Finite element-based space-time total variation-type regularization of the inverse problem in electrocardiographic imaging
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Abstract (arXiv)
Reconstructing cardiac electrical activity from body surface electric potential measurements results in the severely ill-posed inverse problem in electrocardiography. Many different regularization approaches have been proposed to improve numerical results and provide unique results. This work presents a novel approach for reconstructing the epicardial potential from body surface potential maps based on a space-time total variation-type regularization using finite elements, where a first-order primal-dual algorithm solves the underlying convex optimization problem. In several numerical experiments, the superior performance of this method and the benefit of space-time regularization for the reconstruction of epicardial potential on two-dimensional torso data and a three-dimensional rabbit heart compared to state-of-the-art methods are demonstrated.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 57 references that are in the map. First posted on arXiv 21 Aug 2024.
Builds on
5 of 57 references.
References · 5
- 2021Convex optimizationcited by 2,515
- 2022Learning cardiac activation maps from 12-lead ECG with multi-fidelity Bayesian optimization on manifoldscited by 6
- 2023Digital twinning of cardiac electrophysiology models from the surface ECG: a geodesic backpropagation approachcited by 5
- 2020Space-time shape uncertainties in the forward and inverse problem of electrocardiographycited by 4
- 2024Reconstruction of the local contractility of the cardiac muscle from deficient apparent kinematicscited by 2
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Citing papers per year: 2026: 1.
1 paper.