The uncertainty, sensitivity, and/or standard deviation for a patient-specific hemodynamic quantification is determined (26). The contribution of different information, such as the fit of the geometry at different locations, to the uncertainty or sensitivity is determined. Alternatively or additionally, the amount of contribution of information at one location (e.g., geometric fit at the one location) to uncertainty or sensitivity at other locations is determined. Rather than relying on time consuming statistical analysis for each patient, a machine-learnt classifier is trained (46) to determine the uncertainty, sensitivity, and/or standard deviation for the patient.
MACHINE-LEARNT PREDICTION OF UNCERTAINTY OR SENSITIVITY FOR HEMODYNAMIC QUANTIFICATION IN MEDICAL IMAGING