Systems and methods for processing electronic images to evaluate medical images

A computer-implemented method for personalized non-invasive assessment of anatomy of a patient, comprising:

• obtaining a segmentation of at least a portion of the anatomy of the patient, the segmentation identifying voxels in imaging data of the anatomy of the patient that correspond to a lumen within the anatomy of the patient;
• generating a patient-specific geometric model based on the voxels that correspond to the lumen, the patient-specific geometric model representable as a list of points in space;
• obtaining one or more boundary condition for the lumen;
• extracting a centerline of the lumen using the patient-specific geometric model;
• computing a cross-section parameter of the lumen at each of a plurality of points along the centerline, and mapping the cross-section parameter of each point along the centerline to corresponding points of the patient-specific geometric model;
• generating, for each point in the patient-specific geometric model, a feature vector that includes the cross-section parameter corresponding to that point;
• and
• generating, for each point in the patient-specific model, an estimation of fractional flow reserve (FFR) by applying a trained model or algorithm to the feature vectors and the one or more boundary condition of the lumen.

A system for personalized non-invasive assessment of anatomy of a patient, comprising:

• at least one memory storing instructions;
• and
• at least one processor operatively connected to the at least one memory and configured to execute the instructions to perform operations, including:
• obtaining a patient-specific geometric model of a structure of interest within the anatomy of the patient, the patient-specific geometric model representable as a list of points in space;
• extracting a centerline of the structure of interest using the patient-specific geometric model;
• computing a cross-section parameter of the structure of interest at each of a plurality of points along the centerline, and mapping the cross-section parameter of each point along the centerline to corresponding points of the patient-specific geometric model;
• generating, for each of a plurality of points in the patient-specific geometric model, a feature vector that includes the cross-section parameter corresponding to that point;
• and
• generating, for each of the plurality of points, an estimation of fractional flow reserve (FFR) by applying a trained model or algorithm to the feature vectors.