
regions
of k-space, acquisition could be with gradient on and slow sampling across the
entire
region.
[PARA 4541 As another example, scout acquisitions could be used to determine
the k-
value of interest for a relaxometry measure. Since most biologic tissue
textures are non-
crystalline, a finite band in k-space, representative of the textural k-value
span, could be
identified using gradient ON acquisition. This could be achieved by
successively sampling
sub regions across k-space to find the region of interest where the textural
power lies. Once
the k-band of interest is determined, the decay of RF excitation across that
band can be
measured as the signal decays, the sampled region being selected using
gradient ON
acquisition.
[PARA 4551 Scout images can be used to investigate sensitivity to exact
positioning.
[PARA 4561 Thus, as shown in FIG. 32 a procedural flow using scout
acquisitions might
entail:
[PARA 4571 Setting a VOI based on knowledge of the type of underlying tissue
and
disease, step 3202, and acquiring signal data across some broad regions in k-
space. The
regions can be set either by acquisition with a gradient on to broaden the
acquisition in k-
space over the time of the measurement, step 3204, or with stepped k-values,
by windowing
in sample space, step 3206. The former procedure is preferred as it allows
sampling of a
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larger number of textural repeats to better determine where the power of
interest lies. This
measure can be repeated across different ranges in k-space, step 3208, to
determine the k-
space region on which to focus for the diagnostic measure.
[PARA 4581 The scout acquisition can be repeated as needed across various
selected
ranges in k-space, within one or multiple TRs, step 3210. The loss of
coherence between
TRs does not matter as long as the measure remains within a similar region of
tissue texture.
Real time repositioning could be used here as needed for gross repositioning,
step 3212.
[PARA 4591 This scout method can be repeated in as many different
orientations, step
3214, and as many different locations as desired, step 3216.
[PARA 4601 Using the information obtained for the power(s) of interest, VOI
dimensions
can be selected to allow sampling of a minimum of 4 textural repeats (more
would be better,
though the shorter the VOI the easier it is to maintain textural homogeneity
across its
dimensions), step 3218, and data will be acquired across the salient ranges in
k-space, in
one or multiple TRs.
[PARA 4611 It is to be noted that features of interest are not just in the
ranges where there
is much spectral power, but also includes ranges that are of interest due to a
specific lack of
signal intensity there.
[PARA 4621 As briefly described above with respect to various applications of
the
embodiments disclosed, machine learning in development and application of
medical
diagnostics can be applied to facilitate 1) calibration of the diagnostic and
determination of
optimal data acquisition parameters, 2) identification of the biomarkers of a
disease, and 3)
ongoing use of the diagnostic method in both individual and population health
spheres to
ensure optimal extraction of diagnostic information. Machine learning can be
applied in
either a supervised learning method, when it is known what output
quantity/biomarker (such
as trabecular bone thickness) is to be measured, or it can be run in an
unsupervised mode, in
which the algorithm searches the data sets it is given looking for common
features. In the
former case, supervised learning, the accuracy with which a new diagnostic
measures the
known quantity is determined by comparison to some sort of ground truth
measure, and the
machine learning can be used to optimize the data acquisition parameters for
the diagnostic.
In the case of unsupervised learning, once the features common among the
various data sets
are extracted¨in the case of the embodiments disclosed, the power distribution
in certain
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regions of the textural wavelength spectrum¨ they can then, if desired, be
correlated with
other known information about the targeted tissue such as state of
health/disease.
[PARA 4631 Machine learning can be an instrumental part of calibrating the
embodiments
disclosed as a diagnostic tool in each disease in the large range of diseases
for which
variation in tissue texture is a marker of disease onset and progression. The
application of
machine learning towards this goal is facilitated by use of a source of ground
truth measure
of pertinent tissue texture, to provide input for determination of the salient
textural features
that track pathology, features which are currently not measurable in vivo, and
for validation
of the diagnostic data obtained by the embodiments disclosed. This ground
truth can be
provided through use of tissue samples from various organs, reflecting various
states of
health and pathology in disease. Because there is no motion blurring, use of
ex vivo tissue
enables generation of high quality ground truth data sets for calibration of
the embodiments
disclosed by use of techniques such as microCT, pathology staining, and MRI
microscopy.
(Szeverenyi et al., MR imaging of liver microstructure in hepatic fibrosis and
cirrhosis at
11.7T, ISMRM 2016).
[PARA 4641 The work flow for calibration of the embodiments disclosed for
application
in diagnosis of targeted diseases is described in FIG. 33 as the following:
Obtain high resolution 2D or 3D data sets from selected tissue samples by use
of microCT,
MRI microscopy, or pathology, step 3302. Use computer simulation to simulate
data
acquisition by the embodiments disclosed for texture characterization from
these data sets
by using the data sets as input for simulating application of a selected
contrast mechanism,
selectively exciting a simulated volume of interest (VOI) employing a
plurality of simulated
time varying radio frequency signals and applied gradients, applying a
simulated encoding
gradient pulse to induce phase wrap to create a spatial encode for a specific
k-value and
orientation, the specific k-value determined based on the texture within the
VOI, initiating a
series of simulated gradients to produce k-value encodes, a resulting k-value
set being a
subset of that required to produce an image of the VOI, recording multiple
sequential
samples of simulated NMR RF signal encoded with the k-value set and post
processing the
recorded NMR signal samples to produce a data set of signal vs k-values for k-
values in the
k-value set, to characterize a simulation of the textural features of tissue
in the VOI, step
3304. Compare the features in the 2D/3D data sets with the measures of these
features
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obtained by simulated application of texture characterization, step 3306.
Simulate
acquisition of data by the embodiments disclosed for texture characterization
across a high
number of VOIs positioned within the tissue datasets, step 3308, and apply
supervised
machine learning to this data to optimize acquisition parameters such as VOI
dimensions
and acquisition direction, using best resolution of the targeted feature
measure as an
endpoint, step 3310. Use unsupervised machine learning across the various
defined VOIs in
tissue with specific disease markers to identify salient features additional
to that called out
for supervised learning, step 3312. Use machine learning algorithms to
correlate those
features with whatever information is known regarding disease onset and
progression in the
tissue samples towards biomarker identification, step 3314. Determine the
sparsely sampled
data set is that is needed for measuring the tissue biomarkers towards disease
diagnosis, step
3316. Use machine learning to determine the strength of the diagnostic
assessment provided
by the embodiments disclosed, step 3318. Acquire data by the embodiments
disclosed in the
actual SNR environment of the MR scanner, on the same tissue samples, for
comparison to
the ground truth datasets, step 3320. Repeat the above steps towards
optimization of
acquisition parameters and calibration of the embodiments disclosed towards
high
resolution, robust textural measure, step 3322.
[PARA 4651 As previously described, machine learning may be employed using the
tissue
texture measurement methods of the various embodiments to enhance determining
pathology of a tissue type. A contrast mechanism is selected enhancing the
contrast
between component tissue types in a multiphase biologic sample which may also
be
employed for measurement with a MR imaging process. The selected contrast
mechanism
is applied and a volume of interest (VOI) is selectively excited employing a
plurality of time
varying radio frequency signals and applied gradients. An encoding gradient
pulse is
applied to induce phase wrap to create a spatial encode for a specific k-value
and
orientation, the specific k-value determined based on texture within the VOI.
A series of
gradients is then initiated to produce k-value encodes, a resulting k-value
set being a subset
of that required to produce an image of the VOI. Multiple sequential samples
of the NMR
RF signal encoded with the k-value set are recorded and post processed to
produce a data
set of signal vs k-values for k-values in the k-value set, to characterize
textural features of
tissue in the VOI. Machine learning is then applied to a power density
distribution of a
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textural wavelength of the k-value set to identify bio-markers for diagnosis
of pathology of
the tissue.
[PARA 4661 The method may be further enhanced by applying machine learning to
identify a correlation between textural features and features in a power
density spectrum of
the textural wavelengths. Machine learning may also be applied to the textural
features
using additional sources of diagnostic information such as patient histories,
exam records,
imaging, serum markers, physical performance, and cognitive tests for
extraction of
diagnostic data to determine a disease assessment. Machine learning may also
be applied to
determine weighting of the various diagnostic information sources in the
ultimate diagnosis.
[PARA 4671 Input for machine learning may also be created by selecting a
plurality of
biologic phantoms having tissue pathology from healthy through diseased. A
contrast
mechanism enhancing the contrast between component tissue types in each
biologic
phantom is selected for measurement with a MR imaging process and the selected
contrast
mechanism is applied in an MR pulse sequence. A volume of interest (VOI) in
each
biologic phantom is excited employing a plurality of time varying radio
frequency signals
and applied gradients. An encoding gradient pulse is applied to induce phase
wrap to create
a spatial encode for a specific k-value and orientation, the specific k-value
determined based
on anticipated texture within the VOI. A series of gradients to produce k-
value encodes, a
resulting k-value set being a subset of that required to produce an image of
the VOI are then
applied and multiple sequential samples of the NMR RF signal encoded with the
k-value set
are recorded to provide texture measurement of each of the biologic phantoms.
[PARA 4681 Following calibration and optimization of the embodiments disclosed
in
various disease applications, the texture measurements produced by the
embodiments would
then be used as clinical diagnostic tools. Due to the ability to measure
textural features to
high-resolution in a size realm previously unmeasurable, and the fact that
tissue changes are
a very sensitive measure of pathology progression, the embodiments disclosed
produce high
information content data. Also, as it is a fast and hence minimal cost to add
the disclosed
embodiments to an MRI scan, barriers to adoption and use are low. As such, the
disclosed
embodiments will be one of the important drivers in the clinical diagnosis of
disease, and
can thus help in weighting the efficacy of the various other sources of
diagnostic
information that are applied. Machine learning algorithms would be applied to
correlate all
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the data from the multiple sources of pertinent data acquired towards patient
health
assessment, including data acquired by the embodiments disclosed, patient
histories, exam
records, imaging, serum markers, physical performance, cognitive tests, and
any other types
of diagnostic information available. The range of diagnostic information
available on a
patient would be fed into a machine algorithm that would, using the sum of
this data,
provide both an optimized diagnosis and a weighting of the importance of the
various
diagnostic inputs to the algorithm, as well as an evaluation of the level of
certainty of the
diagnosis. This ability to assess diagnostic accuracy and weighting in
clinical practice
would be informed by use of machine learning in the sphere of large population
health data
sets towards determination of the efficacy of many currently used diagnostics.
Additionally,
correlation with all other data sets will provide ongoing calibration and
optimization of the
embodiments disclosed, as well as maximum extraction of diagnostic data.
[PARA 4691 Having now described various embodiments of the invention in detail
as
required by the patent statutes, those skilled in the art will recognize
modifications and
substitutions to the specific embodiments disclosed herein. Such modifications
are within
the scope and intent of the present invention as defined in the following
claims.
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