Generation of patient specific cardiac chamber models using generative neural networks under a Bayesian framework for electroanatomical mapping

Electroanatomical mapping is a technique used in cardiology to create a detailed 3D map of the electrical activity in the heart. It is useful for diagnosis, treatment planning and real time guidance in cardiac ablation procedures to treat arrhythmias like atrial fibrillation. A probabilistic machine...

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Bibliographic Details
Published inarXiv.org
Main Authors Mathew, Sunil, Sra, Jasbir, Rowe, Daniel B
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 27.11.2023
Subjects
Ablation
Bayesian analysis
Cardiology
Computed tomography
Data acquisition
Image reconstruction
Machine learning
Magnetic resonance imaging
Mapping
Neural networks
Three dimensional models
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Summary:Electroanatomical mapping is a technique used in cardiology to create a detailed 3D map of the electrical activity in the heart. It is useful for diagnosis, treatment planning and real time guidance in cardiac ablation procedures to treat arrhythmias like atrial fibrillation. A probabilistic machine learning model trained on a library of CT/MRI scans of the heart can be used during electroanatomical mapping to generate a patient-specific 3D model of the chamber being mapped. The use of probabilistic machine learning models under a Bayesian framework provides a way to quantify uncertainty in results and provide a natural framework of interpretability of the model. Here we introduce a Bayesian approach to surface reconstruction of cardiac chamber models from a sparse 3D point cloud data acquired during electroanatomical mapping. We show how probabilistic graphical models trained on segmented CT/MRI data can be used to generate cardiac chamber models from few acquired locations thereby reducing procedure time and x-ray exposure. We show how they provide insight into what the neural network learns from the segmented CT/MRI images used to train the network, which provides explainability to the resulting cardiac chamber models generated by the model.
ISSN:2331-8422