Modeling and reconstructing the shape of a heart chamber from partial or noisy data is useful in many (minimally) invasive heart procedures. We propose a method to reconstruct the shape of the left atria during the electrophysiology procedure from a series of simple catheter maneuvers. We use left atria shapes generated from a statistical based physical model and approximate traversal locations of catheter maneuvers inside the left atria. These paths mimic realistic ones doable in a lab phantom. We demonstrate the ability of a deep neural network to approximate the atria shape solely based on the given paths. We compare the results against training from partial data generated by the intersection of a randomly generated sphere and the atria. We test the presented network on actual lab phantoms and show promising results.
CITATION STYLE
Baram, A., Safran, M., Ben-Cohen, A., & Greenspan, H. (2018). Left atria reconstruction from a series of sparse catheter paths using neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11074 LNCS, pp. 138–146). Springer Verlag. https://doi.org/10.1007/978-3-030-00129-2_16
Mendeley helps you to discover research relevant for your work.