Systems and methods for estimating ischemia and blood flow characteristics from vessel geometry and physiology

• US 10,010,255 B2
• Filed: 07/01/2016
• Issued: 07/03/2018
• 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
• Primary Examiner(s): Peng, Bo J
• Application Number:
  US15/200,318
• Publication Number:
  US 20160310019A1
• Time in Patent Office: 732 Days
• 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 fractional flow reserve (FFR) for a stenosis of interest for a patient, comprising:

receiving medical image data of the patient including the stenosis of interest;

extracting a set of anatomical and/or geometric features for the stenosis of interest from the medical image data of the patient;

receiving a set of training data comprising;

(1) measured FFR values and (2) anatomical and/or geometric features, of a plurality of individuals;

training a machine learning algorithm based on determined associations mapping the measured FFR values and the anatomical and/or geometric features, for each of the plurality of individuals;

determining a FFR value for the stenosis of interest based on the extracted set of anatomical and/or geometric features using the trained machine-learning algorithm mapping anatomical and/or geometric features to FFR values; and

generating and displaying a geometric model of at least the stenosis of interest of the patient, and indicating the determined FFR value for the stenosis of interest in the geometric model.

2. The method of claim 1, wherein the trained machine-learning based mapping is an empirical learned model that combines features from the set of anatomical and/or geometric features with respective learned weights.

3. The method of claim 1, wherein the trained machine-learning based mapping is a regression function.

4. The method of claim 1, wherein extracting a set of anatomical and/or geometric features for the stenosis of interest from the medical image data of the patient comprises:

extracting a plurality of features characterizing geometry of the stenosis of interest.

5. The method of claim 4, wherein the features characterizing the geometry of the stenosis of interest include proximal and distal reference diameters, minimal lumen diameter, lesion length.

6. The method of claim 5, wherein the features characterizing the geometry of the stenosis of interest further include lumen cross-sectional area features.

7. The method of claim 4, wherein extracting a set of anatomical and/or geometric features for the stenosis of interest from the medical image data of the patient further comprises:

extracting one or more features characterizing plaque shape.

8. The method of claim 4, wherein extracting a set of anatomical and/or geometric features for the stenosis of interest from the medical image data of the patient further comprises:

extracting one or more features characterizing geometry of a coronary artery branch in which the stenosis of interest is located.

9. The method of claim 4, wherein extracting a set of anatomical and/or geometric features for the stenosis of interest from the medical image data of the patient further comprises:

extracting one or more features characterizing geometry of an entire coronary artery tree of the patient.

10. The method of claim 4, wherein extracting a set of anatomical and/or geometric features for the stenosis of interest from the medical image data of the patient further comprises:

extracting one or more features characterizing coronary anatomy and function.

11. The method of claim 1, further comprising:

receiving functional measurements of the patient;

extracting one or more features from the functional measurements of the patient; and

including the one or more feature extracted from the functional measurements of the patient in the set of features used for determining the FFR value.

12. The method of claim 1, further comprising:

receiving demographic of the patient;

extracting one or more features from the demographic information of the patient; and

including the one or more feature extracted from the demographic information of the patient in the set of features used for determining the FFR value.

13. The method of claim 1, wherein the trained machine-learning based mapping is trained based on FFR values computed using a model to simulate blood flow in a set of training data.

14. The method of claim 1, wherein the trained machine-learning based mapping is trained based on anatomical and/or geometric features extracted from synthetically generated stenosis geometries and FFR values corresponding to the synthetically generated stenosis geometries computed using computational fluid dynamics (CFD) simulations performed on the synthetically generated stenosis geometries.

15. A system for determining fractional flow reserve (FFR) for a stenosis of interest for a patient, the system comprising:

a digital storage device storing instructions that, when executed by a processor, cause the computer system to perform a method for determining FFR for the stenosis of interest for the patient; and

a processor configured to execute the instructions to perform the method for determining FFR for the stenosis of interest for the patient, the method comprising;

receiving medical image data of the patient including the stenosis of interest;

extracting a set of anatomical and/or geometric features for the stenosis of interest from the medical image data of the patient;

receiving a set of training data comprising;

(1) measured FFR values and (2) anatomical and/or geometric features, of a plurality of individuals;

training a machine learning algorithm based on determined associations mapping the measured FFR values and the anatomical and/or geometric features, for each of the plurality of individuals;

determining a FFR value for the stenosis of interest based on the extracted set of anatomical and/or geometric features using the trained machine-learning algorithm mapping anatomical and/or geometric features to FFR values; and

generating and displaying a geometric model of at least the stenosis of interest of the patient, and indicating the determined FFR value for the stenosis of interest in the geometric model.

16. The system of claim 15, wherein extracting a set of features for the stenosis of interest from the medical image data of the patient further comprises:

extracting a plurality of features characterizing geometry of the stenosis of interest.

17. The system of claim 16, wherein extracting a set of features for the stenosis of interest from the medical image data of the patient further comprises:

extracting one or more features characterizing plaque shape.

18. The system of claim 16, wherein extracting a set of features for the stenosis of interest from the medical image data of the patient further comprises:

extracting one or more features characterizing geometry of a coronary artery branch in which the stenosis of interest is located.

19. A non-transitory computer readable medium storing computer program instructions for determining fractional flow reserve (FFR) for a stenosis of interest for a patient, the computer program instructions when executed on a processor cause the processor to perform operations comprising:

receiving medical image data of the patient including the stenosis of interest;

extracting a set of anatomical and/or geometric features for the stenosis of interest from the medical image data of the patient;

receiving a set of training data comprising;

(1) measured FFR values and (2) anatomical and/or geometric features, of a plurality of individuals;

training a machine learning algorithm based on determined associations mapping the measured FFR values and the anatomical and/or geometric features, for each of the plurality of individuals;

determining a FFR value for the stenosis of interest based on the extracted set of anatomical and/or geometric features using the trained machine-learning algorithm mapping anatomical and/or geometric features to FFR values; and

generating and displaying a geometric model of at least the stenosis of interest of the patient, and indicating the determined FFR value for the stenosis of interest in the geometric model.