Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping

arXiv:2403.13040v1 Announce Type: cross
Abstract: Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging. In this study, we propose novel alternatives to the traditional iVFM optimization scheme by utilizing physics-informed neural networks (PINNs) and a physics-guided nnU-Net-based supervised approach. Through rigorous evaluation on simulated color Doppler images derived from a patient-specific computational fluid dynamics model and in vivo Doppler acquisitions, both approaches demonstrate comparable reconstruction performance to the original iVFM algorithm. The efficiency of …

abstract arxiv color cs.ai cs.cv cs.lg eess.iv evaluation flow imaging mapping networks neural networks novel optimization physics physics-informed study through type vector