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We propose FiberNet, a method to estimate \emph{in-vivo} the cardiac fiberarchitecture of the human atria from multiple catheter recordings of theelectrical activation. Cardiac fibers play a central role in theelectro-mechanical function of the heart, yet they are difficult to determinein-vivo, and hence rarely truly patient-specific in existing cardiac models.FiberNet learns the fiber arrangement by solving an inverse problem withphysics-informed neural networks. The inverse problem amounts to identifyingthe conduction velocity tensor of a cardiac propagation model from a set ofsparse activation maps. The use of multiple maps enables the simultaneousidentification of all the components of the conduction velocity tensor,including the local fiber angle. We extensively test FiberNet on synthetic 2-Dand 3-D examples, diffusion tensor fibers, and a patient-specific case. We showthat 3 maps are sufficient to accurately capture the fibers, also in thepresence of noise. With fewer maps, the role of regularization becomesprominent. Moreover, we show that the fitted model can robustly reproduceunseen activation maps. We envision that FiberNet will help the creation ofpatient-specific models for personalized medicine. The full code is availableat http://github.com/fsahli/FiberNet.

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