Aortic and Pulmonary 3D Blood Flow Dynamics Derived from Contrast Enhanced MRA by Fluid-Physics Informed Deep-Learning

Motivation: 4D Flow MRI enables comprehensive hemodynamic assessments, but its clinical usage is hindered by long scan times. Contrast-enhanced MRA provides only anatomical information. Goal(s): To develop a series of fluid-physics informed deep-learning (FPI-DL) networks to derive information on time-resolved aortic and pulmonary artery 3D blood flow dynamics directly from cardiothoracic CEMRA. Approach: FPI-DL networks were trained with peak systolic velocities+normalized average velocity time-curve as an input and time-resolved 3D velocities as output. The FPI-DL networks were tested on CEMRA data and compared to pair 4D flow MRI as ground-truth. Results: AI-derived flow showed strong-to-excellent agreement to 4D flow MRI ground-truth across all comparisons. Impact: CEMRA is a widely available, standard-of-care test. As such, this technique enables wider access to complex hemodynamic information in the aorta and pulmonary arteries that generally requires 4D flow MRI, providing better overall patient management and assessments from CEMRA.