Neural Network Reconstruction of the Left Atrium using Sparse Catheter Paths
This addresses a domain-specific problem for clinicians performing catheter-based ablation by enabling faster and easier visualization, though it is incremental as it builds on existing encoder-decoder networks.
The paper tackled the problem of reconstructing the left atrium shape from sparse catheter paths to reduce procedure time in atrial fibrillation ablation, achieving realistic visualization within 3 minutes compared to over 10 minutes for dense sampling.
Catheter based radiofrequency ablation for pulmonary vein isolation has become the first line of treatment for atrial fibrillation in recent years. This requires a rather accurate map of the left atrial sub-endocardial surface including the ostia of the pulmonary veins, which requires dense sampling of the surface and takes more than 10 minutes. The focus of this work is to provide left atrial visualization early in the procedure to ease procedure complexity and enable further workflows, such as using catheters that have difficulty sampling the surface. We propose a dense encoder-decoder network with a novel regularization term to reconstruct the shape of the left atrium from partial data which is derived from simple catheter maneuvers. To train the network, we acquire a large dataset of 3D atria shapes and generate corresponding catheter trajectories. Once trained, we show that the suggested network can sufficiently approximate the atrium shape based on a given trajectory. We compare several network solutions for the 3D atrium reconstruction. We demonstrate that the solution proposed produces realistic visualization using partial acquisition within a 3-minute time interval. Synthetic and human clinical cases are shown.