Systems and methods for processing electronic images to determine flow using flow ratio
Embodiments include a system for determining patient cardiovascular information which includes at least one computer system configured to receive patient-specific data regarding a geometry of an anatomical structure of a patient; create a model representing at least a portion of the anatomical structure of the patient based on the patient-specific data; determine a first blood flow rate at at least one point of interest in the model by using relations of individual-specific anatomic data to functional estimates of blood flow characteristics generated from a plurality of individuals; modify the model; determine a second blood flow rate at a point in the modified model corresponding to the at least one point of interest by using the relations of individual-specific anatomic data to functional estimates of blood flow characteristics; and determine a fractional flow reserve value as a ratio of the second blood flow rate to the first blood flow rate.
1 . A method for processing electronic images to determine blood flow information, comprising:
segmenting computed tomography (CT) imaging data of a patient in a diseased condition to identify a list of voxels belonging to at least a portion of anatomy of the patient that includes a diseased vessel, the list of voxels defining, for each voxel, a spatial location of the voxel and an identification of any neighboring voxels;
generating, based on the list of voxels, a patient-specific anatomical model representative of the patient in the diseased condition, the patient-specific anatomical model including a representation of the diseased vessel;
generating a second patient-specific anatomical model of the patient in a hypothetical healthy condition, the second patient-specific anatomical model including a hypothetical healthy condition version of the diseased vessel, wherein generating the second patient-specific anatomical model includes modifying a geometry of the representation of the diseased vessel to simulate removal of a narrowing or obstruction associated with the diseased vessel;
generating a first blood flow for the patient using the patient-specific anatomical model and generating a second blood flow for the patient using the second patient-specific anatomical model wherein the generating includes, in each case:
determining feature vectors for a plurality of locations within the anatomy of a respective model;
estimating a Fractional Flow Reserve (FFR) value for each feature vector at a corresponding location within the respective model, and appending the estimated FFR value to the feature vector; and
applying a trained machine-learning model to the appended feature vectors for the respective model, the trained machine-learning model trained based on training feature vectors and training blood flow data from one or more individuals to output the first and second blood flow values, respectively; and
calculating a hemodynamic index using the first blood flow and the second blood flow.
2 . The method of claim 1 , wherein the hemodynamic index is based on a ratio of the first blood flow to the second blood flow.
3 . The method of claim 1 , further comprising:
determining a functional significance of the diseased vessel based on the hemodynamic index.
4 . The method of claim 1 , wherein generating the first and second blood flows for the patient each include simulating a state of hyperemia for the patient.
5 . The method of claim 1 , wherein each feature vector includes a numerical description of geometry of the anatomy at the corresponding location.
6 . The method of claim 5 , wherein estimating the FFR value includes using a further trained machine-learning model that has been trained based on training geometry and training FFR values to output the estimated FFR value.
7 . The method of claim 5 , further comprising, for each of the plurality of locations, extracting one or more geometric features of the anatomy from the respective model, wherein the numerical description of geometry includes a numerical description of the one or more geometric features.
8 . A system for processing images to determine blood flow information, comprising:
at least one memory storing instructions and computed tomography (CT) imaging data of a patient in a diseased condition; and
at least one processor operatively connected to the at least one memory and configured to perform operations, including:
segmenting the CT imaging data to identify a list of voxels belonging to at least a portion of anatomy of the patient that includes a diseased vessel, the list of voxels defining, for each voxel, a spatial location of the voxel and an identification of any neighboring voxels;
generating, based on the list of voxels, a patient-specific anatomical model representative of the patient in the diseased condition, the patient-specific anatomical model including a representation of the diseased vessel;
generating a second patient-specific anatomical model of the patient in a hypothetical healthy condition, the second patient-specific anatomical model including a hypothetical healthy condition version of the diseased vessel, wherein generating the second patient-specific anatomical model includes modifying a geometry of the representation of the diseased vessel to simulate removal of a narrowing or obstruction associated with the diseased vessel;
generating a first blood flow for the patient using the patient-specific anatomical model and generating a second blood flow for the patient using the second patient-specific anatomical model wherein the generating includes, in each case:
determining feature vectors for a plurality of locations within the anatomy of a respective model;
estimating a Fractional Flow Reserve (FFR) value for each feature vector at a corresponding location within the respective model, and appending the estimated FFR value to the feature vector; and
applying a trained machine-learning model to the appended feature vectors for the respective model, the trained machine-learning model trained based on training feature vectors and training blood flow data from one or more individuals to output the first and second blood flow values, respectively; and
calculating a hemodynamic index using the first blood flow and the second blood flow.
9 . The system of claim 8 , wherein the hemodynamic index is based on a ratio of the first blood flow second blood flow.
10 . The system of claim 8 , wherein the operations further include:
determining a functional significance of the diseased vessel based on the hemodynamic index.
11 . The system of claim 8 , wherein generating each blood flow for the patient includes simulating a state of hyperemia for the patient.
12 . The system of claim 8 , wherein each feature vector includes a numerical description of geometry of the anatomy at the corresponding location.
13 . The system of claim 12 , wherein estimating the FFR value includes using a further trained machine-learning model that has been trained based on training geometry and training FFR values to output the estimated FFR value.
14 . The system of claim 12 , wherein:
the operations further include, for each of the plurality of locations, extracting one or more geometric features of the anatomy from the respective model; and
the numerical description of geometry includes a numerical description of the one or more geometric features.
15 . A non-transitory computer-readable medium comprising instructions that are executable by at least one processor to perform operations, including:
segmenting computed tomography (CT) imaging data of a patient in a diseased condition to identify a list of voxels belonging to at least a portion of anatomy of the patient that includes a diseased vessel, the list of voxels defining, for each voxel, a spatial location of the voxel and an identification of any neighboring voxels;
generating, based on the list of voxels, a patient-specific anatomical model representative of the patient in the diseased condition, the patient-specific anatomical model including a representations of the diseased vessel;
generating a second patient-specific anatomical model of the patient in a hypothetical healthy condition, the second patient-specific anatomical model including a hypothetical healthy condition version of the diseased vessel, wherein generating the second patient-specific anatomical model includes modifying a geometry of the representation of the diseased vessel to simulate removal of a narrowing or obstruction associated with the diseased vessel;
generating a first blood flow for the patient using the patient-specific anatomical model and generating a second blood flow for the patient using the second patient-specific anatomical model wherein the generating includes, in each case:
determining feature vectors for a plurality of locations within the anatomy of a respective model;
estimating a Fractional Flow Reserve (FFR) value for each feature vector at a corresponding location within the respective model, and appending the estimated FFR value to the feature vector; and
applying a trained machine-learning model to the appended feature vectors for the respective model, the trained machine-learning model trained based on training feature vectors and training blood flow data from one or more individuals to output the first and second blood flow values, respectively; and
calculating a hemodynamic index using the first blood flow and the second blood flow.
16 . The non-transitory computer-readable medium of claim 15 , wherein each feature vector includes a numerical description of geometry of the anatomy at the corresponding location.
17 . The non-transitory computer-readable medium of claim 16 , wherein estimating the FFR value includes using a further trained machine-learning model that has been trained based on training geometry and training FFR values to output the estimated FFR value.
18 . The non-transitory computer-readable medium of claim 16 , wherein:
the operations further include, for each of the plurality of locations, extracting one or more geometric features of the anatomy from the respective model; and
the numerical description of geometry includes a numerical description of the one or more geometric features.