A probabilistic framework for freehand 3D ultrasound reconstruction applied to catheter ablation guidance in the left atrium.

Introduction: The catheter ablation procedure is a minimally invasive surgery used to treat atrial fibrillation. Difficulty visualizing the catheter inside the left atrium anatomy has led to lengthy procedure times and limited success rates. In this paper, we present a set of algorithms for reconstructing 3D ultrasound data of the left atrium in real-time, with an emphasis on automatic tissue classification for improved clarity surrounding regions of interest.

Methods: Using an intracardiac echo (ICE) ultrasound catheter, we collect 2D-ICE images of a left atrium phantom from multiple configurations and iteratively compound the acquired data into a 3D-ICE volume. We introduce two new methods for compounding overlapping US data-occupancy-likelihood and response-grid compounding-which automatically classify voxels as "occupied" or "clear," and mitigate reconstruction artifacts caused by signal dropout. Finally, we use the results of an ICE-to-CT registration algorithm to devise a response-likelihood weighting scheme, which assigns weights to US signals based on the likelihood that they correspond to tissue-reflections.

Results: Our algorithms successfully reconstruct a 3D-ICE volume of the left atrium with voxels classified as "occupied" or "clear," even within difficult-to-image regions like the pulmonary vein openings. We are robust to dropout artifact that plagues a subset of the 2D-ICE images, and our weighting scheme assists in filtering out spurious data attributed to ghost-signals from multi-path reflections. By automatically classifying tissue, our algorithm precludes the need for thresholding, a process that is difficult to automate without subjective input. Our hope is to use this result towards developing 3D ultrasound segmentation algorithms in the future.