All-In-One Left Ventricular Vector Flow, Pressure, & Clotting Risk Mapping by Multi-Physics-Informed Neural Network

Color-Doppler echo is widely used to assess left ventricular (LV) flow. However, it is limited to the velocity component parallel to the ultrasound beam. Vector flow mapping (VFM) infers the cross-beam velocity but it is limited by relying only on mass conservation and offering little tolerance to gappy data. Moreover, VFM does not compute pressure or clotting risk; those involve secondary analyses complicating clinical translation.



We present MPI-VFM, a multi-physics-informed vector flow mapping method that applies deep learning to partial or complete Color-Doppler data. Its underlying models are continuity, Navier-Stokes, de-aliasing, and transport equations to infer clotting risk. We analyze MPI-VFM on CFD-generated ground-truth data vs. imaging parameters like spatial and temporal resolution, probe angle, aliasing, and Doppler sector size. We apply MPI-VFM to clinical Color-Doppler sequences and compare head-to-head with 4D flow MRI.