Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks

• URL: http://arxiv.org/abs/2102.10863v1
• Date: Mon, 22 Feb 2021 09:55:17 GMT
• Title: Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks
• Authors: Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal, Paris Perdikaris, Thomas Pock, Gernot Plank, Rolf Krause
• Abstract summary: We employ a recently developed approach, called physics informed neural networks, to learn the fiber orientations from electroanatomical maps. In particular, we train the neural network to weakly satisfy the anisotropic eikonal equation and to predict the measured activation times. The methodology is tested both in a synthetic example and for patient data.
• Score: 7.70592139052601
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electroanatomical maps are a key tool in the diagnosis and treatment of atrial fibrillation. Current approaches focus on the activation times recorded. However, more information can be extracted from the available data. The fibers in cardiac tissue conduct the electrical wave faster, and their direction could be inferred from activation times. In this work, we employ a recently developed approach, called physics informed neural networks, to learn the fiber orientations from electroanatomical maps, taking into account the physics of the electrical wave propagation. In particular, we train the neural network to weakly satisfy the anisotropic eikonal equation and to predict the measured activation times. We use a local basis for the anisotropic conductivity tensor, which encodes the fiber orientation. The methodology is tested both in a synthetic example and for patient data. Our approach shows good agreement in both cases and it outperforms a state of the art method in the patient data. The results show a first step towards learning the fiber orientations from electroanatomical maps with physics-informed neural networks.

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