SYSTEMS AND METHODS OF PROCESSING IMAGES OF EPICARDIAL AND PERICORONARY FAT

1 . A computer-implemented method for processing medical images, the method comprising:

receiving image data for a patient;

based on the received image data, determining:

a patient-specific epicardial adipose tissue (EAT) metric or a patient-specific pericoronary adipose tissue (PCAT) metric; and

at least one other patient-specific metric; and

using the EAT metric or the PCAT metric, and the at least one other patient-specific metric, to determine a risk score for the patient or to classify a disease state of the patient.

2 . The computer-implemented method of claim 1 , wherein the at least one other patient-specific metric includes a vessel geometry, a vessel morphology, a plaque characteristic, or a hemodynamic measurement.

3 . The computer-implemented method of claim 1 , wherein the image data is a first image data, the EAT metric is a first EAT metric, the PCAT metric is a first PCAT metric, and the at least one other patient-specific metric is at least a first other patient-specific metric, and wherein, before determining the risk score or classifying the disease state of the patient, the computer-implemented method further comprises:

receiving second image data for the patient;

based on the received second image data, determining:

a second patient-specific EAT metric or a second patient-specific PCAT metric; and

at least a second other patient-specific metric; and

using the second EAT metric or the second PCAT metric, and the second other patient-specific metric to determine the risk score for the patient or to classify the disease state of the patient.

4 . The computer-implemented method of claim 1 , wherein the received image data is used to generate a three-dimensional model of a vasculature of the patient.

5 . The computer-implemented method of claim 4 , further comprising generating a display image of the three-dimensional model, wherein the display image includes a color-coded indicator of the risk score or the disease state.

6 . The computer-implemented method of claim 1 , wherein the disease state is Ischemia with Non-Obstructive Coronary Arteries (INOCA).

7 . The computer-implemented method of claim 1 , wherein the risk score is predictive of a fractional flow reserve (FFR) value.

8 . The computer-implemented method of claim 1 , wherein both the EAT metric and the PCAT metric are used to determine the risk score for the patient or to classify the disease state of the patient.

9 . A system for processing medical images of a patient, comprising:

a data storage device storing instructions for medical image processing; and

a processor configured to execute the instructions to perform operations comprising:

receiving medical images of the patient;

based on the received medical images, determining:

a patient-specific epicardial adipose tissue (EAT) metric or a patient-specific pericoronary adipose tissue (PCAT) metric; and

at least one other patient-specific metric; and

using the EAT metric or the PCAT metric, and the at least one other patient-specific metric, to determine a risk score for the patient or to classify a disease state of the patient.

10 . The system of claim 9 , wherein the at least one other patient-specific metric includes a vessel geometry, a vessel morphology, a plaque characteristic, or a hemodynamic measurement.

11 . The system of claim 9 , wherein the received image data is used to generate a three-dimensional model of a vasculature of the patient.

12 . The system of claim 11 , wherein the system is further configured to generate a display image of the three-dimensional model, and wherein the display image includes a color-coded indicator of the risk score or the disease state.

13 . The system of claim 9 , wherein the disease state is Ischemia with Non-Obstructive Coronary Arteries (INOCA).

14 . The system of claim 9 , wherein the risk score is predictive of a fractional flow reserve (FFR) value.

15 . The system of claim 9 , wherein both the EAT metric and the PCAT metric are used to determine the risk score for the patient or to classify the disease state of the patient.

16 . The system of claim 9 , wherein the image data are a first image data, the EAT metric is a first EAT metric, the PCAT metric is a first PCAT metric, and the at least one other patient-specific metric is at least a first other patient-specific metric, and wherein, before determining the risk score or classifying the disease state of the patient, the processor is further configured to execute the instructions to perform operations comprising:

receiving second image data for the patient;

based on the received second image data, determining:

a second patient-specific EAT metric, or

a second patient-specific PCAT metric; and

at least a second other patient-specific metric; and

using the EAT metric or the second PCAT metric, and the second other patient-specific metric, to determine the risk score for the patient or to classify the disease state of the patient.

17 . A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a computer-implemented method for medical image processing, the method comprising:

receiving image data for a patient;

based on the received image data, determining:

a patient-specific epicardial adipose tissue (EAT) metric or a patient-specific pericoronary adipose tissue (PCAT) metric; and

at least one other patient-specific metric; and

using the EAT metric or the PCAT metric, and the at least one other patient-specific metric, to determine a risk score for the patient or to classify a disease state of the patient.

18 . The non-transitory computer-readable medium of claim 17 , wherein the at least one other patient-specific metric includes a vessel geometry, a vessel morphology, a plaque characteristic, or a hemodynamic measurement.

19 . The non-transitory computer-readable medium of claim 17 , wherein the received image data is used to generate a three-dimensional model of a vasculature of the patient.

20 . The non-transitory computer-readable medium of claim 19 , wherein the computer-implemented further involves generating a display image of the three-dimensional model, and wherein the display image includes a color-coded indicator of the risk score or the disease state.