SYSTEMS AND METHODS FOR ESTIMATING ISCHEMIA AND BLOOD FLOW CHARACTERISTICS FROM VESSEL GEOMETRY AND PHYSIOLOGY

• US 20140073976A1
• Filed: 05/16/2013
• Published: 03/13/2014
• Est. Priority Date: 09/12/2012
• Status: Active Grant

• Current Assignee: Heartflow Inc.
• Original Assignee: Heartflow Inc.
• Inventors: FONTE, Timothy, CHOI, Gilwoo, GRADY, Leo, SINGER, Michael
• Granted Patent: US 10,433,740 B2
• Time in Patent Office: Days
• US Class Current: 600/504
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 5/107 Measuring physical dimensions, e.g. size of the entire body or parts thereof
• CPC Class Codes: A61B 5/14535 for measuring haematocrit
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/463 characterised by displaying multiple images or images and diagnostic data on one display
• CPC Class Codes: A61B 6/504 for diagnosis of blood vessels, e.g. by angiography
• CPC Class Codes: A61B 6/507 for determination of haemodynamic parameters, e.g. perfusion CT
• CPC Class Codes: A61B 6/5217 extracting a diagnostic or physiological parameter from medical diagnostic data
• CPC Class Codes: A61B 6/563 : involving image data transmission via a network
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Systems and methods are disclosed for determining individual-specific blood flow characteristics. One method includes acquiring, for each of a plurality of individuals, individual-specific anatomic data and blood flow characteristics of at least part of the individual'"'"'s vascular system; executing a machine learning algorithm on the individual-specific anatomic data and blood flow characteristics for each of the plurality of individuals; relating, based on the executed machine learning algorithm, each individual'"'"'s individual-specific anatomic data to functional estimates of blood flow characteristics; acquiring, for an individual and individual-specific anatomic data of at least part of the individual'"'"'s vascular system; and for at least one point in the individual'"'"'s individual-specific anatomic data, determining a blood flow characteristic of the individual, using relations from the step of relating individual-specific anatomic data to functional estimates of blood flow characteristics.

Claims

1. A method for determining individual-specific blood flow characteristics, the method comprising:

acquiring, for each of a plurality of individuals, individual-specific anatomic data and blood flow characteristics of at least part of the individual'"'"'s vascular system;

executing a machine learning algorithm on the individual-specific anatomic data and blood flow characteristics for each of the plurality of individuals;

relating, based on the executed machine learning algorithm, each individual'"'"'s individual-specific anatomic data to functional estimates of blood flow characteristics;

acquiring, for an individual, individual-specific anatomic data of at least part of the individual'"'"'s vascular system; and

for at least one point in the individual'"'"'s individual-specific anatomic data, determining a blood flow characteristic of the individual, using relations from the step of relating individual-specific anatomic data to functional estimates of blood flow characteristics.

2. The method of claim 1, further comprising:

acquiring, for each of the plurality of individuals, one or more individual characteristics; and

executing the machine learning algorithm further based on the one or more individual characteristics.

3. The method of claim 1, wherein the blood flow characteristics of the individuals include ischemia, blood flow, or fractional flow reserve.

4. The method of claim 1, further comprising:

generating a set of features for each point of interest where a blood flow characteristic is desired;

using a regression or machine learning technique to weight the impact of features on the blood flow characteristic; and

using the regression or machine learning technique to estimate a blood flow characteristic numerically, classify a vessel as ischemia positive or negative, or classify an individual as ischemia positive or negative.

5. The method of claim 2, wherein the individual characteristics include one or more of:

heart rate, blood pressure, demographics such as age or sex, medication, disease states, including diabetes, hypertension, vessel dominance, and prior MI.

6. The method of claim 1, further comprising:

displaying or storing the produced estimates in one or more of a media, including images, renderings, tables of values, or reports, or transferring the produced estimates to a physician through other electronic or physical delivery methods.

7. The method of claim 1, further comprising displaying along with each produced estimate a confidence level or a positive, negative, or inconclusive indication.

8. The method of claim 1, further comprising producing estimates based on one or more of analytical fluid dynamics equations and morphometry scaling laws.

9. The method of claim 1, wherein the individual-specific anatomic data includes one or more of:

vessel size, vessel size at ostium, vessel size at distal branches, reference and minimum vessel size at plaque, distance from ostium to plaque, length of plaque and length of minimum vessel size, myocardial volume, branches proximal/distal to measurement location, branches proximal/distal to plaque, and measurement location.

10. The method of claim 2, further comprising:

compiling a library or database of anatomic and individual characteristics along with FFR, ischemia test results, previous simulation results, and imaging data.

11. The method of claim 10, further comprising:

refining the machine learning algorithm based on additional data added to the library or database.

12. The method of claim 10, wherein the individual-specific anatomic data for the individual or the plurality of individuals is obtained from one or more of:

medical image data, measurements, models, and segmentations.

13. A system for determining individual-specific blood flow characteristics, the system comprising:

a data storage device storing instructions for estimating individual-specific blood flow characteristics; and

a processor configured to execute the instructions to perform a method including the steps of;

acquiring, for each of a plurality of individuals, individual-specific anatomic data and blood flow characteristics of at least part of the individual'"'"'s vascular system;

executing a machine learning algorithm on the individual-specific anatomic data and blood flow characteristics for each of the plurality of individuals;

relating, based on the executed machine learning algorithm, each individual'"'"'s individual-specific anatomic data to functional estimates of blood flow characteristics;

acquiring, for an individual, individual-specific anatomic data of at least part of the individual'"'"'s vascular system; and

for at least one point in the individual'"'"'s individual-specific anatomic data, determining a blood flow characteristic of the individual, using relations from the step of relating individual-specific anatomic data to functional estimates of blood flow characteristics.

14. The system of claim 13, wherein the system is further configured for:

acquiring, for each of the plurality of individuals, one or more individual characteristics; and

executing the machine learning algorithm further based on the one or more individual characteristics.

15. The system of claim 13, wherein the blood flow characteristics include ischemia, blood flow, or fractional flow reserve.

16. The system of claim 13, wherein the processor is further configured for:

generating a set of features for each point of interest where a blood flow characteristic is desired;

using a regression or machine learning technique to weight the impact of features on estimated blood flow characteristic; and

using the regression or machine learning technique to estimate a blood flow characteristic numerically, classify a vessel as ischemia positive or negative, or classify an individual as ischemia positive or negative.

17. The system of claim 14, wherein the individual characteristics include one or more of:

heart rate, blood pressure, demographics such as age or sex, medication, disease states, including diabetes, hypertension, vessel dominance, and prior Ml.

18. The system of claim 13, wherein the processor is further configured for:

displaying or storing the produced estimates in one or more of a media, including images, renderings, tables of values, or reports, or transferring the produced estimates to a physician through other electronic or physical delivery methods.

19. The system of claim 13, wherein the processor is further configured for:

displaying along with each produced estimate a confidence level or a positive, negative, or inconclusive indication.

20. The system of claim 13, further comprising producing estimates based on one or more of analytical fluid dynamics equations and morphometry scaling laws.

21. The system of claim 13, wherein the individual-specific anatomic data includes one or more of:

vessel size, vessel size at ostium, vessel size at distal branches, reference and minimum vessel size at plaque, distance from ostium to plaque, length of plaque and length of minimum vessel size, myocardial volume, branches proximal/distal to measurement location, branches proximal/distal to plaque, and measurement location.

22. The system of claim 14, wherein the processor is further configured for:

compiling a library or database of anatomic and individual characteristics along with FFR, ischemia test results, previous simulation results, and imaging data.

23. The system of claim 13, wherein the individual-specific anatomic data for the individual or the plurality of individuals is obtained from one or more of:

medical image data, measurements, models, and segmentations.

24. A non-transitory computer-readable medium storing instructions that, when executed by a computer, cause the computer to perform a method including:

acquiring, for each of a plurality of individuals, individual-specific anatomic data and blood flow characteristics of at least part of the individual'"'"'s vascular system;

executing a machine learning algorithm on the individual-specific anatomic data and blood flow characteristics for each of the plurality of individuals;

relating, based on the executed machine learning algorithm, each individual'"'"'s individual-specific anatomic data to functional estimates of blood flow characteristics;

acquiring, for an individual, individual-specific anatomic data of at least part of the individual'"'"'s vascular system; and

for at least one point in the individual'"'"'s individual-specific anatomic data, determining a blood flow characteristic of the individual, using relations from the step of relating individual-specific anatomic data to functional estimates of blood flow characteristics.

25. The computer-readable medium of claim 24, wherein the method further comprises;

acquiring, for each of the plurality of individuals, one or more individual characteristics; and

executing the machine learning algorithm further based on the one or more individual characteristics.

26. The computer-readable medium of claim 25, wherein executing the machine learning algorithm comprises:

for a plurality of points in the individual-specific anatomic data for each of the plurality of individuals, creating a feature vector of the individual characteristics; and

associating the feature vector for each of the plurality of individuals with the blood flow characteristics of the corresponding individual.

27. The computer-readable medium of claim 24, wherein executing the machine learning algorithm further comprises:

training the machine learning algorithm to predict blood flow characteristics at various points based on the associated feature vectors.

28. The computer-readable medium of claim 24, wherein each feature vector includes one or more of:

systolic and diastolic blood pressures, heart rate, blood properties, individual age, individual gender, individual height, individual weight, presence or absence of disease, lifestyle characteristics, characteristics of aortic geometry, and characteristics of the coronary branch geometry.

29. The computer-readable medium of claim 24, wherein each feature vector includes one or more of:

a cross-sectional area feature set, an intensity feature set, a surface feature set, a volume feature set, a centerline feature set, and a simplified physics feature set.

30. The computer-readable medium of claim 25, wherein the individual characteristics include one or more of heart rate, hematocrit level, blood pressure, blood viscosity, individual age, individual gender, individual weight, individual lifestyle characteristic, and a mass of supplied tissue.

31. The computer-readable medium of claim 24, wherein the machine learning algorithm includes one or more of:

a support vector machine (SVM), a multi-layer perceptron (MLP), a multivariate regression (MVR), and a weighted linear or logistic regression.