U.S. patent application number 17/700151 was filed with the patent office on 2022-06-30 for modeling and simulation system for optimizing prosthetic heart valve treament.
This patent application is currently assigned to Stenomics, Inc.. The applicant listed for this patent is Stenomics, Inc.. Invention is credited to Michael A. SINGER.
Application Number | 20220208389 17/700151 |
Document ID | / |
Family ID | 1000006211318 |
Filed Date | 2022-06-30 |
United States Patent
Application |
20220208389 |
Kind Code |
A1 |
SINGER; Michael A. |
June 30, 2022 |
MODELING AND SIMULATION SYSTEM FOR OPTIMIZING PROSTHETIC HEART
VALVE TREAMENT
Abstract
A computer-implemented method for simulating blood flow through
one or more coronary blood vessels may first involve receiving
patient-specific data, including imaging data related to one or
more coronary blood vessels, and at least one clinically measured
flow parameter. Next, the method may involve generating a digital
model of the one or more coronary blood vessels, based at least
partially on the imaging data, discretizing the model, applying
boundary conditions to a portion of the digital model that contains
the one or more coronary blood vessels, and initializing and
solving mathematical equations of blood flow through the model to
generate computerized flow parameters. Finally, the method may
involve comparing the computerized flow parameters with the at
least one clinically measured flow parameter.
Inventors: |
SINGER; Michael A.;
(Belmont, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Stenomics, Inc. |
Belmont |
CA |
US |
|
|
Assignee: |
Stenomics, Inc.
Belmont
CA
|
Family ID: |
1000006211318 |
Appl. No.: |
17/700151 |
Filed: |
March 21, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16578030 |
Sep 20, 2019 |
11315690 |
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17700151 |
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14850648 |
Sep 10, 2015 |
10497476 |
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16578030 |
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14264544 |
Apr 29, 2014 |
9135381 |
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14850648 |
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61822133 |
May 10, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G09B 23/303 20130101;
G06F 30/23 20200101; G06F 17/18 20130101; G16H 50/50 20180101 |
International
Class: |
G16H 50/50 20060101
G16H050/50; G09B 23/30 20060101 G09B023/30; G06F 30/23 20060101
G06F030/23; G06F 17/18 20060101 G06F017/18 |
Claims
1. A computer-implemented method including a processor for
simulating blood flow through a one or more coronary blood vessels,
the method comprising: receiving patient-specific imaging data by
said processor related to the one or more coronary blood vessels;
receiving at least one patient-specific measured flow parameter by
said processor related to blood flow through the one or more
coronary blood vessels; generating by said processor a geometric
model of the one or more coronary blood vessels, based at least
partially on the imaging data, the geometric model having modeling
parameters; applying boundary conditions by said processor,
corresponding to desired flow, to a portion of the geometric model
that contains the one or more coronary blood vessels, wherein
applying the boundary conditions comprises selecting boundary
conditions based at least partially on patient-specific
measurements; solving mathematical equations of blood flow through
the geometric model by said processor to generate a first set of
computerized flow parameters by simulating blood flow through the
model while the model characterizes physical features of an
anatomic topology of the one or more coronary blood vessels;
comparing by said processor the first set of computerized flow
parameters with the at least one measured flow parameter.
2. A method as in claim 1, wherein selecting the boundary
conditions comprises selecting inflow and outflow boundary
conditions that compensate for at least one of underlying
psychological condition or medical condition.
3. A method as in claim 1, wherein receiving the patient-specific
imaging data comprises receiving at least one of
non-interventionally generated data or minimally invasively
generated data.
4. A method as in claim 1, wherein generating the geometric model
comprises generating the geometric model based at least partially
on the imaging data and at least partially on the at least one
measured flow parameter.
5. A method as in claim 1, further comprising performing at least
one of a sensitivity analysis or an uncertainty analysis on the
first and second sets of computerized flow parameters.
6. A method as in claim 1, further comprising using the geometric
model for at least one of diagnosing a disease state, assessing a
disease state, determining a prognosis of a disease state,
monitoring a disease state, planning patient treatment or
performing patient treatment.
7. A method as in claim 1, wherein receiving the patient-specific
imaging data comprises receiving the imaging data from an imaging
modality selected from the group consisting of echocardiography,
ultrasound, magnetic resonance imaging, x-ray, optical tomography
and computed tomography.
8. A method as in claim 1, wherein receiving the at least one
measured flow parameter comprises receiving a parameter selected
from the group consisting of Doppler echocardiograph,
catheterization and a functional magnetic resonance image.
9. A computer-implemented method including a processor for
generating a geometric model of one or more coronary blood vessels,
the method comprising: receiving patient-specific imaging data by
said processor of the one or more coronary blood vessels;
generating by said processor a geometric model of the one or more
coronary blood vessels, based at least partially on the imaging
data, the geometric model having modeling parameters and model
boundaries representing physical features of an anatomic topology
of the one or more coronary blood vessels; modeling blood flow
through the geometric model to generate a first set of computerized
flow parameters by said processor, wherein generating the first set
of computerized flow parameters comprises applying boundary
conditions, corresponding to desired flow, to a portion of the
geometric model that contains the one or more coronary blood
vessels, and wherein applying the boundary conditions comprises
selecting boundary conditions based at least partially on
patient-specific measures; comparing by said processor the first
set of computerized flow parameters with at least one measured flow
parameter.
10. A method as in claim 9, wherein generating the first set of
computerized flow parameters further comprises: discretizing the
geometric model; and solving mathematical equations of blood flow
through the geometric model.
11. A method as in claim 9, wherein receiving the patient-specific
imaging data comprises receiving at least one of
non-interventionally generated data or minimally invasively
generated data.
12. A method as in claim 9, wherein receiving the patient-specific
imaging data comprises receiving the imaging data from an imaging
modality selected from the group consisting of echocardiography,
ultrasound, magnetic resonance imaging, x-ray, optical tomography
and computed tomography.
13. A method as in claim 9, wherein receiving the at least one
measured flow parameter comprises receiving a parameter selected
from the group consisting of a Doppler echocardiograph, a
catheterization and a functional magnetic resonance image.
14. A method as in claim 9, further comprising performing at least
one of a sensitivity analysis or an uncertainty analysis on the
first set of computerized flow parameters.
15. A method as in claim 9, further comprising using the adjusted
geometric model for at least one of diagnosing a disease state,
assessing a disease state, determining a prognosis of a disease
state, monitoring a disease state, planning patient treatment or
performing patient treatment.
16. A system for generating a geometric model including a processor
of one or more coronary blood vessels, the system comprising at
least one computer system configured to: receive patient-specific
imaging data by said processor of the one or more coronary blood
vessels; receive at least one patient-specific measured flow
parameter by said processor related to blood flow through the one
or more coronary blood vessels; generate by said processor a first
geometric model of the one or more coronary blood vessels, based at
least partially on the imaging data, the geometric model having
modeling parameters and model boundaries representing physical
features of an anatomic topology of the one or more coronary blood
vessels; model blood flow through the first geometric model by said
processor to generate a first set of computerized flow parameters,
wherein generating the first set of computerized flow parameters
comprises applying boundary conditions, corresponding to desired
flow, to a portion of the geometric model that contains the one or
more coronary blood vessels, and wherein applying the boundary
conditions comprises selecting boundary conditions based at least
partially on patient-specific measurements; compare by said
processor the first set of computerized flow parameters with
measured flow parameters.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] The present application is a continuation of U.S. patent
application Ser. No. 16/578,030, filed Sep. 20, 2019, which is
continuation of U.S. patent application Ser. No. 14/850,648, filed
Sep. 10, 2015, now U.S. Pat. No. 10,497,476, issued Dec. 3, 2019,
which is a continuation of U.S. patent application Ser. No.
14/264,544, filed Apr. 29, 2014, now U.S. Pat. No. 9,135,381,
issued Sep. 15, 2015, which claims priority to U.S. Provisional
Patent Application No. 61/822,133, filed May 10, 2013. The full
disclosures of all of the above-listed patent applications are
hereby incorporated by reference herein.
FIELD
[0002] The present disclosure relates generally to the field of
computer-aided modeling and simulation. More specifically, the
disclosure relates to computer-based systems and methods for
modeling cardiac anatomy and physiology for simulation,
therapeutic, treatment, and/or diagnostic purposes.
BACKGROUND
[0003] Cardiovascular disease is the leading cause of death in the
United States and claims the lives of more than 600,000 Americans
each year. According to the World Health Organization,
cardiovascular disease is the leading cause of death worldwide and
claims the lives of approximately 7 million people per year.
Further, according to the American Heart Association (AHA), more
than five million Americans are diagnosed with heart valve disease,
which is a form of cardiovascular disease, each year, and diseases
of the aortic and mitral valves are the most prevalent. Combined,
aortic and mitral valve diseases affect more than five percent of
the U.S. population. Hence, it is clear that cardiovascular
disease, and heart valve disease in particular, is a major health
concern and impacts the lives of numerous people.
[0004] Aortic stenosis (AS), which is a form of aortic valve
disease, is a ubiquitous and potentially life-threatening disease
that impacts approximately 1.5 million people in the United States
and is the third most common cardiovascular disorder in the western
world. Aortic stenosis is a general term that characterizes the
abnormal operation of the heart valve that separates the left
ventricle from the ascending aorta, and AS may or may not be
symptomatic. A stenosed aortic valve (AV) that does not open
completely leads to abnormal blood flow through the valve and the
aortic root. These abnormal flow patterns may lead to increased
vascular resistance and insufficient downstream perfusion. In
addition, an AV that does not close properly may lead to aortic
regurgitation (AR), in which reverse flow traverses the AV during
diastole when the valve is supposed to be closed completely.
[0005] Mitral regurgitation (MR), which is a form of mitral valve
disease, is also a widespread and potentially life-threatening
disease. In the United States, the occurrence of MR increases with
age. In a study conducted in 2000, at least moderate MR was
observed in 0.5% of participants aged 18 to 44 years and in 9.3% of
participants aged 75 years or greater. In Europe, MR is the second
most frequent valvular disease requiring surgery. Similar to aortic
regurgitation, mitral regurgitation is a general term that
characterizes the abnormal operation of the mitral valve, which is
the valve that separates the left atrium from the left ventricle.
When the mitral valve does not close properly, blood may leak from
the ventricle into the atrium during contraction of the left
ventricle and thereby decrease the pumping efficiency of the heart.
In contrast to dysfunctional aortic valves, dysfunctional mitral
valves may be repaired and may not require replacement.
[0006] The prognosis of patients with severe, untreated valvular
heart disease is poor. In the case of AS, for example, clinical
studies of untreated patients have demonstrated that survival rates
are as low as 50% at two years and 20% at five years after the
onset of symptoms. Further, acute mitral regurgitation is poorly
tolerated and carries a poor prognosis in the absence of treatment.
Therefore, it is evident that patients with symptomatic, severely
diseased heart valves should seek treatment.
[0007] Accurate clinical diagnosis is instrumental in determining
the severity and nature of heart valve disease. The American
College of Cardiology (ACC) and the AHA have published medical
guidelines that help characterize the clinical indications for
valvular heart disease and the corresponding clinical treatments.
In the context of AS, diagnosis is dependent on the quantitative
values of various blood flow parameters as well as a visual
inspection of the valve and its operation. The outcome of a patient
examination may be a diagnosis of mild, moderate, severe or
critical AS. Per society guidelines, only patients with
symptomatic, severe or critical AS may be candidates for aortic
valve replacement (AVR), which usually involves open heart surgery.
Similarly, the ACC and AHA have published guidelines to help
diagnose and treat diseases of the other three heart valves, and
these diagnostic methods are based on analysis of medical images
and characteristics of the blood flow.
[0008] Despite the apparent need for treatment, an increasing
number of patients with symptomatic, severe AS are ineligible for
open heart surgery and surgical AVR. Ineligibility for open chest
surgery may be due to significant co-morbidities, such as high
surgical risk, advanced age, history of heart disease or frailty.
These patients have a poor prognosis and may benefit greatly from
alternative therapies and treatments that do not require open chest
surgery.
[0009] For patients deemed inoperable or who do not wish to undergo
an invasive surgical operation, minimally invasive or transcatheter
valve implantation may be an option for improving valvular
function, alleviating symptoms, and improving quality of life.
Transcatheter aortic valve replacement (TAVR), for example, is a
minimally-invasive approach to replace the malfunctioning native
aortic valve with a functional prosthetic valve. During a TAVR
procedure, a prosthetic aortic valve is typically inserted via a
catheter that is introduced via a femoral or transapical pathway.
In contrast to surgical AVR, TAVR does not require a sternotomy
(incision in the center of the chest that separates the chestbone
to allow access to the heart), and a heart-lung machine is not
needed because the heart is not stopped. Further, because the TAVR
procedure is less invasive than surgical AVR, patients generally
spend less time in the hospital, experience shorter recovery times,
and may be less reluctant to undergo the procedure. Transcatheter
valve implantation may also be an option to repair other heart
valves such as the mitral or pulmonary valve. Alternatively,
sutureless heart valves provide a minimally invasive mechanism for
heart valve replacement.
[0010] Despite the apparent benefits of transcatheter valve
replacement, there are serious clinical risks associated with the
procedure. In the case of TAVR, for example, clinically significant
post-procedural AR is a frequent problem and occurs in up to 50% of
patients.
Further, results from clinical trials suggest a linear relationship
between the severity of post-procedural AR and 1- and 2-year
mortality, and even mild AR may be associated with increased
mortality. Therefore, to maximize the potential benefits of TAVR
and minimize the long-term risks to patient well-being, AR should
be minimized as much as possible. Other risks of transcatheter
valve replacement, which are applicable to all percutaneously
deployed heart valves, include stroke, vascular complications,
improper deployment, obstruction of secondary vessels (e.g.,
coronary ostium), and valve migration.
[0011] Minimizing the risks of negative complications following
transcatheter valve implantation requires careful pre-surgical
planning and execution of the procedure. Valvular regurgitation in
the presence of transcatheter aortic heart valves, for example, is
often due to a large mean annulus size, valvular calcification,
and/or improper sizing of the valve. Specifically, paravalvular
regurgitation (i.e., undesired, reverse flow--or leakage--that
occurs between the perimeter of the prosthetic valve and the aortic
annulus) is a frequent occurrence with aortic valves and is often
caused by improper valve sizing. In contrast to surgical valve
replacement, wherein the surgeon may visually inspect the anatomic
structure of the native valve and surrounding vasculature before
implanting the prosthetic valve, transcatheter approaches currently
rely on clinical imaging techniques (e.g., echocardiography,
computed tomography, magnetic resonance imaging) for sizing,
positioning, and deploying the prosthesis. These images may not
provide accurate anatomic information suitable for precise planning
and deployment of transcatheter valves, which may contribute to the
relatively high incidence of complications (e.g., valvular
regurgitation).
[0012] Two-dimensional images of inherently three-dimensional
anatomy may provide inaccurate information for planning and
executing transcatheter and minimally invasive procedures. In
addition, imaging modalities with relatively low spatial resolution
(e.g., ultrasound) may be unable to resolve anatomic structures
that are critical for pre-surgical planning. In the context of
TAVR, for example, relatively low resolution two-dimensional
echocardiographic images are known to underestimate the size of the
aortic annulus; the size of the annulus is used to select the size
of the prosthetic valve. This underestimation of vascular dimension
may lead to deployment of a relatively small prosthetic valve and
thereby contribute to a high incidence of paravalvular
regurgitation because the prosthetic valve is too small to fill the
native annulus. In contrast, relatively high resolution
two-dimensional computed tomography (CT) imaging is known to
overestimate the size of the aortic annulus, and prosthetic valve
sizing based on CT measurements often leads to a lower incidence of
regurgitation.
Hence, while proper sizing and pre-procedural planning of
transcatheter and minimally invasive heart valve procedures is
widely recognized as an essential component for maximizing clinical
benefits, the means by which these heart valves are sized requires
appreciable clinical judgment and is prone to error.
[0013] Therefore, it would be very desirable to have a system and
method for accurately assessing the anatomic size and morphology of
heart valves and the surrounding vasculature. Such a system and
method would ideally facilitate proper selection, sizing,
positioning, and pre-surgical planning of prosthetic heart valve
procedures. Such systems should not expose patients to excessive
risks.
DESCRIPTION OF RELATED ART
[0014] There are many academic and industrial research groups that
use computer modeling and simulation to analyze flow through heart
valves. Historically, valvular hemodynamic analyses have focused on
the aortic heart valve and have employed methods of computational
fluid dynamics (CFD) to provide detailed insight into the blood
flow surrounding the aortic valve. These insights have then been
used to facilitate the design and construction of heart valves with
optimal or near optimal hemodynamic properties that maximize
functionality and durability while minimizing the potentially fatal
risks of valvular malfunction and adverse response.
[0015] In recent years, hemodynamic modeling of heart valves has
included both surgically implanted and transcatheter prostheses,
but the focus of most studies remains the aortic valve. With the
rapidly expanding clinical deployment of transcatheter aortic heart
valves, modeling and simulation has helped understand and
characterize the unique hemodynamic challenges of transcatheter
deployment in comparison to traditional surgical implantation of
aortic valves. In particular, computer modeling has been used to
quantify valvular regurgitation, downstream flow effects in the
aortic arch, leaflet stresses, vascular response, and other
characteristics of valvular implantation that impact device
efficacy, robustness, durability, and longevity.
[0016] To date, all computer modeling and simulation studies of
heart valves are focused on evaluating and improving prosthetic
valve design and function.
BRIEF SUMMARY
[0017] In contrast to currently available systems and methods for
computer modeling of heart valves, the embodiments described herein
involve modeling and simulation systems and methods that may be
used to facilitate the selection, sizing, deployment, and/or
pre-surgical planning of prosthetic heart valves. The systems and
methods may also be used to diagnose and assess diseased heart
valves. Unlike currently available systems, the embodiments
described herein are directed toward anatomic assessment for
diagnostic and pre-surgical planning purposes (e.g., device
selection, sizing, deployment), rather than device design and
function. In various embodiments, the systems and methods may be
applied to any one or more heart valves.
[0018] The modeling and simulation system described herein uses
computer modeling to facilitate sizing and deployment of
transcatheter heart valves (e.g., aortic valve, mitral valve). In
addition to using anatomic and geometric data gathered through two-
and/or three-dimensional imaging studies, the modeling and
simulation system also incorporates physiologic (e.g., hemodynamic)
data into the construction of an accurate anatomic model that
serves as the basis for diagnosis and surgical planning/execution.
Hemodynamic data, which are currently excluded from all valvular
sizing methods, provide three-dimensional insight into local
valvular morphology, which enables an accurate physiologic
assessment for prosthesis sizing and deployment. The sizing and
deployment data obtained from the modeling and simulation system
provide physicians with clinically relevant information that
enables informed decision-making and thereby reduces the
possibilities of adverse clinical events (e.g., valvular
regurgitation). In addition, the system also facilitates
sensitivity and uncertainly analyses, thereby enabling the complete
and accurate planning of heart valve implantation.
[0019] In one aspect, a computer-implemented method for simulating
blood flow through a heart valve may first involve receiving
patient-specific data, including imaging data related to the heart
valve, an inflow tract of the heart valve and an outflow tract of
the heart valve, and at least one clinically measured flow
parameter. Next, the method may involve generating a digital model
of the heart valve and the inflow and outflow tracts, based at
least partially on the imaging data, discretizing the model,
applying boundary conditions to a portion of the digital model that
contains the heart valve and the inflow and outflow tracts, and
initializing and solving mathematical equations of blood flow
through the model to generate computerized flow parameters.
Finally, the method may involve comparing the computerized flow
parameters with the at least one clinically measured flow
parameter. Optionally, the method may further involve adjusting the
digital model after the comparison step. In some embodiments, the
method may also involve, after adjusting the digital model,
re-solving the mathematical equations to generate new computerized
flow parameters. The method may further include comparing the new
computerized flow parameters with the clinically measured flow
parameters.
[0020] In some embodiments, the patient-specific data may be
derived from only non-interventional data collection method(s)
and/or minimally invasive data collection method(s). In some
embodiments, generating the digital model may involve generating
the model based at least partially on the imaging data and at least
partially on the clinically measured flow parameter(s). Optionally,
some embodiments may further involve performing a sensitivity
analysis and/or an uncertainty analysis on the computerized flow
parameters.
[0021] In various embodiments, the digital model may be used for
diagnosing a disease state, assessing a disease state, determining
a prognosis of a disease state, monitoring a disease state,
planning a prosthetic heart valve implantation and/or performing a
prosthetic heart valve implantation. The imaging data may be
derived from any suitable imaging modality, such as but not limited
to echocardiography, ultrasound, magnetic resonance imaging, x-ray,
optical tomography and/or computed tomography. The clinically
measured flow parameter(s) may be measured using any suitable
modality, such as but not limited to Doppler echocardiography,
catheterization and/or functional magnetic resonance.
[0022] In another aspect, a computer-implemented method for
generating an anatomical model of a heart valve may include:
receiving patient-specific imaging data of the heart valve and
inflow and outflow tracts of the heart valve; generating a digital
anatomical model of the heart valve and the inflow and outflow
tracts, based at least partially on the imaging data; modeling
blood flow through the digital model to generate a first set of
computerized flow parameters; comparing the first set of
computerized flow parameters with at least one clinically measured
flow parameter; adjusting the digital model, based on the
comparison of the first set of computerized flow parameters with
the clinically measured flow parameters; modeling blood flow
through the adjusted digital model to generate a second set of
computerized flow parameters; and comparing the second set of
computerized flow parameters with the at least one clinically
measured flow parameter.
[0023] In some embodiments, the method may further involve, before
adjusting the digital model, determining, based on the comparison
of the first set of parameters with the clinically measured
parameters, that the digital model is unacceptable. For example,
determining that the digital anatomical model is unacceptable may
involve determining that the first set of computerized flow
parameters differs from the at least one clinically measured flow
parameter by at least a predetermined threshold amount. In some
embodiments, generating the first set of computerized flow
parameters may involve: discretizing the digital model; applying
boundary conditions to a portion of the digital model that contains
the heart valve and the inflow and outflow tracts; and initializing
and solving mathematical equations of blood flow through the
digital model.
[0024] In some embodiments, the method may further involve, after
the second comparing step: adjusting the adjusted digital
anatomical model, based on the comparison of the second set of
computerized flow parameters with the at least one clinically
measured flow parameter, to generate a new adjusted digital
anatomical model; modeling blood flow through the new adjusted
digital model to generate a third set of computerized flow
parameters; and comparing the third set of computerized flow
parameters with the at least one clinically measured flow
parameter. Some embodiments may involve repeating the adjusting,
modeling and comparing steps until a desired level of agreement is
reached between a most recently calculated set of computerized flow
parameters and the at least one clinically measured flow
parameter.
[0025] In another aspect, a system for generating an anatomical
model of a heart valve may include at least one computer system
configured to: receive patient-specific imaging data of the heart
valve and inflow and outflow tracts of the heart valve; generate a
first digital anatomical model of the heart valve and the inflow
and outflow tracts, based at least partially on the imaging data;
model blood flow through the first model to generate a first set of
computerized flow parameters; compare the first set of computerized
flow parameters with clinically measured flow parameters; adjust
the first digital anatomical model, based on the comparison of the
first set of computerized flow parameters with the clinically
measured flow parameters, to generate a second digital anatomical
model; model blood flow through the second model to generate a
second set of computerized flow parameters; and compare the second
set of computerized flow parameters with the clinically measured
flow parameters.
[0026] Optionally, the computer system may be further configured to
determine, before the adjusting step and based on the comparison of
the first set of parameters with the clinically measured
parameters, that the first digital anatomical model is
unacceptable. For example, to determine that the first digital
anatomical model is unacceptable, the at least one computer system
may be configured to determine that the first set of flow
parameters differs from the clinically measured flow parameters by
at least a predetermined threshold amount. In some embodiments, the
computer system may be further configured to repeat the
determining, adjusting, modeling and comparing steps until a
desired level of agreement is reached between a most recently
calculated set of computerized flow parameters and the at least one
clinically measured flow parameter.
[0027] In some embodiments, to generate the first set of
computerized flow parameters, the computer system may be configured
to: discretize the first model; apply boundary conditions to a
portion of the first model that contains the heart valve and the
inflow and outflow tracts; and initialize and solve mathematical
equations of blood flow through the first model.
[0028] These and other aspects and embodiments will be described in
further detail below, in reference to the attached drawing
figures.
BRIEF DESCRIPTION OF DRAWINGS
[0029] FIG. 1 is a flow diagram, outlining a method for modeling
and simulation, according to one embodiment;
[0030] FIG. 2 is a perspective view of a simplified geometric
model, based on patient-specific anatomic parameters, of the aortic
valve and the surrounding cardiac inflow and outflow vessels,
according to one embodiment;
[0031] FIG. 3 is a perspective view of a simplified geometric model
with the computational surface mesh, based on patient-specific
anatomic parameters, of the aortic valve and the surrounding
cardiac inflow and outflow vessels, according to one embodiment;
and
[0032] FIGS. 4A-4D are perspective views of representative
polyhedra used to discretize the interior volume of the geometric
model, according to one embodiment.
DETAILED DESCRIPTION
[0033] This disclosure describes computer modeling and simulation
systems and methods that qualitatively and quantitatively
characterize anatomic geometry of a heart valve and/or the
corresponding inflow/outflow tracts of the heart. The various
embodiments described herein may be applied to any single heart
valve, a combination of multiple heart valves, and/or combinations
of one or more heart valves and one or more coronary blood vessels.
Although occasional references may be made to one specific heart
valve, these specific references should not be interpreted as
limiting the scope of this disclosure. For example, the aortic
heart valve is occasionally used throughout this disclosure as a
specific example of a prototypical heart valve. Illustration of the
systems and methods via the example of the aortic heart valve,
however, is not intended to limit the scope of the computer
modeling and simulation systems and methods disclosed herein.
[0034] Referring to FIG. 1, one embodiment of a method for
implementing a modeling and simulation system is illustrated. A
first step of the method may involve importing or receiving
patient-specific geometric, anatomic, physiologic, and/or
hemodynamic data into the computer system 100. Typically, this
patient-specific data includes at least some imaging data and at
least one clinically measured flow parameter. In various
embodiments, the imaging data and the clinically measured flow
parameter(s) may be received by the system at the same time or at
different times during the process. The system may receive data
from any number and/or any type of patient-specific data collection
source or modality. In some embodiments, all the data received may
be data generated from non-invasive and/or minimally invasive
modalities. Examples of imaging modalities from which data may be
received include, but are not limited to, echocardiography,
ultrasound, magnetic resonance imaging (MM), x-ray, optical
tomography such as optical coherence tomography (OCT) and computed
tomography (CT). Examples of modalities which may be used for
generating the received clinically measured flow parameter(s)
include, but are not limited to, Doppler echocardiography,
catheterization procedures, functional magnetic resonance, routine
clinical tests (e.g., blood pressure, heart rate) and/or tests
otherwise prescribed by physicians to diagnose abnormal function of
the cardiac chambers or one or more heart valves.
[0035] The second step (FIG. 1) may involve constructing a
(possibly parameterized) geometric model, using the
imported/received data 200. A typical geometric model 10, as
illustrated in FIG. 2, may be a multi-dimensional digital
representation of the relevant patient anatomy, which may include
at least one heart valve 12, at least a portion of an inflow vessel
14 (or "inflow tract"), and at least a portion of an outflow vessel
16 (or "outflow tract") of the corresponding valve 12. The model
may also include one or more ventricles and/or atria of the heart
or a portion thereof. The geometric model is created from
patient-specific anatomical, geometric, physiologic, and/or
hemodynamic data. In some embodiments, the model may be created 200
using exclusively imaging data. Alternatively, the model may be
created 200 using imaging data and at least one clinically measured
flow parameter. Imaging data may be obtained from any suitable
diagnostic imaging exam(s), such as those listed above. Clinically
measured flow parameters may be obtained from any suitable test(s),
such as those listed above.
[0036] The model may also contain at least one inflow boundary and
at least one outflow boundary through which blood flows in and out
of the multi-dimensional model, respectively. These inflow and
outflow boundaries may denote finite truncation of the digital
model and may not be physically present in a patient. The digital
geometric model may be created using methods of applied mathematics
and image analysis, such as but not limited to image segmentation,
machine learning, computer aided design, parametric curve fitting,
and polynomial approximation. In various embodiments, a hybrid
approach, which combines a collection of geometric modeling
techniques, may also be used. The final, multi-dimensional model
provides a digital surrogate that captures the relevant physical
features of the anatomic topology under consideration and may
contain one or more morphological simplifications that exploit the
underlying geometric features of the patient-specific valvular and
vascular system under consideration. Such simplifications may, for
example, involve mathematical transformations (e.g., geometric
smoothing) or the exclusion of anatomic structures (e.g., chordae
tendineae of the mitral valve).
[0037] Referring again to FIG. 1, following the construction of the
digital model 200, the modeling and simulation system may
discretize the surface and volume of the model into a finite number
of partitions 300. These individual and non-overlapping partitions,
termed elements, facilitate the application and solution of the
physical laws of motion that govern blood flow through the
geometric model. The set of surface and volume elements used to
discretize the model, collectively referred to as the computational
mesh, transforms the continuous geometric model into a set of mesh
points and edges, where each element point in the computational
mesh has discrete x, y, and z spatial coordinates; each element
edge is bounded by two mesh points and has a finite length.
[0038] An illustration of a representative mesh 21 that discretizes
the surface of a geometric model 20 is shown in FIG. 3. FIG. 3 is a
perspective view of a geometric model 20, including an aortic valve
22, inflow tract 24 and outflow tract 26. This illustration of the
model 20 is used to show the mesh 21.
[0039] Referring to FIGS. 4A-4D, the shape of the surface elements
created by the modeling and simulation system may take the form of
any closed polygon, but the surface mesh typically contains a
collection of triangles, convex quadrilaterals or a combination
thereof. Volume elements are created by the modeling and simulation
system and are used to fill the interior of the model completely.
Each volume element may take the form of any closed polyhedron, but
the volume mesh (i.e., the set of volume elements) typically
contains a collection of tetrahedra, hexahedra, wedges or a
combination thereof (FIGS. 4A-4D). The surface and volume mesh
densities, which determine the spatial resolution of the discrete
model, may vary in space and time, as illustrated in FIG. 3. The
local densities of the surface and volume meshes may depend on the
complexity of the local topology of the underlying geometric model:
more complex local topology may require higher spatial resolution,
and therefore a higher mesh density, to resolve than local regions
of less complex topology (e.g., see FIG. 3 (right) near the aortic
valve 22).
[0040] The modeling and simulation system may use CFD to simulate
blood flow through the discretized geometric model. Blood may be
represented as a Newtonian or non-Newtonian fluid, and blood flow
may be represented physically by the conservation of mass,
momentum, and energy (or a combination thereof) and mathematically
by the fluid flow equations (e.g., continuity, Navier-Stokes
equations) with appropriate initial and boundary conditions; the
boundary conditions may be constant or a function of time and/or
space, and the boundary conditions may be different at different
inflow/outflow surfaces. Initial and boundary conditions may be
determined from empirical or heuristic relationships, clinical
data, mathematical formulas or a combination thereof, and the model
boundaries may be rigid or compliant or a combination thereof. The
mathematical equations and corresponding initial and boundary
conditions may be solved using conventional mathematical
techniques, which include analytical or special functions,
numerical methods (e.g., finite differences, finite volumes, finite
elements, spectral methods), methods of machine learning or a
hybrid approach that combines various aspects of the methods
listed.
[0041] As a next step in the modeling and simulation method, and
referring again to FIG. 1, the one or more boundary conditions may
be applied to a discrete patient model 400. The boundary flow
conditions may be obtained from patient-specific clinical measures
(e.g., Doppler echocardiography, MRI), in which case they may be
applied to the model in a manner that is consistent with clinical
observations and measurements. In addition, inflow and outflow
boundary conditions may be applied to compensate for underlying
psychological or medical conditions such as pain, anxiety, fear,
anemia, hyperthyroidism, left ventricular systolic dysfunction,
left ventricular hypertrophy, hypertension or arterial-venous
fistula, which may produce clinically misleading results, upon
which medical diagnoses and treatments may be based.
[0042] Referencing FIG. 1 and following the initialization of the
blood flow equations, the equations may be solved, and hemodynamic
quantities may be computed, by the modeling and simulation system
500. The blood flow equations may be solved in a steady-state or
time-dependent fashion; a hybrid approach that combines
steady-state and time-dependent methods may also be used. Next,
computed hemodynamic quantities may be compared with corresponding
quantities obtained from clinical measurements, tests, and/or
examinations (e.g., Doppler echocardiography, catheterization
procedures, functional magnetic resonance or phase contrast MRI)
600. If the computed and clinically measured hemodynamic quantities
are in satisfactory agreement 600a, then the results of the
modeling and simulation system may be analyzed and information or a
report may be delivered to a physician(s) or another medical
professional 700. If the computed and clinically measured
hemodynamic quantities are not in satisfactory agreement 600b, the
patient-specific model may be modified in a manner thought to
increase agreement between computed and clinical hemodynamic
quantities, and a new computation may be performed with the
modified model. Steps 300-600 may then be repeated until
satisfactory agreement between computed and clinical data is
obtained, and information or a report may be delivered to a
physician(s) or another medical professional 700.
[0043] As an illustrative example of the embodiments described in
600, 600a, and 600b of FIG. 1, the clinically measured (via Doppler
echocardiography, for example) peak velocity distal to the AV may
be compared with the corresponding numerical value computed (via
CFD) by the modeling and simulation system. If the computed and
clinical velocities are agreeable to within a specified accuracy
tolerance(s), then the geometric and hemodynamic models may be
deemed accurate 600a, and information or a report that details the
geometric and/or hemodynamic results may be delivered to a
physician(s) or another medical professional 700. If, however, the
computationally computed peak velocity and the clinically measured
peak velocity fail to meet the specified accuracy tolerance(s)
600b, then the geometric and/or hemodynamic model may be adjusted
and the flow may be recomputed via CFD. The new peak velocity
distal to the AV that is computed with the new geometric and/or
hemodynamic model may then be compared with the corresponding
clinical velocity per 600 of FIG. 1. This iterative process of
modifying the geometric and/or hemodynamic model, recomputing the
flow, and comparing the computed and clinical velocities may be
repeated until the computationally computed flow quantities and the
clinically measured flow quantities are in satisfactory
agreement.
[0044] After satisfactory agreement is achieved, the iterative
process may be terminated, and information or a report that details
the geometric and/or hemodynamic results may be delivered to a
physician(s) or medical professional, per 700. In this illustrative
example, the intent of adjusting the geometric and/or hemodynamic
model is to maximize agreement between the computationally computed
and clinically measured peak velocity distal to the AV, thereby
ensuring the construction of an accurate geometric and hemodynamic
model. In some embodiments, characterizing and understanding the
similarities and differences between the clinically measured and/or
derived results and the corresponding modeling and simulation
system results may be used to adjust modeling parameters and
maximize agreement between the clinically measured and/or derived
results and those results numerically computed by the modeling and
simulation system. These similarities and differences, as well as
additional geometric and/or hemodynamic information provided by the
modeling and simulation system, may also be used to guide clinical
diagnoses and decision-making.
[0045] Output of each CFD analysis may include qualitative and/or
quantitative geometric and hemodynamic information that may be
computed directly from the CFD analysis and/or through one or more
mechanisms of post-processing. These numerical results may be
analyzed to reveal patient-specific anatomic, geometric,
physiologic, and/or hemodynamic information that aid in the
construction of an accurate and inclusive model at a single time or
at a multitude of points in time. These qualitative and
quantitative data may also be used to guide clinical
decision-making and/or predictive information about disease state,
progression or risk stratification.
[0046] Output data from the modeling and simulation system may be
delivered to physicians or other medical professionals, who may use
the data for clinical decision-making 700. Delivery of
patient-specific information to medical professionals may occur via
verbal discussions, written correspondence, electronic media or a
combination thereof. These data may then be used by an individual
physician or by a team of physicians to develop a complete,
comprehensive, and accurate understanding of patient cardiac health
and determine whether or not medical treatment is warranted. If
medical treatment is warranted, then results from the modeling and
simulation system may be used to guide clinical decision-making.
Specific ways in which output from the modeling and simulation
system may be incorporated into the clinical management of cardiac
patients include, but are not limited to: (1) analysis of heart
valve operation, including, for example, diagnosing the severity,
functional significance, mechanism, and clinical response to
abnormal heart valve operation; (2) pre-surgical planning of heart
valve procedures, including, for example, patient-specific
selection, sizing, deployment mechanisms, and positioning of
prosthetic heart valves for surgical, minimally invasive,
transcatheter or valve-in-valve treatments; (3) post-surgical
assessment of heart valve procedures, including, for example,
regurgitation, gradients, velocities, pressures, placements or
efficacy; and (4) patient monitoring and/or follow-up. This list of
potential uses for the systems and methods described herein is for
example purposed only, and the list is not intended to be
exhaustive.
[0047] The modeling and simulation system provides a virtual
framework for conducting patient-specific sensitivity analyses.
Such analyses may assess the relative impacts of anatomic and/or
physiologic changes to the underlying anatomy and/or hemodynamic
state of a patient. These state changes may then be assessed for
functional and clinical significance, thereby estimating patient
response to therapy, disease progression, and/or patient-specific
risk stratification. Sensitivity analyses may be performed, for
example, by coupling the modeling and simulation system with Monte
Carlo and/or adjoint-based numerical methods that interact closely
with the modeling and simulation system described above (FIG. 1).
These numerical methods may be derivative-based or derivative-free
and may enable numerous anatomic, geometric, physiologic, and/or
hemodynamic scenarios to run in a virtual environment without
exposing patients to any medical risks. Results from the plethora
of simulations conducted during a sensitivity analysis may be
aggregated and presented to a medical professional to aid with
clinical decision-making. Results from sensitivity analyses may
also be used in conjunction with uncertainty analyses to assess
global and/or local uncertainties of anatomic, geometric,
physiologic, and/or hemodynamic results produced by the modeling
and simulation system.
Uncertainty analysis may also be used to assess the clinical impact
or significance of variability or unknown parameters associated
with device(s) that may be deployed during treatment (e.g.,
manufacturing tolerances).
[0048] The modeling and simulation system may enable planning of
heart valve replacement therapy and the selection of optimal valve
deployment. In particular, executing the modeling and simulation
system described herein may provide an accurate assessment of
anatomic, geometric, physiologic, and/or hemodynamic considerations
for valvular deployment and function, e.g., valve type, size,
mechanism, angle and/or the like. Hence, the modeling and
simulation systems and methods may provide a complete framework
that facilitates the accurate and complete anatomic and physiologic
assessment of heart valves and their corresponding inflow/outflow
tracts. This information may be used by medical professionals to
guide clinical decisions regarding patient treatment of heart valve
disease as to maximize the benefits to each patient.
[0049] Although the foregoing description is intended to be
complete, any of a number of acceptable additions, subtractions or
alterations to the described systems and methods may be made,
without departing from the scope of the invention. For example,
various method steps may be eliminated or performed in different
order. Therefore, this description is provided for exemplary
purposes, and should not be interpreted as limiting the scope of
the invention.
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