Systems and methods for processing electronic images to determine flow using flow ratio

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.

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.