Deep Learning for Model Correction in Cardiac Electrophysiological Imaging

Victoriya Kashtanova, Ibrahim Ayed, Andony Arrieula, Mark Potse, Patrick Gallinari, Maxime Sermesant
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, PMLR 172:665-675, 2022.

Abstract

Imaging the electrical activity of the heart can be achieved with invasive catheterisation. However, the resulting data are sparse and noisy. Mathematical modelling of cardiac electrophysiology can help the analysis but solving the associated mathematical systems can become unfeasible. It is often computationally demanding, for instance when solving for different patient conditions. We present a new framework to model the dynamics of cardiac electrophysiology at lower cost. It is based on the integration of a low-fidelity physical model and a learning component implemented here via neural networks. The latter acts as a complement to the physical part, and handles all quantities and dynamics that the simplified physical model neglects. We demonstrate that this framework allows us to reproduce the complex dynamics of the transmembrane potential and to correctly identify the relevant physical parameters, even when only partial measurements are available. This combined model-based and data-driven approach could improve cardiac electrophysiological imaging and provide predictive tools.

Cite this Paper

BibTeX
@InProceedings{pmlr-v172-kashtanova22a, title = {Deep Learning for Model Correction in Cardiac Electrophysiological Imaging}, author = {Kashtanova, Victoriya and Ayed, Ibrahim and Arrieula, Andony and Potse, Mark and Gallinari, Patrick and Sermesant, Maxime}, booktitle = {Proceedings of The 5th International Conference on Medical Imaging with Deep Learning}, pages = {665--675}, year = {2022}, editor = {Konukoglu, Ender and Menze, Bjoern and Venkataraman, Archana and Baumgartner, Christian and Dou, Qi and Albarqouni, Shadi}, volume = {172}, series = {Proceedings of Machine Learning Research}, month = {06--08 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v172/kashtanova22a/kashtanova22a.pdf}, url = {https://proceedings.mlr.press/v172/kashtanova22a.html}, abstract = {Imaging the electrical activity of the heart can be achieved with invasive catheterisation. However, the resulting data are sparse and noisy. Mathematical modelling of cardiac electrophysiology can help the analysis but solving the associated mathematical systems can become unfeasible. It is often computationally demanding, for instance when solving for different patient conditions. We present a new framework to model the dynamics of cardiac electrophysiology at lower cost. It is based on the integration of a low-fidelity physical model and a learning component implemented here via neural networks. The latter acts as a complement to the physical part, and handles all quantities and dynamics that the simplified physical model neglects. We demonstrate that this framework allows us to reproduce the complex dynamics of the transmembrane potential and to correctly identify the relevant physical parameters, even when only partial measurements are available. This combined model-based and data-driven approach could improve cardiac electrophysiological imaging and provide predictive tools.} }
Endnote
%0 Conference Paper %T Deep Learning for Model Correction in Cardiac Electrophysiological Imaging %A Victoriya Kashtanova %A Ibrahim Ayed %A Andony Arrieula %A Mark Potse %A Patrick Gallinari %A Maxime Sermesant %B Proceedings of The 5th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2022 %E Ender Konukoglu %E Bjoern Menze %E Archana Venkataraman %E Christian Baumgartner %E Qi Dou %E Shadi Albarqouni %F pmlr-v172-kashtanova22a %I PMLR %P 665--675 %U https://proceedings.mlr.press/v172/kashtanova22a.html %V 172 %X Imaging the electrical activity of the heart can be achieved with invasive catheterisation. However, the resulting data are sparse and noisy. Mathematical modelling of cardiac electrophysiology can help the analysis but solving the associated mathematical systems can become unfeasible. It is often computationally demanding, for instance when solving for different patient conditions. We present a new framework to model the dynamics of cardiac electrophysiology at lower cost. It is based on the integration of a low-fidelity physical model and a learning component implemented here via neural networks. The latter acts as a complement to the physical part, and handles all quantities and dynamics that the simplified physical model neglects. We demonstrate that this framework allows us to reproduce the complex dynamics of the transmembrane potential and to correctly identify the relevant physical parameters, even when only partial measurements are available. This combined model-based and data-driven approach could improve cardiac electrophysiological imaging and provide predictive tools.
APA
Kashtanova, V., Ayed, I., Arrieula, A., Potse, M., Gallinari, P. & Sermesant, M.. (2022). Deep Learning for Model Correction in Cardiac Electrophysiological Imaging. Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 172:665-675 Available from https://proceedings.mlr.press/v172/kashtanova22a.html.

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