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Electroanatomical mapping is a technique used in cardiology to create adetailed 3D map of the electrical activity in the heart. It is useful fordiagnosis, treatment planning and real time guidance in cardiac ablationprocedures to treat arrhythmias like atrial fibrillation. A probabilisticmachine learning model trained on a library of CT/MRI scans of the heart can beused during electroanatomical mapping to generate a patient-specific 3D modelof the chamber being mapped. The use of probabilistic machine learning modelsunder a Bayesian framework provides a way to quantify uncertainty in resultsand provide a natural framework of interpretability of the model. Here weintroduce a Bayesian approach to surface reconstruction of cardiac chambermodels from a sparse 3D point cloud data acquired during electroanatomicalmapping. We show how probabilistic graphical models trained on segmented CT/MRIdata can be used to generate cardiac chamber models from few acquired locationsthereby reducing procedure time and x-ray exposure. We show how they provideinsight into what the neural network learns from the segmented CT/MRI imagesused to train the network, which provides explainability to the resultingcardiac chamber models generated by the model.

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