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

TL;DR

FiberNet is a physics-informed neural network method that estimates patient-specific cardiac fiber architecture from multiple electroanatomical maps, enabling personalized heart models and improved understanding of cardiac function.

Contribution

The paper introduces FiberNet, a novel neural network approach that infers in-vivo cardiac fiber orientation from sparse activation data, addressing a key challenge in personalized cardiac modeling.

Findings

Three maps suffice to accurately identify fibers even with noise.

Fewer maps require regularization to maintain accuracy.

The model can predict unseen activation maps reliably.

Abstract

We propose FiberNet, a method to estimate \emph{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 electro-mechanical 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 fiber arrangement by solving an inverse problem with physics-informed neural networks. The inverse problem amounts to identifying the conduction velocity tensor of a cardiac propagation model from a set of sparse activation maps. The use of multiple maps enables the simultaneous identification of all the components of the conduction velocity tensor, including the local fiber angle. We extensively test FiberNet on synthetic 2-D and 3-D examples, diffusion tensor fibers, and a patient-specific case. We show that 3 maps are sufficient to accurately capture the fibers, also in the presence of noise. With fewer maps, the role of regularization becomes prominent. Moreover, we show that the fitted model can robustly reproduce unseen activation maps. We envision that FiberNet will help the creation of patient-specific models for personalized medicine. The full code is available at http://github.com/fsahli/FiberNet.