ArticleIEEE transactions on medical imaging2019
Noninvasive Reconstruction of Transmural Transmembrane Potential With Simultaneous Estimation of Prior Model Error.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Volumetric non-invasive cardiac mapping for accessible global arrhythmia characterization.Communications medicine · 2026Article
- Hybrid Neural State-Space Modeling for Supervised and Unsupervised Electrocardiographic Imaging.IEEE transactions on medical imaging · 2024Article
- Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference.IEEE transactions on medical imaging · 2024Article
- Improving Generalization by Learning Geometry-Dependent and Physics-Based Reconstruction of Image Sequences.IEEE transactions on medical imaging · 2023Article
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4 authors.
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Abstract
To reconstruct electrical activity in the heart from body-surface electrocardiograms (ECGs) is an ill-posed inverse problem. Electrophysiological models have been found effective in regularizing these inverse problems by incorporating a priori knowledge about how the electrical potential in the heart propagates over time. However, these models suffer from model errors arising from, for example, parameters associated with tissue properties and the earliest sites of excitation. We present a Bayesian approach to simultaneously estimate transmembrane potential (TMP) signals and prior model errors, exploiting sparsity of the error in the gradient domain in the form of a novel sparse prior based on variational lower bound of the generalized Gaussian distribution. In synthetic and real-data experiments, we demonstrate the improvement of accuracy in TMP reconstruction brought by simultaneous model error estimation. We further provide theoretical and empirical justifications for the change of performances in the presented method at the presence of different model errors.
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