Non-invasive assessment and therapy guidance for coronary artery disease in diffuse and tandem lesions

An apparatus for non-invasive assessment and therapy planning for coronary artery disease from medical image data of a patient, comprising:

• means for extracting geometric features from medical image data representing at least a portion of a coronary artery tree of the patient;
• means for detecting one or more lesions in the coronary artery tree of the patient and computing a hemodynamic quantity of interest at a plurality of points along the coronary artery tree including multiple points within each of the one or more lesions based on the extracted geometric features using a first machine learning model, resulting in an estimated pullback curve for the hemodynamic quantity of interest;
• means for predicting post-treatment values for the hemodynamic quantity of interest at the plurality of points along the coronary artery tree including the multiple points within each of the one or more lesions for each of one or more candidate treatment options for the patient, resulting in a respective predicted post-treatment pullback curve for the hemodynamic quantity of interest for each of the one or more candidate treatment options;
• and
• means for displaying a visualization of a treatment prediction for at least one of the candidate treatment options for the patient.

A non-transitory computer readable medium storing computer program instructions for non-invasive assessment and therapy planning for coronary artery disease from medical image data of a patient, the computer program instructions when executed by a processor cause the processor to perform operations comprising:

• extracting geometric features from medical image data representing at least a portion of a coronary artery tree of the patient;
• detecting one or more lesions in the coronary artery tree of the patient and computing a hemodynamic quantity of interest at a plurality of points along the coronary artery tree including multiple points within each of the one or more lesions based on the extracted geometric features using a first machine learning model, resulting in an estimated pullback curve for the hemodynamic quantity of interest;
• predicting post-treatment values for the hemodynamic quantity of interest at the plurality of points along the coronary artery tree including the multiple points within each of the one or more lesions for each of one or more candidate treatment options for the patient, resulting in a respective predicted post-treatment pullback curve for the hemodynamic quantity of interest for each of the one or more candidate treatment options;
• and
• displaying a visualization of a treatment prediction for at least one of the candidate treatment options for the patient.