ArticleComputing in cardiology2020
Improving Localization of Cardiac Geometry Using ECGI.
Article in Computing in cardiology, 2020. 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.
- Improving Generalization by Learning Geometry-Dependent and Physics-Based Reconstruction of Image Sequences.IEEE transactions on medical imaging · 2023Article
- Reconstruction of cardiac position using body surface potentials.Computers in biology and medicine · 2022Article
- Body Surface Potential Mapping: Contemporary Applications and Future Perspectives.Hearts (Basel, Switzerland) · 2021Article
- Simultaneous Multi-Heartbeat ECGI Solution with a Time-Varying Forward Model: a Joint Inverse Formulation.Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH (Conference) · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
introductionElectrocardiographic imaging (ECGI) requires a model of the torso, and inaccuracy in the position of the heart is a known source of error. We previously presented a method to localize the heart when body and heart surface potentials are known. The goal of this study is to extend this approach to only use body surface potentials.
methodsWe used an iterative coordinate descent optimization to estimate the positions of the heart for several consecutive heartbeats relying on the assumption that the epicardial potential sequence is the same in each beat. The method was tested with data synthesized using measurements from a isolated-heart, torso-tank preparation. Improvement was evaluated in terms of both heart localization and ECGI accuracy.
resultsThe geometric correction resulted in cardiac geometries closely matching ground truth geometry. ECGI accuracy increased dramatically by all metrics using the corrected geometry. DISCUSSION: Future studies will employ more realistic animal models and then human subjects. Success could impact clinical ECGI by reducing errors from respiratory movement and perhaps decrease imaging requirements, reducing both cost and logistical difficulty of ECGI, widening clinical applicability.
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Registered trials
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