IP Library Granted Patent US 10,716,513
Granted Patent B2
US 10,716,513 · App. 14/963,743 · Granted Jul 21, 2020

Systems and methods for cardiovascular blood flow and musculoskeletal modeling for predicting device failure or clinical events

Inventors: Gilwoo Choi (Mountain View, CA); Charles A. Taylor (Menlo Park, CA); Leo J. Grady (Millbrae, CA)
Assignee: HeartFlow, Inc.
A61B5/72A61B5/0022A61B5/026A61B5/1118A61B34/00A61B34/10G16H50/30G16H50/50A61B5/021A61B5/02007A61B5/055A61B2034/105A61B2562/0219
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Quick Facts
Patent No.
US 10,716,513
App. No.
14/963,743
Granted
Jul 21, 2020
Kind
B2
Abstract

Computer-implemented methods are disclosed for assessing the effect of musculoskeletal activities on disease and/or clinical events, the method comprising: receiving a patient-specific vascular and musculoskeletal model of a patient's anatomy, including at least one vessel of the patient; receiving at least one characteristic of the patient's musculoskeletal activity; generating or updating a computational anatomic vascular and musculoskeletal model of the patient's anatomy based on the received at least one characteristic of musculoskeletal activity; performing at least one of a computational fluid dynamics analysis or a structural mechanics simulation on the computational anatomic vascular and musculoskeletal model; and estimating at least one of the patient's risk of disease or clinical events based on the performed computational fluid dynamics analysis and/or structural mechanics simulation. Systems and computer readable media for executing these methods are also disclosed.

Claims (50)

1. A computer-implemented method of non-invasively assessing a risk of a clinical event in a patient, the method comprising:

obtaining, via an imaging device, one or more images of a patient's anatomy;

generating a first three-dimensional patient-specific vascular model of the patient's anatomy based on the one or more images, including at least one vessel of the patient;

measuring a duration and/or frequency of musculoskeletal behavior of the patient while the patient is at a given posture via a device associated with the patient;

generating an updated three-dimensional patient-specific vascular model of the patient's anatomy by simulating or detecting the given posture corresponding to the measured duration and/or frequency of musculoskeletal behavior of the patient;

computing mechanical and/or hemodynamic characteristics of the patient's vascular system at the given posture by performing at least one of a computational fluid dynamics analysis or a structural mechanics simulation using the updated three-dimensional patient-specific vascular model;

using the at least one of the computational fluid dynamics analysis or structural mechanics simulation by creating a feature vector of the musculoskeletal behavior of the patient while the patient is at the given posture to estimate and output an effect of the computed mechanical and/or hemodynamic characteristics on at least one of the patient's risk of disease, a risk of failure of a device, or a performance characteristic of a device;

assessing a risk of a clinical event in the patient at the given posture, using the estimated effect of the mechanical and/or hemodynamic characteristics of the vascular system of the patient on the at least one of the patient's risk of disease, the risk of failure of a device, or the performance characteristic of a device; and

outputting the risk of the clinical event to an electronic storage medium or a display for use by one or more healthcare providers.

2. The computer-implemented method of claim 1 , wherein generating the updated three-dimensional patient-specific vascular model of the patient's anatomy comprises generating a computational anatomic vascular model of the patient's anatomy.

3. The computer-implemented method of claim 2 , wherein the updated three-dimensional patient-specific vascular model of the patient's anatomy is generated using one or more of:

a computational structural mechanics simulation; or

images of the patient's venous system in various postures taken by a magnetic resonance imaging (MRI) system.

4. The computer-implemented method of claim 2 , wherein the musculoskeletal behavior of the patient includes one or more of sitting or lying down.

5. The computer-implemented method of claim 1 , wherein estimating the effect of the mechanical and/or hemodynamic characteristics of the vascular system of the patient on at least one of the patient's risk of disease, the risk of failure of a device, or the performance characteristic of a device includes executing one or more learned algorithms.

6. The computer-implemented method of claim 5 , wherein executing the one or more learned algorithms comprises a training phase and a prediction phase.

7. The computer-implemented method of claim 6 , wherein the training phase includes creating a feature vector of healthy patients and patients suffering from known clinical events or disease.

8. A computer system for non-invasively assessing a risk of a clinical event in a patient, the system comprising:

a data storage device storing instructions for the determining the effect of musculoskeletal activities in diagnosing or treating disease; and

a processor configured to execute the instructions to perform a method including:

obtaining, via an imaging device, one or more images of a patient's anatomy;

generating a first three-dimensional patient-specific vascular model of the patient's anatomy based on the one or more images, including at least one vessel of the patient;

measuring a duration and/or frequency of musculoskeletal behavior of the patient while the patient is at a given posture via a device associated with the patient;

generating an updated three-dimensional patient-specific vascular model of the patient's anatomy by simulating or detecting the given posture corresponding to the measured duration and/or frequency of musculoskeletal behavior of the patient;

computing mechanical and/or hemodynamic characteristics of the patient's vascular system at the given posture by performing at least one of a computational fluid dynamics analysis or a structural mechanics simulation using the updated three-dimensional patient-specific vascular model;

using the at least one of the computational fluid dynamics analysis or structural mechanics simulation by creating a feature vector of the musculoskeletal behavior of the patient while the patient is at the given posture to estimate and output an effect of the computed mechanical and/or hemodynamic characteristics on at least one of the patient's risk of disease, a risk of failure of a device, or a performance characteristic of a device;

assessing a risk of a clinical event in the patient at the given posture, using the estimated effect of the mechanical and/or hemodynamic characteristics of the vascular system of the patient on the at least one of the patient's risk of disease, the risk of failure of a device, or the performance characteristic of a device; and

outputting the risk of the clinical event to an electronic storage medium or a display for use by one or more healthcare providers.

9. The system of claim 8 , wherein the step of generating the updated three-dimensional vascular model of the patient's anatomy comprises generating a computational anatomic vascular model of the patient's anatomy.

10. The system of claim 9 , wherein the updated three-dimensional patient-specific vascular model of the patient's anatomy is generated using one or more of:

a computational structural mechanics simulation; or

images of the patient's venous system in various postures taken by a magnetic resonance imaging (MRI) system.

11. The system of claim 9 , wherein the musculoskeletal behavior of the patient includes one or more of sitting or lying down.

12. The system of claim 8 , wherein estimating the effect of the mechanical and/or hemodynamic characteristics of the vascular system of the patient on at least one of the patient's risk of disease, the risk of failure of a device, or the performance characteristic of a device includes executing one or more learned algorithms.

13. The system of claim 12 , wherein executing the one or more learned algorithms comprises a training phase and a prediction phase.

14. The system of claim 13 , wherein the training phase includes creating a feature vector of healthy patients and patients suffering from known clinical events or disease.

15. A non-transitory computer readable medium for use on a computer system containing computer-executable programming instructions for performing a method of non-invasively assessing a risk of a clinical event in a patient, the method comprising:

obtaining, via an imaging device, one or more images of a patient's anatomy;

generating a first three-dimensional patient-specific vascular model of the patient's anatomy based on the one or more images, including at least one vessel of the patient;

measuring a duration and/or frequency of musculoskeletal behavior of the patient while the patient is at a given posture via a device associated with the patient;

generating an updated three-dimensional patient-specific vascular model of the patient's anatomy by simulating or detecting the given posture corresponding to the measured duration and/or frequency of musculoskeletal behavior of the patient;

computing mechanical and/or hemodynamic characteristics of the patient's vascular system at the given posture by performing at least one of a computational fluid dynamics analysis or a structural mechanics simulation using the updated three-dimensional patient-specific vascular model;

using the at least one of the computational fluid dynamics analysis or structural mechanics simulation by creating a feature vector of the musculoskeletal behavior of the patient while the patient is at the given posture to estimate and output an effect of the computed mechanical and/or hemodynamic characteristics on at least one of the patient's risk of disease, a risk of failure of a device, or a performance characteristic of a device;

assessing a risk of a clinical event in the patient at the given posture, using the estimated effect of the mechanical and/or hemodynamic characteristics of the vascular system of the patient on the at least one of the patient's risk of disease, the risk of failure of a device, or the performance characteristic of a device; and

outputting the risk of the clinical event to an electronic storage medium or a display for use by one or more healthcare providers.

16. The non-transitory computer readable medium of claim 15 , wherein the step of generating the updated three-dimensional vascular model of the patient's anatomy comprises generating a computational anatomic vascular model of the patient's anatomy.

17. The non-transitory computer readable medium of claim 15 , wherein the updated three-dimensional patient-specific vascular model of the patient's anatomy is generated using one or more of:

a computational structural mechanics simulation; or

images of the patient's venous system in various postures taken by a magnetic resonance imaging (MRI) system.

18. The non-transitory computer readable medium of claim 15 , wherein the musculoskeletal behavior of the patient includes one or more of sitting or lying down.

Assignments (5)
RELEASE OF SECURITY INTEREST Recorded Sep 11, 2025
From: HAYFIN SERVICES LLP
To: HEARTFLOW, INC.
Reel/Frame 072876/0775 →
RELEASE OF SECURITY INTEREST Recorded Jun 21, 2024
From: HAYFIN SERVICES LLP
To: HEARTFLOW, INC.
Reel/Frame 067801/0032 →
SECURITY INTEREST Recorded Jun 18, 2024
From: HEARTFLOW, INC.
To: HAYFIN SERVICES LLP
Reel/Frame 067775/0966 →
SECURITY INTEREST Recorded Jan 20, 2021
From: HEARTFLOW, INC.
To: HAYFIN SERVICES LLP
Reel/Frame 055037/0890 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 30, 2015
From: CHOI, GILWOO; TAYLOR, CHARLES A.; GRADY, LEO J.
To: HEARTFLOW, INC.
Reel/Frame 037388/0051 →
Cited By (1)
US 12,670,998