Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps

Abstract

We propose FiberNet, a method to estimate in-vivo the cardiac fiber architecture of the human atria from multiple catheter recordings of the electrical activation. Cardiac fibers play a central role in the electromechanical function of the heart, yet they are difficult to determine in-vivo, and hence rarely truly patient-specific in existing cardiac models. FiberNet learns the fibers arrangement by solving an inverse problem