Systems and methods for assessing cardiovascular disease and treatment effectiveness from adipose tissue

Patent metadata

Field	Value
Publication number	US-11817219-B2
Application number	US-202217930090-A
Country	US
Kind code	B2
Filing date	Sep 7, 2022
Priority date	Oct 17, 2018
Publication date	Nov 14, 2023
Grant date	Nov 14, 2023

Abstract

Systems and methods are disclosed for assessing cardiovascular disease and treatment effectiveness based on adipose tissue. One method includes identifying a vascular bed of interest in a patient's vasculature; receiving a medical image of the patient's identified vascular bed of interest; identifying adipose tissue in the received medical image; receiving a geometric vascular model comprising a representation of the patient's identified vascular bed of interest; and computing an inflammation index associated with the geometric vascular model, using the identified adipose tissue.

First claim

What is claimed is:

1. A computer-implemented method for determining a blood-flow metric based on adipose tissue, comprising:
determining a patient-specific model of blood flow through anatomy of a patient, the patient-specific model of blood flow including boundary conditions at inflow and outflow boundaries of the anatomy;
determining a patient-specific inflammation index of the anatomy of the patient, the patient-specific inflammation index being based on adipose tissue identified within a predetermined distance of the anatomy, wherein determining the patient-specific inflammation index includes:
obtaining patient-specific medical imaging of the anatomy;
identifying adipose tissue in the patient-specific medical imaging;
determining a quantification of the identified adipose tissue that is weighted based on distance between the identified adipose tissue and a region of interest; and
determining the patient-specific inflammation index based on the quantification;

modifying at least one of the boundary conditions of the patient-specific model of blood flow based on the patient-specific inflammation index; and
determining at least one patient-specific blood-flow metric using the patient-specific model of blood flow with the at least one modified boundary condition.

2. The computer-implemented method of claim 1 , wherein the patient-specific model of blood flow is based on a patient-specific geometric model of the anatomy.

3. The computer-implemented method of claim 2 , wherein the patient-specific geometric model of the anatomy is a parameterized model that is parameterized by vessel centerline and vessel radius.

4. The computer-implemented method of claim 1 , wherein at least one inflow boundary of the patient-specific model of blood flow is coupled to one or more of a heart model or a lumped parameter model.

5. The computer-implemented method of claim 1 , wherein at least one outflow boundary of the patient-specific model of blood flow is coupled to one or more of one-dimensional wave propagation model or a lumped parameter model.

6. The computer-implemented method of claim 1 , wherein modifying the at least one boundary condition includes one or more of:
decreasing a boundary condition of microvascular resistance based on the patient-specific inflammation index to represent dilation; or
increasing the boundary condition of microvascular resistance based on the patient-specific inflammation index to represent a narrowing, blockage, or stenosis.

7. The computer-implemented method of claim 1 , wherein the at least one patient-specific blood-flow metric includes fractional flow reserve.

8. The computer-implemented method of claim 1 , further comprising:
generating a patient-specific blood-flow metric threshold based on the patient-specific inflammation index.

9. The computer-implemented method of claim 1 , wherein:
determining the patient-specific inflammation index includes determining a plurality of patient-specific inflammation indexes for a plurality of different locations in the anatomy; and
determining at least one patient-specific blood-flow metric includes determining the at least one patient-specific blood-flow metric at the plurality of different locations.

10. A system for determining a blood-flow metric based on adipose tissue, comprising:
at least one memory including instructions; and
at least one processor operatively connected to the at least one memory, and configured to execute the instructions to perform operations, including:
determining a patient-specific model of blood flow through anatomy of a patient, the patient-specific model of blood flow including boundary conditions at inflow and outflow boundaries of the anatomy;
determining a patient-specific inflammation index of the anatomy of the patient, the patient-specific inflammation index being based on adipose tissue identified within a predetermined distance of the anatomy, wherein determining the patient-specific inflammation index includes:
obtaining patient-specific medical imaging of the anatomy;
identifying adipose tissue in the patient-specific medical imaging;
determining a quantification of the identified adipose tissue that is weighted based on distance between the identified adipose tissue and a region of interest; and
determining the patient-specific inflammation index based on the quantification;

modifying at least one of the boundary conditions of the patient-specific model of blood flow based on the patient-specific inflammation index; and
determining at least one patient-specific blood-flow metric using the patient-specific model of blood flow with the at least one modified boundary condition.

11. The system of claim 10 , wherein:
the patient-specific model of blood flow is based on a patient-specific geometric model of the anatomy; and
the patient-specific geometric model of the anatomy is a parameterized model that is parameterized by vessel centerline and vessel radius.

12. The system of claim 10 , wherein:
at least one inflow boundary of the patient-specific model of blood flow is coupled to one or more of a heart model or a lumped parameter; and
at least one outflow boundary of the patient-specific model of blood flow is coupled to one or more of one-dimensional wave propagation model or a lumped parameter model.

13. The system of claim 10 , wherein modifying the at least one boundary condition includes one or more of:
decreasing a boundary condition of microvascular resistance based on the patient-specific inflammation index to represent dilation; or
increasing the boundary condition of microvascular resistance based on the patient-specific inflammation index to represent a narrowing, blockage, or stenosis.

14. The system of claim 10 , wherein the at least one patient-specific blood-flow metric includes fractional flow reserve.

15. The system of claim 10 , further comprising:
generating a patient-specific blood-flow metric threshold based on the patient-specific inflammation index.

16. The system of claim 10 , wherein:
determining the patient-specific inflammation index includes determining a plurality of patient-specific inflammation indexes for a plurality of different locations in the anatomy; and
determining at least one patient-specific blood-flow metric includes determining the at least one patient-specific blood-flow metric at the plurality of different locations.

17. A non-transitory computer readable medium comprising instructions that are executable by one or more processors to perform operations, including:
determining a patient-specific model of blood flow through anatomy of a patient, the patient-specific model of blood flow including boundary conditions at inflow and outflow boundaries of the anatomy;
determining a patient-specific inflammation index of the anatomy of the patient, the patient-specific inflammation index being based on adipose tissue identified within a predetermined distance of the anatomy, wherein determining the patient-specific inflammation index includes:
obtaining patient-specific medical imaging of the anatomy;
identifying adipose tissue in the patient-specific medical imaging;
determining a quantification of the identified adipose tissue that is weighted based on distance between the identified adipose tissue and a region of interest; and
determining the patient-specific inflammation index based on the quantification;

modifying at least one of the boundary conditions of the patient-specific model of blood flow based on the patient-specific inflammation index; and
determining at least

Assignees

• Heartflow Inc

Inventors

• Rabbat Mark
• Taylor Charles
• Schaap Michiel
• Fonte Timothy
• Grady Leo

Classifications

• G16H50/30 Primary

  for calculating health indices; for individual health risk assessment · CPC title

• A61B5/02007

  Evaluating blood vessel condition, e.g. elasticity, compliance · CPC title

• A61B5/4848

  Monitoring or testing the effects of treatment, e.g. of medication · CPC title

• A61B5/7278

  Artificial waveform generation or derivation, e.g. synthesizing signals from measured signals · CPC title

• G16H30/20

  for handling medical images, e.g. DICOM, HL7 or PACS · CPC title