Method and system for sensitivity analysis in modeling blood flow characteristics

Patent metadata

Field	Value
Publication number	US-9424395-B2
Application number	US-201313864996-A
Country	US
Kind code	B2
Filing date	Apr 17, 2013
Priority date	Mar 4, 2013
Publication date	Aug 23, 2016
Grant date	Aug 23, 2016

Abstract

Methods for determining cardiovascular information for a patient include receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of the patient's vasculature based on the patient-specific data; and creating a computational model of a blood flow characteristic based on the anatomic model. The method also includes identifying one or more of an uncertain parameter, an uncertain clinical variable, and an uncertain geometry; modifying a probability model based on one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry; determining a blood flow characteristic within the patient's vasculature based on the anatomic model and the computational model; and calculating, based on the probability model and the determined blood flow characteristic, a sensitivity of the determined fractional flow reserve to one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry.

First claim

What is claimed is:

1. A system for determining cardiovascular information for a patient, the system comprising:
at least one computer system configured to:
receive patient-specific data regarding a geometry of at least the patient's vasculature;
create an anatomic model representing at least a portion of the patient's vasculature based on the patient-specific data;
create a computational model of a blood flow characteristic based on the anatomic model;
identify, using the computational model, a region within the patient's vasculature meeting a threshold value of the blood flow characteristic;
identify one or more of an uncertain parameter, an uncertain clinical variable, and an uncertain geometry;
modify a probability model based on one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry;
determine the blood flow characteristic for the identified region within the patient's vasculature based on the anatomic model and the computational model of the blood flow characteristic of the patient's vasculature; and
calculate, based on the probability model and the determined blood flow characteristic for the identified region within the patient's vasculature, a sensitivity of the blood flow characteristic to one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry.

2. The system of claim 1 , wherein the computational model of the blood flow characteristic is a reduced order model or a one dimensional model.

3. The system of claim 1 , wherein the anatomic model is a three-dimensional model, a four-dimensional model, or a one-dimensional wave propagation model.

4. The system of claim 1 , wherein the probability model is initialized based on a stochastic collocation method.

5. The system of claim 4 , wherein the sensitivity is calculated based on the Smolyak quadrature method.

6. The system of claim 1 , wherein:
the uncertain clinical variable is one or more of a myocardial mass, hematocrit, viscosity, pressure, body or surface area, heart-rate, systolic and diastolic brachial blood pressures, patient height, patient weight, patient history, smoking status, and presence/absence of diabetes; and
the uncertain geometry is one or more of maximal stenosis diameter, stenosis length, stenosis location, lesion size, lesion length, or healthy segments with image-based artifacts.

7. The system of claim 1 , wherein the probability model is initialized by constructing a coarse stochastic space for one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry.

8. The system of claim 1 , wherein the anatomic model representing at least the portion of the patient's vasculature includes at least a portion of an aorta and at least a portion of a plurality of coronary arteries emanating from the portion of the aorta.

9. The system of claim 1 , wherein:
blood flow characteristic is a fractional flow reserve value;
the fractional flow reserve indicates a ratio between a pressure in the aorta and a pressure at a location in the vasculature; and
the at least one computer system is configured to determine the fractional flow reserve at a plurality of locations in the vasculature.

10. The system of claim 1 , wherein the patient-specific data includes data obtained from computer tomography or magnetic resonance imaging techniques.

11. The system of claim 1 , wherein the at least one computer system is configured to create the anatomic model based on the imaging data by locating boundaries of lumens of the patient's vasculature using the imaging data.

12. The system of claim 1 , wherein the model of the blood flow characteristic includes at least one lumped parameter model representing a blood flow through boundaries of the anatomic model.

13. The system of claim 1 , wherein the at least one computer system is configured to determine the blood flow characteristic using a parameter associated with at least one of a level of hyperemia, a level of exercise, or a medication.

14. The system of claim 13 , wherein the at least one computer system is configured to determine the blood flow characteristic using a parameter associated with the level of hyperemia, and the parameter relates to a coronary artery resistance of the patient, an aortic blood pressure of the patient, or a heart rate of the patient.

15. The system of claim 1 , wherein the at least one computer system is configured to display an indicator of the calculated sensitivity in relation to the determined blood flow characteristic.

16. The system of claim 15 , wherein the blood flow characteristic is an FFR value and the indicator of the calculated sensitivity is a confidence interval.

17. A method for determining cardiovascular information for a patient, using at least one computer system, the method comprising:
receiving patient-specific data regarding a geometry of the patient's vasculature;
creating an anatomic model representing at least a portion of the patient's vasculature based on the patient-specific data;
creating a computational model of a blood flow characteristic based on the anatomic model;
identifying, using the computational model, a region within the patient's vasculature meeting a threshold value of the blood flow characteristic;
identifying one or more of an uncertain parameter, an uncertain clinical variable, and an uncertain geometry;
modifying a probability model based on one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry;
determining the blood flow characteristic for the identified region within the patient's vasculature based on the anatomic model and the computational model of the blood flow characteristic of the patient's vasculature; and
calculating, based on the probability model and the determined blood flow characteristic for the identified region within the patient's vasculature, a sensitivity of the blood flow characteristic to one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry.

18. The method of claim 17 , wherein the computational model of the blood flow characteristic is a reduced order model or a one dimensional model.

19. The method of claim 17 , wherein the anatomic model is a three-dimensional model, a four-dimensional model, or a one-dimensional wave propagation model.

20. The method of claim 17 , wherein the probability model is initialized based on a stochastic collocation method.

21. The method of claim 20 , wherein the sensitivity is calculated based on the Smolyak quadrature method.

22. The method of claim 17 , wherein:
the uncertain clinical variable is one or more of a myocardial mass, hematocrit, viscosity, pressure, body or surface area, heart-rate, systolic and diastolic brachial blood pressures, patient height, patient weight, patient history, smoking status, and presence/absence of diabetes; and
the uncertain geometry is one or more of maximal stenosis diameter, stenosis location, stenosis length, lesion size, lesion length, or healthy segments with image-based artifacts.

23. The method of claim 20 , wherein the probability model is initialized by constructing a coarse stochastic space for one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry.

24. The method of claim 17 , further comprising determining a location of a functionally significant narrowing in a vess

Assignees

• Sankaran Sethuraman
• Grady Leo
• Taylor Charles A
• Heartflow Inc

Inventors

• Sankaran Sethuraman
• Grady Leo
• Taylor Charles A

Classifications

• G06T2207/10081

  Computed x-ray tomography [CT] · CPC title

• A61B5/055

  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging · CPC title

• G06T2207/30104

  Vascular flow; Blood flow; Perfusion · CPC title

• A61B6/504

  for diagnosis of blood vessels, e.g. by angiography · CPC title

• A61B5/026

  Measuring blood flow {(A61B3/1233, A61B3/1241 take precedence)} · CPC title