Neural Network Reconstruction of the Left Atrium using Sparse Catheter
  Paths
Alon Baram Moshe Safran Tomer Noy Naveh Geri Hayit Greenspan
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.