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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Left Atria Reconstruction from a Series of Sparse Catheter Paths Using Neural Networks
verfasst von : Alon Baram, Moshe Safran, Avi Ben-Cohen, Hayit Greenspan
Erschienen in: Machine Learning for Medical Image Reconstruction
Abstract
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.