TY - GEN
T1 - Learning Atrial Fiber Orientations and Conductivity Tensors from Intracardiac Maps Using Physics-Informed Neural Networks
AU - Grandits, Thomas
AU - Pezzuto, Simone
AU - Costabal, Francisco Sahli
AU - Perdikaris, Paris
AU - Pock, Thomas
AU - Plank, Gernot
AU - Krause, Rolf
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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, with an RMSE of 2.2 ms on the in-silico data and outperforming a state of the art method on the patient data. The results show a first step towards learning the fiber orientations from electroanatomical maps with physics-informed neural networks.
AB - 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, with an RMSE of 2.2 ms on the in-silico data and outperforming a state of the art method on the patient data. The results show a first step towards learning the fiber orientations from electroanatomical maps with physics-informed neural networks.
UR - http://www.scopus.com/inward/record.url?scp=85111807691&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-78710-3_62
DO - 10.1007/978-3-030-78710-3_62
M3 - Conference contribution
AN - SCOPUS:85111807691
SN - 9783030787097
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 650
EP - 658
BT - Functional Imaging and Modeling of the Heart - 11th International Conference, FIMH 2021, Proceedings
A2 - Ennis, Daniel B.
A2 - Perotti, Luigi E.
A2 - Wang, Vicky Y.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021
Y2 - 21 June 2021 through 25 June 2021
ER -