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Patent 2319119 Summary

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(12) Patent: (11) CA 2319119
(54) English Title: CEREBRAL CIRCULATION MODEL AND APPLICATIONS
(54) French Title: MODELE DE CIRCULATION CEREBRALE ET APPLICATIONS CORRESPONDANTES
Status: Expired
Bibliographic Data
(51) International Patent Classification (IPC):
  • A61B 5/026 (2006.01)
  • A61B 8/06 (2006.01)
  • A61B 5/055 (2006.01)
(72) Inventors :
  • CHARBEL, FADY T. (United States of America)
  • CLARK, M. E. (United States of America)
  • SADLER, LEWIS (United States of America)
  • ALPERIN, NOAM (United States of America)
  • LOTH, FRANCIS (United States of America)
  • QUEK, FRANCIS (United States of America)
  • ZHAO, MEIDE (United States of America)
(73) Owners :
  • THE BOARD OF TRUSTEES OF THE UNIVERSITY OF ILLINOIS (United States of America)
(71) Applicants :
  • THE BOARD OF TRUSTEES OF THE UNIVERSITY OF ILLINOIS (United States of America)
(74) Agent: AVENTUM IP LAW LLP
(74) Associate agent:
(45) Issued: 2008-04-22
(86) PCT Filing Date: 1999-02-03
(87) Open to Public Inspection: 1999-08-05
Examination requested: 2004-02-03
Availability of licence: N/A
(25) Language of filing: English

Patent Cooperation Treaty (PCT): Yes
(86) PCT Filing Number: PCT/US1999/002276
(87) International Publication Number: WO1999/038433
(85) National Entry: 2000-07-26

(30) Application Priority Data:
Application No. Country/Territory Date
60/073,580 United States of America 1998-02-03

Abstracts

English Abstract



A method and apparatus are provided for modeling cerebral
circulation in a living subject. The method includes the steps (200) of
developing a model for living subjects in general, and correcting the
model to substantially conform to the overall cerebral physiology of the
living subject. The method further includes the step of calculating a
cerebral flow of the living subject based upon the corrected model and a
selected cerebral blood flow pertubation.


French Abstract

La présente invention concerne un procédé et un appareil de modélisation de la circulation cérébrale chez un sujet vivant. Ce procédé consiste à réaliser (200) un modèle pour des sujets vivants en général et à le corriger de manière à ce qu'il corresponde quasiment à la physiologie cérébrale générale du sujet vivant. Ce procédé concerne par ailleurs le calcul du débit cérébral du sujet, lequel calcul repose sur le modèle corrigé et sur une perturbation sélectionnée du débit sanguin cérébral.

Claims

Note: Claims are shown in the official language in which they were submitted.



WE CLAIM:

1. A method of modeling circulation in a living subject,
such method comprising the steps of:
simulating the fluid dynamics of an arterial network,
wherein the simulation models blood flow through a plurality
of arterial segments including one or more terminal efferent
vessels;
adapting the simulation to substantially conform to a
specific arterial anatomy of the living subject;
forcing the simulation with a forcing function made up
of a signature selected from the group consisting of at
least flow-time signature and at least one pressure-time
signature;
calculating blood flows in the arterial network based
upon the forced simulation;

measuring a blood flow in the living subject;
correcting the simulation based on the measured and
calculated blood flows;
modifying the simulation to model a particular
interventional procedure; and,

calculating a post-procedure blood flow in a selected
arterial segment using the modified simulation in order to
predict an outcome of the actual interventional procedure
performed in the living subject.

2. A method of modeling as in claim 1 wherein the
simulated arterial network includes a Circle of Willis.

3. A method of modeling as in claim 1 wherein the step of
adapting the simulation to substantially conform to the
43


living subject's anatomy further comprises conforming a
vessel of the simulation with a corresponding vessel in an
image of the living subject.

4. A method of modeling as in claim 3 wherein the step of
adapting the simulation to substantially conform to the
living subject's anatomy further comprises measuring a
diameter of the corresponding vessel in the image of the
living subject.

5. A method of modeling as in claim 4 further comprising
localizing the corresponding vessel in three-dimensional
space and tracing a boundary into adjacent areas in three-
dimensional space to locate respective ends of the
corresponding vessel.

6. A method of modeling as in claim 1 wherein the
simulation of the arterial network includes a one-
dimensional, explicit, finite difference algorithm based
upon a conservation of mass equation, a Navier-Stokes
momentum equation, and an equation of state relating local
pressure to local artery size.

7. A method of modeling as in claim 1 wherein the
simulation is forced with a flow measurement obtained from
the living subject.

8. A method of modeling as in claim 1 wherein the
simulation is forced with a pressure-time signature obtained
from a prototypical measurement.

44


9. A method of claim 1 further comprising the step of
obtaining a flow measurement in the living subject by phase
contrast magnetic resonance angiography.

10. A method of claim 1 further comprising the step of
obtaining a flow measurement in the living subject by a
Doppler flow measurement.

11. Apparatus for modeling circulation within a living
subject, such apparatus comprising:
a computerized model of an arterial network made up of
a plurality of arterial segments including one or more
terminal efferent vessels, wherein the apparatus includes
means for calculating blood flows in the arterial network
when the model is forced with a forcing function;
means for adapting the model of the arterial network to
substantially conform to a specific arterial anatomy of the
living subject;
means for measuring a blood flow in the living subject;
means for correcting the model based upon the
calculated and measured flows;
means for modifying the model to reflect a particular
interventional procedure; and,

means for calculating a post-procedure blood flow in a
selected arterial segment using the modified model in order
to predict an outcome of the actual interventional procedure
performed in the living subject.

12. An apparatus for modeling as in claim 11 wherein the
circulation model further comprises the Circle of Willis.



13. An apparatus for modeling as in claim 11 wherein the
means for measuring blood flow is a phase contrast magnetic
resonance angiography flow measurement system.

14. An apparatus for modeling as in claim 13 wherein the
means for adapting the model to substantially conform to the
living subject's anatomy further comprises means for
selecting a vessel of the model and a corresponding vessel
in an image of the living subject.

15. An apparatus for modeling as in claim 14 wherein the
means for adapting the model to substantially conform to the
living subject's anatomy further comprises means for
measuring a diameter of the corresponding vessel.

16. An apparatus for modeling as in claim 15 further
comprising means for localizing the corresponding vessel in
three-dimensional space and tracing a boundary into adjacent
areas in three-dimensional space to locate respective ends
of the corresponding vessel.

17. An apparatus for modeling as in claim 11 wherein the
computerized simulation model includes a one-dimensional,
explicit, finite difference algorithm based upon a
conservation of mass equation, a Navier-Stokes momentum
equation, and an equation of state relating local pressure
to local artery size.

46


18. An apparatus for modeling as in claim 11 wherein
the model is forced with a flow measurement obtained from
the living subject.

19. An apparatus for modeling as in claim 11 wherein the
model is forced with a pressure-time signature obtained from
a prototypical measurement.

20. An apparatus for modeling as in claim 11 wherein the
means for measuring blood flow is a Doppler flow measurement
device.

47

Description

Note: Descriptions are shown in the official language in which they were submitted.



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WO 99/38433 PCT/US99/02276
CEREBRAL CIRCULATION MODEL AND APPLICATIONS

Field of the Invention
The field of the invention relates to blood
circulation in the human body and more particularly, to
blood flow within the human brain.

Description of the Invention
Stroke is the third leading cause of death and
disability in the United States, with significant
socioeconomic impact. Therapeutic options for occlusive
cerebrovascular diseases include a variety of
reconstructive procedures such as endarterectomy, vessel
transposition, bypass, angioplasty and thrombolysis; all
of which share the common goal of enhancing the cerebral
circulation.
However, because of the many individual
variabilities between patients, assessment of the
potential merit of such procedures is difficult and has
mainly relied on clinical trials involving large groups
of patients over long periods of time and at staggering
costs (e.g., EC-IC bypass and carotid endarterectomy
studies). The ethical dilemma of randomizing a patient
to an ineffective or even harmful therapy not
withstanding, selection of the optimal cerebral vascular
reconstructive procedure in a given patient with complex
occlusive patterns remains difficult and ultimately
relies on an intuitive process.
The ability to reliably simulate such procedures
for any particular patient, increases the likelihood of


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WO 99/38433 PCT/US99/02276
selecting the optimal procedure. This invention applies
a computerized model of the cerebral circulation to
patients with cerebrovascular diseases to simulate any
of a number of procedures, thereby providing a tool for
selecting the optimal procedure for any given patient.
The benefit of this invention provides improved patient
outcome, significant cost savings and major impact on
the way cerebrovascular reconstructive procedures are
performed.
Charbel et al., 2"a international Skull Base
Congress, 7th Annual Meeting of the North American Skull
Base Society, (San Diego, July, 1996) discussed the
applications of an earlier computerized model of the
cerebral circulation in skullbase surgery. The role of
computerized modeling in cerebrovascular surgery was
discussed by Charbel et al. at the 11t'' International
Congress of Neurological Surgery (Amsterdam, July 1997).
Charbel et al. discussed the use of a computerized
predictor for the tolerance of carotid artery sacrifice
at 11th International Congress of Neurological Surgery
(Amsterdam, July 1997). Applications of modeling to
cerebral revascularization are also contemplated.
Summary
A method and apparatus are provided for modeling
cerebral circulation in a living subject. The method
includes the steps of developing a model for living
subjects in general and correcting the model to
substantially conform to the overall cerebral physiology
of the living subject. The method further includes the
step of calculating a cerebral flow of the living

2


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subject based upon the corrected model and a selected
cerebral blood flow perturbation.

Brief Description of the Drawings
FIG. 1 is a block diagram of a modeling system for
cerebral circulation in accordance with an illustrated
embodiment of the invention;
FIG. 2 is a cerebral model used by the system of FIG.
1;
FIG. 3 is an operator interface used by the system of
FIG. 1;

FIG. 4 is a flow diagram used by the operator
interface of FIG. 3;

FIG. 5 is an operator display used by the system of
FIG. 1;

FIG. 6 is a flow chart of cerebral modeling of the
system of FIG. 1; and

FIG.. 7 depicts the operator interface of FIG. 3 as it
may appear during use.

In accordance with a first aspect of the present
invention, there is provided a method of modeling
circulation in a living subject, such method comprising the
steps of:

simulating the fluid dynamics of an arterial network,
wherein the simulation models blood flow through a
plurality of arterial segments including one or more
terminal efferent vessels;

3


CA 02319119 2006-10-27

adapting the simulation to substantially conform to a
specific arterial anatomy of the living subject;
forcing the simulation with a forcing function made up
of a signature selected from the group consisting of at
least flow-time signature and at least one pressure-time
signature;
calculating blood flows in the arterial network based
upon the forced simulation;
measuring a blood flow in the living subject;
correcting the simulation based on the measured and
calculated blood flows;
modifying the simulation to model a particular
interventional procedure; and,
calculating a post-procedure blood flow in a selected
arterial segment using the modified simulation in order to
predict an outcome of the actual interventional procedure
performed in the living subject.
In accordance with a second aspect of the present
invention, there is provided an apparatus for modeling
circulation within a living subject, such apparatus
comprising:

a computerized model of an arterial network made up of
a plurality of arterial segments including one or more
terminal efferent vessels, wherein the apparatus includes
means for calculating blood flows in the arterial network
when the model is forced with a forcing function;
means for adapting the model of the arterial network
to substantially conform to a specific arterial anatomy of
the living subject;

3a


CA 02319119 2006-10-27

means for measuring a blood flow in the living
subject;
means for correcting the model based upon the
calculated and measured flows;

means for modifying the model to reflect a particular
interventional procedure; and,
means for calculating a post-procedure blood flow in a
selected arterial segment using the modified model in order
to predict an outcome of the actual interventional
procedure performed in the living subject.

Detailed Description of an Illustrated Embodiment
The present invention is a practical integration of
several aspects of cerebrovascular and computer research.
A program has been developed for computer aided
neurovascular analysis and simulation that is useful for
assessment and prediction of cerebral circulation. The
program has up to four major components: (i) a vessel
extraction system from Digital Subtraction Angiography

(DSA); (ii) a three-dimensional phase contrast Magnetic
Resonance (MR) flow measurement system and three-
dimensional pulsatility visualization; (iii) a computer
simulation system for cerebral

30 3b


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WO 99/38433 PCT/US99/02276
circulation; and (iv) a worldwide web-based interface
and window to the world.
The process involved in the present invention
creates a virtual replica of the Circle of Willis and
computes network blood flow. The modeling program also
allows analysis and simulation of flow and pressure
within the cerebrum under the condition of a selected
blood flow perturbation (e.g., a cerebral aneurysm,
stenosis, bypass, other cerebrovascular disease, etc.).
The modeling program currently uses the finite
difference method.
The vessel extraction system is used for
determining exact vessel diameters for use in the model.
Currently an Attention-Based Model (AIM) forms the
basis of the vessel extraction system. The vessel
extraction system provides accurate and rapid
measurement of vessel diameters and length at various
predetermined regions of the Circle of Willis. The data
with the smallest vessel size resolution (on the order
of a few hundred microns) is currently derived from x-
ray angiograms [platform: SGI Workstation, IRIS 6.2
Varsity Package (Rapidapp, Open Inventor, C++)].
The use of digital x-ray angiography and
computerized acquisition of the digitized picture, for
example through an "image-grabber" can accelerate and
enhance the data acquisition process over manual reading
of vessel diameters from the x-ray angiogram. The two-
dimensional nature of current x-ray angiographic
measurements limits the ability to derive detailed
three-dimensional data from the technique.
Magnetic resonance imaging (MRI) has developed in
such a manner that useful three-dimensional data

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WO 99/38433 PCT/US99/02276
pertaining to vessel size and flow is obtainable. The
current source of choice of data from magnetic resonance
imaging uses three-dimensional phase contrast
angiographic methods to measure flow [platform: SGI
Workstation, IRIS 6.2 Varsity Package (Rapidapp, Open
inventor, C++)]. On its own, the vessel size resolution
of the MRI data is on the order of a half a millimeter.
The use of this data in the three-dimensional phase
contrast magnetic resonance (MR) flow measurement system
of the invention enhances the accuracy of the cross-
section and flow measurements by three-dimensional
localization and visualization of the vessels.
Turning now to image processing, AIM will now be
discussed. For the interactive approach described
herein to be effective, it must exploit the facility of
humans to operate or communicate within the bounds of
certain rules which govern the approach. As will become
apparent, AIM is a selective attention-inspired
interaction paradigm for the analysis of multimodal
medical images (e.g., magnetic resonance angiography
(MRA), magnetic resonance imaging (MRI), xenon computed
tomography (XeCT), x-ray angiograms (XRA), etc.). In
general, the AIM approach exploits the strengths of both
humans and machines to overcome the others' weaknesses.
FIG. 1 is a block diagram of a cerebral modeling
system 10, generally in accordance with an illustrated
embodiment of the invention. The modeling system 10
generally includes a data source, 12, a central
processing unit (CPU) 14, a display 18 and a keyboard
16. The data source 12 may one or more medical imaging
systems (e.g., MRA, MRI, XeCT, XRA, etc.).

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WO 99/38433 PCT/US99/02276
While many aspects of human attention are yet to be
ascertained, the following is known: 1. Attention
facilitates the direction of limited cognitive
resources; 2. Spatial cues (or prompts) are effective in
directing the attentional "spotlight" for recognition;
3. Central semantic cues at both the feature and object
levels facilitate performance in visual attention tasks;
and 4. A model of what is in the attentional space of
one's interlocutor is critical to maintaining effective
communication.
AIM defines two interaction channels: a semantic
context (what to look for) and a focus-of-attention FOA
(where to look). In an AIM system 10, the user selects
the context from a menu (or a schematic diagram of the
neurovascular system) and manipulates a FOA cursor
(FOAC) through the data using a 2D or 3D pointing
device. For example, FIG. 2 shows the human Circle of
Willis 100, including, seventy-three blood vessels.
Selection of one of the vessels provides the system 10
with a context of what to look for.
FIG. 3 shows an interactive screen 152 that may be
presented on the display 18 of the system 10. The
Circle of Willis 100 of FIG. 2 may be displayed as a
graphical context representation in a box 152 of the
screen 150. An operator (not shown) of the system 10
may select a vessel (e.g., 102) using the cursor 162.
Once a context has been selected in the first box
152, the operator may show the CPU 16 where to look for
the vessel in the image work area 154. The operator may
identify the vessel 102 by placing the FOAC (i.e., the
smart cursor 162) over the vessel and activating an
ENTER button.

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WO 99/38433 PCT/US99/02276
The AIMS processes the data in the region of the
FOAC to locate and highlight entities matching the
selected context in real-time as the cursor moves. When
the correct entity is highlighted in the work area 154,
the user acknowledges this and the system extends the
dialog by tracing the entity (e.g., the highlighted
vessel), all the while providing feedback via animated
highlighting. The system may trace the entity by
looking for high contrast areas between adjacent pixels
or groups of pixels of the image. The high contrast
areas may be used to identify boundary areas between
vessels and surrounding tissue. Where the vessel is
identified to the system by the operator, the system
identifies the vessel within the FOAC and begins
processing adjacent areas to trace the contrasted areas
in three-dimensional space using the continuity of the
contrasted area of a path to other parts (e.g., the
respective ends) of the vessel.
Using the contrast, the system not only traces the
vessel, but also measures a diameter of the vessel. The
system identifies the outer wall of the vessel by
seeking the area of the greatest rate of change in
contrast. A measurement may then be taken of the
distance between opposing sides of the vessel.
Since the system knows what it is looking for, the
problem becomes one of detection of a specific entity.
Hence, very specifically tuned detectors may be used.
it is not critical if the detector highlights the wrong
entity because the user can retarget the detector by
simply moving the pointing device.
AIM defines an abstraction hierarchy of contexts.
A context may vary in abstraction from full scene

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WO 99/38433 PCT/US99/02276
interpretation through object level, feature level and
point level contexts. In the domain of neurovascular
tree, an object level context may be a segment of the
carotid artery, a feature level context may be arterial
boundary model, and in the point level context, a user
may trace the boundary points of a vessel by hand. This
provides robustness for the system for our mission-
critical task in the process of patient care. For
neurovascular image interpretation, this guarantees the
ability to obtain the necessary vessel extraction and
measurement even if the higher-level recognition
algorithms fail (e.g., owing to patient pathology or
data quality).
The AIM model provides a system and software
architecture of broad applicability. This is important
because AIMS is easily extensible to accommodate new
algorithms, imaging modalities and entities of interest
(e.g., other brain structures such as the
interhemispheric chasm of the brain, aorta walls for
cardiovascular image interpretation, tumor models for
digital mammogram analysis) without requiring extensive
ad hoc reengineering for each domain. FIG. 4 is a block
diagram of the architecture characterizing the
interaction model. The object-oriented architecture
comprises two distinct components: The user interface
and the domain knowledge representation. Such
modularity is important for the system 10 to be portable
across platforms and display/interaction technologies
(e.g., for both 2D and 3D interpretation environments).
The context database maintains domain knowledge
about the entities of interest (e.g., vessels of
different types, and the operators necessary to extract

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WO 99/38433 PCT/US99/02276
them from the different kinds of image data). The data
information maintains meta-information about the data
(e.g., file name sequences, where data is stored, the
resolution of each image type, etc.). The Operator
Selector selects the appropriate FOAC operator from the
Operator Library. The Process knowledge database
maintains knowledge of the interpretation protocol.
The process knowledge database realizes the concept
of dialog extension by tying it directly to
medical/radiological protocol. For example, in the
domain of neurovascular image interpretation, the
protocol dictates an order of vessel extraction that is
efficient with respect to the ordering of the XRA
dataset and the content of particular viewing
projections in the XRA images. This protocol can be
encoded seamlessly into the interaction sequence so that
the system prompts the user for each succeeding vessel
in the neurovasculature. A protocol definition file
pairs the order of vascular measurement with the
preferred images in which the measurement may be made.
The system may simulate an extended dialog by prompting
the user for each vessel measurement in the appropriate
images. This permits the system to predict the next
entity to be extracted so that the user does not have to
remember the order thus reducing processing errors
(e.g., missing or mixed-up readings). This example also
illustrates the concept of interrupting and resuming
dialog streams. A user may wish to interrupt the
protocol for several reasons. She may wish to correct
and erroneous measurement made earlier, or may want to
make a measurement of opportunity out of sequence. AIM
permits such interruption by having the user select the
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WO 99/38433 PCT/US99/02276
context of interest. One may think of this as the user
"changing the subject". AIM maintains the location of
"interrupt on" and permits the user to resume the dialog
(or protocol) whenever she wishes. Hence, the process
knowledge database actually facilitates the
psycholinguistic components of discourse situatedness
and repair that are essential for effective interaction.
FIG. 3 shows the general screen layout of the AIM
interface 150 used in the interpretation of complex
images. The Working Area 154 is the primary area in
which the operator interacts with the system in the
interpretation task. The Focus of Attention (FOA) is
directed by manipulating the Smart Cursor 162 over the
displayed subject image in the Working Area with a
pointing device (e.g., a mouse). Feedback for the
interpretation process is provided in the Working Area
by highlighting the objects under the Smart Cursor which
satisfy the current context. The FOA Status Display 158
at the bottom left of the display provides a magnified
view of the area under the smart cursor as well as the
processing status. In a later section, we shall see how
partial processing results are presented in this
display.
The Graphical Context Presentation 152 on the left
side of the screen provides the user with the overall
status of the interaction as well as the particular
interpretive context. In our neurovascular
interpretation prototype, this is a schematic of the
neurovascular tree. In a cardiovascular measurement
system, this may be a schematic representation of the
aortic system. The schematic representation provides
the user with an overall state of the interpretation


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WO 99/38433 PCT/US99/02276
process with the use of color (e.g., in our
neurovascular measurement system of the context box 152,
already measured vessels are filled with green, and
context vessel is colored red). The user may interrupt
the dialog process (or measurement protocol) by
selecting a context object in this window by direct
selection of its representation in this schematic. In
addition to this graphical presentation, this generic
screen layout also provides for textual context
identification in the System Status and Process Status
boxes 156, 160. The former provides general information
about the selected context, and the latter details the
status of the current interpretation (e.g., the
dimension of the vessel being measured in the current
image).
The pulldown menu items 164 on the top of the
screen permit alteration of the system parameters and
selection of data sets on which to operate.
Owing to the strong man-machine interaction model
behind it and the user interface design principles used
in the development of the interface, the AIM interface
described herein is simple, yet effective and easy to
use. FIG. 7 shows an example of the AIM interface 150
as it may appear during the interpretation of XRA images
of the neurovascular system. The XRA image is displayed
in the Working Area 154. This area is scrollable, so
any size of XRA image can be displayed. The user can
increase or decrease the size of the image at any time
by choosing appropriate selection item from the menubar
164. The Focus of Attention (FOA) is represented by the
box 162 shown in the image and is directed by the mouse.
A schematic representation 100 of the neurovasculature
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is provided in the Graphical Context Presentation 152.
It is shown in the upper left corner of the screen to
provide object level context to the system in a
graphical fashion. When the user selects a vessel on
this schematic representation 100 (using the mouse), the
information about this vessel is displayed in the window
160 in the lower right corner of the screen. This
information contains the vessel number (used as an
index), the vessel name, the width and any comment
written by the user about the measurement process of
this vessel. If no measurement has been taken for this
vessel, the vessel, the vessel has the default width
stored in the database. The vessels are highlighted in
different colors to give feedback on the selection and
measurement processes.
The FOA Status Display"158 at the bottom left of
the screen displays the processing status of the smart
cursor. It shows a magnified view of vessel boundary
found in the FOA and locates the cross-section where the
measurement is being made. Partial results of the
computation and the process parameters are also shown in
this area.
The AIM neurovascular measurement system is
designed to be used by medical/radiological personnel.
Hence, it is advantageous to exploit the familiarity of
such experts with systems they currently use for the
selection of XRAs to be used in the measurement. FIG. 5
shows an additional window 177 that facilitates the easy
use of the system 10. In the system 10, every patient's
XRA images are stored on the storage device in a
separated directory. The system 10 creates thumbnail
images of the original images, as shown in the window
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177, which are 128x128 and 256x256 pixels wide. The
user can see all of the 128x128 versions of the
thumbnail images at the same time on a window similar to
the lightboard used by the doctors to inspect the XRAs.
When the cursor 162 is held on one of the thumbnail
images for a few seconds, the bigger, 256x256 version of
theta image is displayed on the screen. This allows the
user to inspect the image more carefully. When the user
decides to choose one image for interpretation, he
simply clicks the mouse button and the system brings the
original version of this image into the Working Area.
Another important feature of the system is that the
interpretation process is done in real time on modest
computation hardware. Instead of processing the entire
image, only the area under the FOA is processed,
resulting in very fast processing speeds. The strong
feedback mechanisms, both graphical and textual, during
the interpretation process make the system effective and
easy to use.
A practical system for neurovascular interpretation
must account for different individuals doing the
measurement and using the measurement result. A surgeon
may delegate the measurement process to a medical
technician, but the surgeon is ultimately responsible
for her final treatment decision. Hence, the AIM dialog
model has been extended to include the recording of the
state of the interaction when the measurement was
acquired. This History of Measurement feature maintains
information about the measured vessels, including
identifier of the image used, the coordinates and size
of the FOA at the point of measurement. The measurement
results are stored in a database for every measurement

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taken. This permits the physician to perform "quality
control" checks of the measurement. Any measurement
status may be recalled by selecting the vessel on the
schematic representation or from the measurement table
at the bottom right of the screen (the Process Status
window). The user may also step through the measurement
and quality control, the user can change any measurement
taken using the AIM interpretation process.
As described above, vessel size and location may be
obtained from MRA, MRI, XeCT or XRA data. The
dimensional information derived from the data may be
enhanced using MRI operating in the Doppler mode. The
use of Doppler MRI allows volumetric blood flow to be
determined by measuring blood velocity across a cross-
section of each vessel.
The enhancement of the accuracy and vessel size
resolution arises from the three-dimensional
reconstruction of the vessels that currently uses an
interpolation scheme that is constrained by a piecewise
smooth volumetric flow equation. The flow data is
corroborated by transcranial Doppler measurements at
predetermined vessel intervals. The flow velocity has
also been found to vary as a result of vasoreactivity.
Benchmark values for cerebral autoregulation have been
established and is continuously refined.
The data analysis system supports both automatic
and interactive extraction of the vessel cross-sections.
A color coding scheme facilitates visualization and
user interaction with the data to reduce the inter-user
variability.
For example, a user can view, move and freely
rotate a three-dimensional picture of the

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cerebrovascular.network 100. The user can also place a
plane anywhere in the three-dimensional picture, then
view the two-dimensional cross-section of the
cerebrovascular network 100 at that location.
The vessels are clearly discernible in the cross-
section, with color coding to denote flow into and out
of the planar cross-section. Three-dimensional
pulsatility of blood flow can be animated and visualized
in the currently-developed user interface 150. A vessel
can be selected by the user for detailed and graphical
analysis of the flow through the vessel cross-section
that animatedly shows the pulsatile flow changes with
time.
The computer simulation system 10 for cerebral
circulation employs the outputs from both the vessel
extraction system and the three-dimensional phase
contrast MR flow measurement system to calibrate,
customize and drive the cerebral circulation model
[platform: PC (Pentium), Windows95/NT or DOS, Lahey
Fortran 77]. The model is reconfigurable to account for
person-to-person variability in the cerebrovascular
network. The overall number of vascular segments in the
flow model can be increased or decreased as needed to
form a customized model for each patient.
The computer simulation system is flexible and
accommodates empirical observations of measurements from
the x-ray and magnetic resonance angiograms as well as
from direct measurements of flow using flowmeters during
surgery, and transcranial Doppler (TCD) at selected
sites. The parameters of the model can be adapted as
validation of the model requires in "normal" subjects
and subjects with cerebrovascular diseases.



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The computer model is a one-dimensional, explicit,
finite difference algorithm based on a conservation of
mass equation, a Navier-Stokes momentum equation, and an
equation of state relating local pressure to local size
of artery [Khufahl & Clark, ASME J. of Biomech. Eng.
107:112-122 (1985)].
Since the arterial networks contain vessel loops
(as well as many branchings), the pressure and flow
nodes are staggered throughout the model. Each vessel
is divided into many segments; the flow nodes are
located at segment ends, the pressure nodes at segment
centers. Any multi-vessel network configuration can be
specified solely from the data file.
The model is forced by one or more pressure or flow
signatures at appropriate locations. Usually, a
pressure-time signature at the root of the aorta,
obtained from prototype measurements or angiographic
data, serves as the forcing function. Velocities at
certain points in the network as determined by
transcranial Doppler measurement are also integrated.
As shown (FIG. 6), the model of the system 10 is
first initialized 202 with initialized pressures and
flows at all points in all vessels. A cross-sectional
area is calculated 202 for all points using the current
pressure. The mass balance is determined 204 by
calculating pressures at all points except the pressures
at vessel junction centers. An inlet forcing function
is invoked 206 to update pressure and flow sources. The
internal junction boundary conditions may be evaluated
208 by calculating flows at all junctions and the
pressures at all junction centers.

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A momentum balance may then be determined 210 by
calculating the flows at all points except the flows at
junctions. The pressures at all junction centers may be
used as special forcing functions to introduce internal
flow sources. A set of terminal boundary conditions may
be determined 212 by calculating flows at the last nodes
in all efferent vessels.
Finally, a current time value is incremented 214
and the incremented time is compared 216 with a modeling
period. If the incremented time is less than the
modeling period, the process 200 repeats. If not, the
process terminates.
A baseline vessel network 100 is used in the
current model, including the Circle of Willis,
ophthalmic arteries and other natural anastomoses. When
surgical anastomoses are considered, the number of
vessels can rise as needed. In addition, segments
stenosed (perturbed) by any specified amount can be
placed in any number of vessels. Aneurysms can also be
simulated at various sites in the network. The results
of any of a number of surgical procedures may be
accurately predicted based upon the model results of the
system 10.
A worldwide web-based interface and window to the
world is available to allow users access to the
interactive and user-friendly program from remote sites
throughout the Internet.
The system for computer aided neurovascular
analysis and simulation is usefully applied to model and
analyze various neurosurgical conditions.
Early models of blood flow in the brain were based
upon an assumption that any particular artery feeds a
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particular part of the brain. The volume of brain fed
can be calculated. First, the total mass of the brain
can be determined. Then, the portion fed can be
determined as a percentage of the determined total.
Knowing the mass of brain fed by a particular artery
allows the volume of blood necessary to feed that mass
to be determined. Those early models were general,
using assumptions of the elasticity of the blood
vessels, the viscosity of the blood, and the vasculature
arrangement of a normal patient's brain circulatory
system.
The present invention is a refined model that is
capable of being adapted to specific patients. Once the
volume of blood necessary has been determined, the model
is calibrated to a particular patient.
Deviations of the arterial structure of the blood
supply of the patient's brain from the general model are
identified from the angiograms. An x-ray angiogram (XR
angiogram)of the patient's brain is used to determine
the diameter of the blood vessels. Phase contrast
Magnetic Resonance imaging angiography (MR angiography)
is then used to determine an actual blood flow in the
brain. Missing or additional arterial segments may be
identified and used to adjust the model. A knowledge of
the actual arterial structure and actual blood flows can
be used to customize the model to the actual patient.
An empirical study of user variability using the
three-dimensional phase contrast MR angiographic flow
measurement system was conducted. The study implemented
an end-to-end system. The study tested the
repeatability of measurements by having ten users select
the vessel cross-section from the interpolated color

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WO 9958433 PCT/US99/02276
velocity images of the cerebrovascular flow. The flow
computation performance of the system was verified
against the data collected from a constant flow phantom,
from ten patients with varying cerebrovascular diseased,
and from a computational fluid dynamic (CFD) simulation.
The results of the empirical study show that the
readings are stable across users, and that the flow
measurements show a good degree of fidelity to the flow
obtained through clinical measurement and CFD
simulation. A 96 percent overall accuracy on 160
measurements was observed for the phantom, with less
than 5 percent mean error in the inter-user variability
in extraction of the vessel cross-section among the ten
users. For the ten patients, the flow computed was
satisfactory in qualitative evaluation. The computed
flow also correlated very well with those measured with
a flowmeter in two patients r=0.985, P<0.001). The flow
measurements in the Circle of Willis correlated well
with the results of a CFD simulation of two patients (r-
0.970, P<0.001).

Cerebral Circulation Modeling
The circulation around the "Circle of Willis" as
first described in 1664 was initially only thought to be
a poorly functioning anastomotic network at the base of
the brain. Willis, Annals of Medical History 2:81-94
(1940). Later it became accepted that it acts as the
main distribution center for cerebral blood flow. In
health, it distributes blood proportionately to each
part of the brain; in disease, when the blood supply is
decreased or diminished, it can redistribute flow in an
effort to maintain cerebral hemodynamic homeostasis.

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Because the cerebral circulation network is encased
by the skull, it is very difficult to measure flow and
pressure in its blood vessel constituents in a direct
manner. Therefore computer simulation using the system
10 becomes an attractive way to predict flows and
pressures in the cerebral circulation network. Fluid
dynamic-based computer simulation offers the convenience
of predicting pressures and flows at almost any desired
section in the circulating system. Moreover, it not
only can be used to estimate the flow and pressure under
health and disease situations, but also can be used to
predict the result of treatment procedures.
The simulation of cerebral circulation presents a
range of challenging fluid dynamic problems, including:
modeling the non-Newtonian properties of blood; dealing
with "physiological" unsteady pulsatile flow; modeling
the elasticity of vessel walls; and modeling moving
boundaries caused by vessel wall elasticity. In
addition, because the cerebral circulation network is an
interconnected three dimensional arterial network, the
question arises of how the curvature of the artery is
modeled. The asymmetric and three dimensional
characteristic of bifurcations are also very important
issues.
Computer simulation started with models of the
dog's cerebral circulation system. Clark et al., Acta
Neurol. Scandinav., 43:189-204 (1967), built a computer
model for one-dimensional, linear, steady laminar flow
and compared the result with an engineering model built
by the same group [Himwich et al., Archive of Neurology,
13:164-172 (1965); Himwich et al., Archive of Neurology,
13:173-182 (1965)). Cooper, Ph.D. thesis, Washington



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WO 99/38433 PCT/US99/02276
University (1970), studied the pulsatile flow effect.
Chao & Hwang, TiT Journal of Life Science, 2:3-81 (1972)
simulated non-linear pulsatile flow in their model.
Himwich & Clark, Pathology of Cerebral Microcirculation,
J. Cervos-Navarro, ed. (Walter de Gruyter, New York:
1974), pp. 140-152, and Clark & Kufahl, Proc. of 15t
Intl. Conf. Cardiovascular System Dynamics, MIT Press
(Boston, 1978), pp. 380-390, simulated the pulsatile
flow in distensible vessels. Hillen et al., J.
Biomechanics, 15:441-448 (1982), developed a non-linear,
one-dimensional model, simplified to only five vessels,
that accounted for pulsatile flow and elastic vessel
walls. Kufahl & Clark, J. Biomechanical Engineering,
107:112-122 (1985), developed a one-dimensional finite-
difference model with distensible vessels. Kufahl &
Clark's dog model contained thirty-five vessels, both
steady flow and pulsatile flow were studied.
Beginning in the late eighties, investigators
started to simulate the human cerebral circulation.
Adapting the animal models to generally simulate human
cerebral circulation was not trivial.
The cerebral circulation network of a human is more
complex than the that of an animal. Considerations to
be addressed included the number of arteries it was
necessary to simulate, the selection of those arteries,
and the configuration of the chosen arteries to
represent the functionality of the Circle of Willis.
Cerebral circulation in humans is not consistent
between individuals. Theõnumber of arteries, and
parameters such as artery length and diameter differ
from person to person, limiting the utility of standard
data for individual cases. A successful model must be
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very flexible and robust to handle various simulation
conditions.
The difficulty of obtaining direct measurements in
humans limits the ability to accumulate parameter
information to refine the model.
Clark et al., Neurological Res., 11:217-230 (1989),
developed an elaborate model for the human cerebral
circulation network following the mathematical method
used by Kufahl & Clark, J. Biomechanical Engineering,
107:112-122 (1985). The new model included seventy-
three vessels representing the basic Circle of Willis.
FIG. 2 is the schematic drawing of the 73 vessel model
of Clark et al. After adding naturally occurring
anastomoses, the total number of vessels increased to
eighty-five. When artificial anastomoses were imposed,
the number of vessels increased to eighty-seven.
Duros et al., Neurological Res., 13:217-223 (1991),
built a model that not only contained the cerebral
arteries but also the human body main supply arteries.
The Duros et al. model was used to simulate the rupture
condition of aneurysm.
Beside the fluid dynamic models, several electrical
models were built based on the similarity of the
governing equations of electrical circuits and one-
dimensional linear flow. Moreover, electrical networks
are good at simulating networks with capacitance and
resistance. Electrical network models are also well
understood and provides convenient abstractions. Roller
& Clark, J. Biomechanics, 2:244-251 (1969) simulated the
pulsatile flow and flexible vessel wall using
transmission line theory. Hellal, Comput. Biol. Med.,
24:103-118 (1994), built an electrical model using

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transmission line equations derived from the linearized
Navier-Stokes equation and vessel wall deformation
equation.
Differences between the Existing Models.
All existing cerebral circulation models are one-
dimensional. All fluid dynamic-based models are
electrical models that begin with the same set of
governing equations, namely the one-dimensional
continuity equation and Napier-Stokes equation. In
those models, the following assumptions must be made in
order to derive the equations [Long et al., J. Fluid
Mech., 55:493-511 (1972); Imaeda, Ph.D. thesis, Univ. of
Waterloo (1975); Imaeda et al., J. Biomechanics,
13:1007-1021 (1980); nerm et al., Handbook of
Engineering (McGraw-Hill, New York: 1987), pp. 21.1-
21.21]: (i) The flow is radially symmetric and well-
developed laminar flow; (ii) blood is an incompressible,
Newtonian fluid; and (iii) external forces (e.g.
gravitational force) are negligible.
Because the most important components of interest
are flow and pressure distributions, the one-dimensional
integrated continuity and momentum equations are more
convenient. The integrated governing equations are
[Kufahl & Clark, J. Biomechanical Engineering, 107:112-
122 (1985); Raines et al., Proc. of the Summer Computer
Simulation Conference (MIT Press, Boston: 1975), pp.
890-900; Raines et al., J. Biomechanics, 7:77-91 (1974);
Proenta et al., J. Biomechanical Eng., 108:161-167
(1986)]:
continuity equation:

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aQ+ aA _
(1) ax at -o

momentum equation:
(2) aQ+AaPzDz
ax ax P ax p

where Q is a quantum flow and A is cross-sectional area.
T is the shear stress at the wall. The convective term
a(Q2 IA)
ax
makes the governing equation set nonlinear.
The main differences between the various models are
how they define the problem (e.g. whether the flow is
steady or pulsatile, whether the vessel wall is rigid or
elastic, and whether a linear or non-linear governing
equation is used to describe the flow field) and how
they solve the problem (e.g. whether an analytical
method or numerical method is used).
Definition of the Problem.
Even when the assumption is made that blood is
Newtonian and flow is laminar, the governing equations
are still non-linear because of the existence of the
convective term in the momentum equation. For pipe
flow, The convective term can be ignored because the
gradient of the velocity is very small. The result is a
linearized Navier-Stocks equation. If it is further
assumed that the vessel wall is rigid and the flow is
steady, the problem becomes Poiseuille flow, which can

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WO 99/38433 PCT/US99/02276
be solved analytically by applying the well-known Hagen-
Poiseuille formula.

(3) Q,rRZ\Pu-Pbl
8 e
where R is the pipe radius and 1 is the pipe length.
For the rigid vessel unsteady pulsatile flow,
Wormersley, Phys. Med. Biology, 2:178-187 (1957), gave
an analytical solution. Ling & Attack, J. Fluid Mech.,
55:493-511 (1972), extended the Wormersley model by
taking into account the consideration of non-linear
effects and distensibility of the blood vessel. The
finite difference technique was employed to solve the
problem numerically. To date, most existing models can
handle the non-linear, pulsatile flow and distensible
vessel wall problem.
From the definition of the problem, a computer
model can be divided into two categories.
The first type of models applied the linear
governing equation and rigid vessel assumption. For
steady flow, the solution is given by the Hagen-
Poiseuille formula. For unsteady flow, the solution is
given by the Wormersley model. Examples of models of
this type include Clark et al. Acta Neurol. Scandinav.,
43:189-204 (1967), and Hillen et al., J. Biomechanics,
21:807-814 (1988). Because some of the electrical
models [Roller and Clark, J. Biomechanics, 2:244-251
(1969); Helal, Comput. Bio. Med., 24:103-118 (1994)]
were derived from the Hagen-Poiseuille formula, they
belong to this type. The advantages of a model with
linear governing equation and rigid vessel assumptions


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WO 99/38433 PCT/US99/02276
is that the model is simple and an analytical solution
exists. Thus sensitivity analysis of parameters can be
performed. It also offers the possibility of gaining
insight into mechanisms that govern the flow in the
cerebral network.
The other type is non-linear, pulsatile flow and
distensible vessel models. Examples of models of this
type include Kufahl & Clark [Kufahl and Clark, J.
Biomechanical Engineering, 107:112-122 (1985); Clark et
al., Neurological Research, 11:217-230 (1989); Kufahl,
Ph.D. thesis (Univ. of Illinois, Urbana: 1980)] Hillen
et al. [Hillen et al., J. Biomechanics, 15:441-448
(1982); Hillen et al., J. Biomechanics, 19:187-194
(1986)] and Duros et al., Neurological Research, 13:217-
223 (1991).
The advantage of this kind of model is that the
non-linearity, pulsatile flow and distensible vessels
are more accurate than the first type's in describing
the physical behavior of blood flow in arteries. For
instance, Ling and Attack, J. Fluid Mechanics, 55:493-
411 (1972), point out that the non-linear term cannot be
neglected. Kufahl and Clark, J. Biomechanical
Engineering, 107:112-122 (1985), showed that pulsatile
flow resulted in a 10 percent increase of the total flow
and contributed to some flow redistribution.
Problem Solution.
For the first type of problem, analytical solutions
exist. For the second type of problem, numerical
techniques must be used. The most commonly used
computational fluid dynamics tools are finite-difference
(FD), finite-element (FE) and finite volume (FV).

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In the task of simulating the human cerebral
circulation system, the finite-difference technique is
widely used [Kufahl & Clark, J. Biomechanical
Engineering, 107:112-122 (1985); Clark et al.,

Neurological Research, 111:217-230 (1989); Duros et al.,
Neurological Research, 13:217-223 (1991); Kufahl, Ph.D.
thesis (Univ. of Illinois, Urbana, 1980)] because the
application of finite difference approach is
straightforward and it can achieve certain accuracy with
modest computational resources. However the finite
difference approach requires high regularity of the
grid, and this restrains the approach to be used in
solving complex geometry problems.
In contrast, the finite element approach can handle
the complex geometry problem very easily, but the
shortcoming of this method is that it needs much more
computation time than the finite difference method.
Cerebral network models usually contain hundreds of
arteries and tens of bifurcations, which makes the use
the finite element approach difficult using presently
available computer equipment. To apply the finite
element method in the network simulation, powerful
computers are needed. With advances in computation
technology, the finite element method can be applied in
cerebral network simulation. The present invention
currently incorporates the finite difference approach.
Application.
Stroke is the second leading cause of death in the
United States, as well as in most western countries.
Among the many approaches available for cerebral
revascularization (e.g. angioplasty, endarterectomy,
byphase and embolectomy), the procedure of choice for

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each particular patient is at least in theory the one
that restores cerebral blood flow. However because of
the complex architecture of cerebral circulation, each
patient has a unique dimensional structure. As a
result, any surgical treatment can have different
effects on different patients.
Computer models can simulate the cerebral
circulation under "normal" conditions. Computer models
can also be used to predict the results of potential
treatment procedures. However, there is no system
currently available which forms a comprehensive model
customized to the patient or which allows a user to
perturb that model at will.
Hillen et al., J. Biomechanics, 15:441-448 (1982),
built a non-linear one dimensional model to study the
functional significance of the Circle of Willis. The
model consisted of two afferent and two efferent
arteries connected by the posterior communicating
artery. Hillen et al. found that in normal cases, the
flow in the posterior communicating artery was towards
the posterior cerebral artery, and that the flow
direction in posterior communicating artery depended on
the ratio of peripheral resistance. Raising the ratio
significantly would change the flow direction, therefore
Hillen et al. postulated the formation of a dead point
where flow in the posterior communicating artery
approaches zero.
Kufahl and Clark, J. Biomechanical Engineering,
107:112-122 (1985), developed a 35-vessel computer model
for dog circulation. The model was non-linear, one-
dimensional with pulsatile flow and distensible vessel
walls. The computer simulation revealed the large drop
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in pressure over the length of the middle cerebral due
to the large fluid friction at the vessel wall. Large
pulses of flow were found in the common carotid but
smaller pulses occurred farther downstream.
The comparison by Kufahl and Clark of running the
model with steady flow versus pulsatile flow showed that
the total rate increased as the effect of pulsatile
flow. Kufahl and Clark also found flow redistribution
in some arteries.
A series of stenosis were simulated by Kufahl and
Clark to study the efficacy of the Circle of Willis in
maintaining the flow under arterial disease. The middle
cerebral flow was found to be well-maintained under
different cases, including the few cases in which the
pulse vanished.
Hillen et al., J. Biomechanics, 21:807-814 (1988),
extended their previous model [Hillen et al., J.
Biomechanics15:441-448 (1982)] to include 18 vessels.
The effect of asymmetry on the flow distribution in
afferent and efferent vessels and the possibility of the
occurrence of a "dead point" in the posterior
communicating artery were studied. The authors found
that a slight asymmetric change in the right posterior
communicating artery (doubling the diameter) could
result in noticeable effects in the flow distribution
(flow in the left carotid artery exceeded that in the
right carotid artery, a small flow was present in the
anterior communicating artery), but the pressure seemed
to be unaffected. The authors pointed out that a dead
point in the posterior communicating artery cannot occur
in humans.

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Hillen et al., J. Biomechanics, 21:807-814 (1988),
simplified their model by ignoring pulsatility and
vessel wall elasticity. From the comparison of the
simple model and the previous model, they concluded that
the deletion of pulsatility and vessel wall elasticity
caused only a slight decrease of the flows without any
flow redistribution. This finding was in contrast to
the study of Kufahi and Clark, J. Biomechanical
Engineering, 107:112-122 (1985), in which the flow
redistribution was found.due to pulsatile flow. The
authors concluded the contradiction was caused by the
ways in which the terminal resistances were determined
in the different models. Hillen et al. used the Hagen-
Poiseuille resistance, whereas Kufahl and Clark
calculated energy losses from a quasi two-dimensional
velocity profile that is approximated by a sixth degree
polynomial.
Clark et al., Neurological Research, 11:217-230
(1989), built four computer models for the human Circle
of Willis. The first model contained 73 vessels ("model
73") representing the basic circle and served as a
benchmark. The second model was constructed by adding
the naturally-occurring secondary anastomosis, the total
number of vessels increased to 85 ("model 85"). Adding
an artificial anastomosis between the frontal artery and
the middle cerebral artery in model 73 and model 85
resulted in model 75 and model 88, respectively.
In order to evaluate the ability of each of the
anastomotic vessels under normal conditions, Clark et
al. studied five cases. In each case, a slight increase
in the diameter of the anastomoses was observed over the
previous model. The results showed that the EC-IC

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bypass was very beneficial when non natural anastomoses
were present.
In another five cases, 90 percent stenosis was
imposed in the middle cerebral artery. The same studies
were performed by Clark et al. as before. Clark et al.
found that the anastomotic channels helped supply the
ischemic areas, the larger the diameter, the better the
supply. And again, the artificial anastomoses were most
beneficial when the natural anastomoses were not
present.
Later Charbel et al. ["Predictive value of a
computerized model of the cerebral circulation", 44th
Annual Meeting of Congress of Neurological Surgeons
(Chicago, 1994); "Validation and clinical potential of a
computerized model of the cerebral circulation", 15c
Annual Meeting on the Joint Section on Cerebrovascular
Surgery of the AANS & CNS (1996)] introduced both
quantitative, semi-quantitative, as well as direct
quantitative methods of validating a patient-specific
computer model by directly measuring flow in blood
vessels during surgery and comparing the values with the
computer model. More recently, the same group began
utilizing magnetic resonance as another valuable
validation tool, "Phase Contrast MR flow measurement
system using volumetric flow constrained image
interpolation and color coded image visualization", 47th
Annual Meeting of Congress of Neurological Surgeons,
(New Orleans, 1997), thus further bringing computer
modeling within the reach of the clinician.
Duros et al., Neurological Research, 13:217-223
(1991), built a 69 vessel model to simulate the cerebral
circulation with an aneurysm. The mathematical method

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used in the model was the same as Kufahl and Clark, J.
Biomechanical Engineering, 107:112-122 (1985). The
Duros model differed from the other models in that the
model not only contained cerebral arteries, but also
contained 30 main supply arteries to different organs of
the human body.
The aneurysm was balloon-shaped, elastically
tapered with zero distal flow and was placed at the
junction of the internal carotid, the anterior cerebral
and the middle cerebral arteries. To simulate the
condition where a rupture may happen, several parameters
were adjusted: all terminal vessels' resistance values
were enlarged by a factor of 10; two 80 percent stenoses
were placed in the middle cerebral artery and anterior
cerebral artery. Systemic pressure was increased to 150
mm Hg to represent hypertension. The compliance
coefficient of the aneurysm was set to 1.5 to represent
a stiff wall condition.
Duros et al. focused on the pressure propagation
inside the aneurysm and found that the pressure did not
change with the neck diameter, but the pressure peak
value increased with increasing the sack diameter. In
order to achieve a high pressure (310 mm Hg) which may
trigger the rupture of the aneurysm, hypertension,
increased number of reflecting sites in both the near
and far fields, and arteriosclerotic arteries were
needed.
Computational Fluid Dynamic Modeling of Cerebral
B1ood Flow During Carotid Occlusion.
The surgical treatment of giant aneurysms or head
and neck neoplasms often requires permanent occlusion of
the internal carotid artery. Tolerance to permanent

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carotid artery sacrifice is traditionally assessed by
temporarily occluding the carotid artery. This
procedure is not without risk. The computational model
of cerebral circulation of the present invention is a
safer alternative to the balloon occlusion test (BOT).
The model used by the system 10 was used to create
a virtual replica of the Circle of Willis and compute
network blood flow using the finite difference method.
To evaluate the ability of the model to identify
patients who tolerate permanent carotid occlusion, the
difference in ipsilateral computed middle cerebral
artery flow between patients passing and failing the
balloon occlusion test was determined prospectively.
Each patient underwent four vessel angiography and
awake temporary occlusion of the internal carotid
artery. Failure of the BOT was defined as appearance of
any one of the following: a neurological deficit during
the BOT, slowing of EEG wave patterns, and a drop in
RSO2 of >10 percent.
Five of the 22 patients failed the BOT. The change
in computed middle cerebral artery flow was 7+/- 0.74
cc/min for patients passing the BOT (p<0.001).
The value of computational fluid dynamics (CFD) in
predicting the outcome of BOT is striking. The results
suggest a role for CFD in the evaluation of patients for
permanent artery occlusion.
The numerical model of the cerebral circulation was
utilized along with the data from other supplemental
diagnostic modalities to evaluate cerebrocirculatory
collateral function during the balloon occlusion test
(BOT).

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EEG, transcranial Doppler, cerebral oximetry,
SPECT, were also done along with the computational
modeling for patients undergoing temporary occlusion of
the carotid to assess cerebral circulation when
permanent occlusion is needed.
Case example: A 49 year old female displayed
diplopia and headache. Upon investigation, she was
found to have a large right cavernous internal carotid
artery (ICA) aneurysm. A computer analysis of her
cerebral blood flow was done to assess her cerebral
circulation, focusing mainly the total middle cerebral
artery flow. The numerical value of the total middle
cerebral artery blood flow with and without occlusion of
the ipsilateral ICA was predicted by the computer flow
to be almost the same.

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WO 99/38433 PCTIUS99/02276
Vessel Flow before Flow after
balloon occlusion balloon
occlusion
Left Middle 83 81
cerebral artery
and branches 40 40
17 16
17 16
16 17

Total MCA Flow 169 172
Right Middle 80 79
cerebral artery
38 35
and branches
18 17
17 17
17 15

Total MCA Flow 169 172
In view of the predicted toleration of the occlusion,
the patient underwent balloon occlusion of the ICA on
the right side, just distal to the aneurysms. The
patient displayed good flow in both cerebral hemispheres
with minimal changes in total computed middle cerebral
artery flow. Concomitantly, there were no change in
amplitude in EEG waves, recordings of cerebral oximetry
using near infrared spectroscopy, transcranial Doppler
(TCD), or clinical deterioration.



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WO 99/38433 PCT/US99/02276
Temporary balloon occlusion is invasive and adds
risk to the evaluation of patients who may already be at
risk for infarction. The numerical model of the
cerebral circulation is employed as an aid in the
evaluation of patients for permanent internal carotid
occlusion.
Pulsatile pressure and flow in normal and diseased
vessels (e.g. aneurysms, stenoses) can also be
simulated. Modeling of network flow with anatomical
variations, bypasses, or lepto-meningeal collateral
vessels is also possible.
In this particular case, this model was utilized to
simulate changes in the middle cerebral artery blood
flow before and after the balloon occlusion. If the
blood flow is decreased in a region of the Circle of
Willis after occlusion of the carotid artery, the
simulation shows red at the region receiving decreased
flow, and displays numbers corresponding to the
magnitude of the decrease. If the blood flow is
increased in a region of the Circle of Willis after
occlusion, the simulation shows green at the region
receiving increased flow, and denotes the magnitude.
From the simulation output, the user can estimate
whether the patient will tolerate the occlusion (or
would pass the balloon occlusion test).
Further Development of the Model.
Bifurcation.
Flow branching at arterial bifurcations is an
important factor in the progression of vascular disease.
This blood flow through the artery bifurcation was

36


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WO 99/38433 PCT/US99/02276
extensively studied both by experimental and numerical
methods.
Liepsch, Biorheology, 21:571-586 (1984), studied
the non-Newtonian fluid in a T-shaped bifurcation.
Perktold et al., Biorheology, 26:1011-1030 (1989),
studied a Y-shaped bifurcation with an aneurysm in the
cerebrovascular tree. Walburn, J. Biomechanical
Engineering, 104:66-88 (1982), studied steady flow and
pulsatile flow through the aortic bifurcation, secondary
flow was not observed during the'pulsatile flow but was
observed during the steady flow. Fukushima et al., J.
Biomechanical Engineering, 110:161-171 (1988), found
secondary flow in both steady and pulsatile flow,
however, the influence of the secondary flow is smaller
during the pulsatile flow than during the steady flow.
To take the importance of the secondary flow into
consideration, a few three dimensional computer models
have been built. Wille, J. Biomechanical Engineering,
6:49-55 (1984), built a single three-dimensional model
of aortic bifurcation with steady flow. Yung et al., J.
Biomechanical Engineering, 112:189-297 (1990), studied
the steady flow in the aortic bifurcation. In order to
generate the mesh conveniently, Yung et al. used a non-
physiological area ration of 2.0 to produce flow
geometry. Perktold & Peter, J. Biomechanical
Engineering, 12:2-12 (1990) studied the wall shear
stress in a three-dimensional model of a T-shaped
bifurcation. Perktold [J. Biomechanical Engineering,
13:464-475 (1991); J. Biomechanics, 24:409-420 (1991)]
analyzed non-Newtonian characteristics, wall shear
stress and pulsatile flow in a three-dimensional model
of carotid bifurcation. Rindt et al., built a three-
37


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WO 99/38433 PCT/US99/02276
dimensional model for carotid bifurcation, but they used
steady flow and rigid vessel wall.
Blood Vessel Wall Elasticity.
The simplest blood vessel wall model is a rigid
tube. However vessel wall elasticity has an important
effect on the blood flow wave propagation. For a large
artery, the vessel wall is composed of three distinct
layers, an intima, a media and an adventitia. Each of
these layers has a unique function. The vessel wall is
not only elastic but also viscoelastic. The vessel
elasticity was modeled by a relationship between blood
vessel cross-sectional area and local pressure. Raines
proposed a widely used model:

(4) A(p,x)= A(p.,x)+ /3ln(p/ p,,)
Another quadratic form used by Porenta, Balar, and
Stergiopulos is :

(5) A(x) =A. (x1+C,, (p-põ)+Co(p-pj]
where 0, C'o and C'1 are constants which are determined
by experiment.
These models are purely elastic models. However
Patel and Vaishnav verified the existence of the
viscoelasticity in arterial walls through a dynamic
experiment. Reuderink et al. found that neglecting the
viscoelasticity of the tube wall could result in an
underestimation of both phase velocity and damping.
Hawley discovered that a viscoelastic wall model yielded
results closer to normal physidlogical reality than an
38
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WO 99/38433 PCT/US99/02276
elastic wall model. For more discussion about the
vessel wall mechanics property, refer to Fung.
Artery Curvature.
In most computer models, arteries were treated as
straight tubes. But the structure of the Circle of
Willis is three-dimensional, and arteries are bent.
Curvature together with pulsatile flow greatly affect
wall shear stress and cause secondary flow which may be
important in understanding the progression of
neurovascular disease. Although curvature was not
considered in intracranial cerebral circulation models,
blood flow in extracranial curved arteries such as the
aortic arch, carotid siphon and coronary arteries were
analyzed by a number of researchers. Friedman and
Ehrlich digitized the radiography of human aortic
bifurcation to obtain the contours of the artery and
performed the computation of steady two-dimensional flow
in the region. The results suggested that wall slope
may be an important factor affecting the variability of
shear stress along the medial wall. The variation in
the curvature of the proximal iliac entries may affect
the susceptibility of these arteries to vascular
disease. Chang and Tarbell simulated pulsatile flow in
the aortic arch and found that the secondary flow was
nearly as large as the axial flow component, the
secondary flow was complex with up to seven vortices,
peak axial and highest r.m.s. wall shear stress were
found at the inside wall. Also the axial-flow direction
was reversed at the inside wall.
Chang and Tarbel simulated flow in the coronary
artery using the same method of Chang and Tarbel. They
found that flow was quasi-steady under resting

39


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WO 99/38433 PCT/US99/02276
conditions but markedly unsteady under exciting
conditions. Only a single secondary flow vortex was
found. Perktold et al. used two different entrance
velocity profiles to study the flow pattern in a
slightly curved segment of the left main coronary
artery. The different velocity profiles only resulted
in significant differences in the immediate vicinity of
the inlet, the difference does not persist far into the
artery, and significant influence of the secondary flow
on the wall shear stress distribution was found.
Perktold et al. analyzed flow in the carotid siphon
and left main coronary artery. They found that the
maximum secondary flow velocities were on the order of
three to four percent of the maximum axial velocity and
the secondary flow has an important influence on the
wall shear stress distribution.
Some studies of curved arteries include Chandral et
al. More general discussion can be found in Pedley.
Non-Newtonian Property of Blood.
It is commonly believed that the blood behaves as a
Newtonian fluid in the large blood vessel due to the
high shear rate. However, some studies indicated that
there were.some non-Newtonian effects in the large
vessel. It was also found that non-Newtonian effects
exist in the low shear area near bends and bifurcations.
Xu used the Casson model to investigate the non-
Newtonian effect in artery bifurcation. They found no
great differences in velocity profiles. Lou and Yang
used a weak-form Casson model to study the non-Newtonian
effect in aortic bifurcation. The results indicated
that the non-Newtonian property of blood did not
drastically change the flow pattern, but caused an



CA 02319119 2000-07-26

WO 99/38433 PCT/US99/02276
appreciable increase in the shear stress and a slightly
higher resistance to both flow separation and the phase
shifts between flow layers. Dutta and Tarbell applied a
simple power law model in an elastic artery and again
found the viscoelasticity of the blood does not appear
to influence its flow behavior under physiological
conditions in large arteries.
Conclusion
The evolution of models of the cerebral circulation
has paralleled the progress in the fields of electrical
engineering, fluid dynamics and computer science. With
each new possibility of simulation, a closer approach to
"physiological" modeling was achieved. The ideal model
would be patient specific, highly predictive, and
reflective of actual conditions both at the macro and
micro circulation level. In addition, such and ideal
model would be easily re-configurable to conform with
the "real time" demands of clinical medicine.
Although computer models of various degrees of
sophistication have been proposed to simulate the
cerebral circulation, they have been used mainly as
theoretical and research tools. Turning them into tools
for clinical applications has the potentially powerful
benefit of predicting the result of neurovascular
reconstructive procedures. However, none of the
published models has ever been validated in vivo using
quantitative human data, partly because of the lack of
available tools for measuring such data.
A specific embodiment of a method and apparatus for
modeling cerebral circulation according to the present
invention has been described for the purpose of
illustrating the manner in which the invention is made

41


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WO 99/38433 PCT/US99/02276
and used. It should be understood that the
implementation of other variations and modifications of
the invention and its various aspects will be apparent
to one skilled in the art, and that the invention is not
limited by the specific embodiments described.
Therefore, it is contemplated to cover the present
invention any and all modifications, variations, or
equivalents that fall within the true spirit and scope
of the basic underlying principles disclosed and claimed
herein.

42

Representative Drawing
A single figure which represents the drawing illustrating the invention.
Administrative Status

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Administrative Status

Title Date
Forecasted Issue Date 2008-04-22
(86) PCT Filing Date 1999-02-03
(87) PCT Publication Date 1999-08-05
(85) National Entry 2000-07-26
Examination Requested 2004-02-03
(45) Issued 2008-04-22
Expired 2019-02-04

Abandonment History

Abandonment Date Reason Reinstatement Date
2003-02-03 FAILURE TO PAY APPLICATION MAINTENANCE FEE 2003-06-06

Payment History

Fee Type Anniversary Year Due Date Amount Paid Paid Date
Application Fee $300.00 2000-07-26
Maintenance Fee - Application - New Act 2 2001-02-05 $100.00 2001-01-22
Registration of a document - section 124 $100.00 2001-07-19
Registration of a document - section 124 $100.00 2001-07-19
Maintenance Fee - Application - New Act 3 2002-02-04 $100.00 2002-01-31
Reinstatement: Failure to Pay Application Maintenance Fees $200.00 2003-06-06
Maintenance Fee - Application - New Act 4 2003-02-03 $100.00 2003-06-06
Request for Examination $800.00 2004-02-03
Maintenance Fee - Application - New Act 5 2004-02-03 $200.00 2004-02-03
Maintenance Fee - Application - New Act 6 2005-02-03 $200.00 2005-01-25
Maintenance Fee - Application - New Act 7 2006-02-03 $200.00 2006-01-19
Maintenance Fee - Application - New Act 8 2007-02-05 $200.00 2007-01-23
Final Fee $300.00 2007-12-19
Maintenance Fee - Application - New Act 9 2008-02-04 $200.00 2008-01-25
Maintenance Fee - Patent - New Act 10 2009-02-03 $250.00 2009-01-19
Maintenance Fee - Patent - New Act 11 2010-02-03 $250.00 2010-01-18
Maintenance Fee - Patent - New Act 12 2011-02-03 $250.00 2011-01-17
Maintenance Fee - Patent - New Act 13 2012-02-03 $250.00 2012-01-17
Maintenance Fee - Patent - New Act 14 2013-02-04 $250.00 2013-01-17
Maintenance Fee - Patent - New Act 15 2014-02-03 $450.00 2014-02-03
Maintenance Fee - Patent - New Act 16 2015-02-03 $450.00 2015-02-02
Maintenance Fee - Patent - New Act 17 2016-02-03 $450.00 2016-02-01
Maintenance Fee - Patent - New Act 18 2017-02-03 $650.00 2017-03-06
Maintenance Fee - Patent - New Act 19 2018-02-05 $450.00 2018-01-29
Owners on Record

Note: Records showing the ownership history in alphabetical order.

Current Owners on Record
THE BOARD OF TRUSTEES OF THE UNIVERSITY OF ILLINOIS
Past Owners on Record
ALPERIN, NOAM
CHARBEL, FADY T.
CLARK, M. E.
LOTH, FRANCIS
QUEK, FRANCIS
SADLER, LEWIS
ZHAO, MEIDE
Past Owners that do not appear in the "Owners on Record" listing will appear in other documentation within the application.
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Representative Drawing 2000-11-08 1 21
Cover Page 2008-03-27 2 48
Abstract 2000-07-26 1 70
Claims 2000-07-26 10 356
Drawings 2000-07-26 5 118
Description 2000-07-26 42 1,815
Cover Page 2000-11-08 2 64
Description 2006-10-27 44 1,869
Claims 2006-10-27 5 148
Representative Drawing 2007-06-11 1 12
Prosecution-Amendment 2004-02-03 1 46
Correspondence 2000-10-13 1 2
Assignment 2000-07-26 3 99
PCT 2000-07-26 7 260
Assignment 2001-07-19 9 277
Fees 2003-06-06 1 40
Fees 2002-01-31 1 35
Fees 2001-01-22 1 35
Prosecution-Amendment 2004-10-15 1 30
Prosecution-Amendment 2006-04-28 4 112
Prosecution-Amendment 2006-10-27 13 404
Correspondence 2007-12-19 2 52