Fluoroscopic Image-Based 3-D Environment Reconstruction and Automated Path Planning for a Robotically Steerable Guidewire.

Cardiovascular diseases are the leading cause of death globally and surgical treatments for these often begin with the manual placement of a long compliant wire, called a guidewire, through different vasculature. To improve procedure outcomes and reduce radiation exposure, we propose steps towards a fully automated approach for steerable guidewire navigation within vessels. In this paper, we utilize fluoroscopic images to fully reconstruct 3-D printed phantom vasculature models by using a shape-from-silhouette algorithm. The reconstruction is subsequently de-noised using a deep learning-based encoder-decoder network and morphological filtering. This volume is used to model the environment for guidewire traversal. Following this, we present a novel method to plan an optimal path for guidewire traversal in three-dimensional vascular models through the use of slice planes and a modified hybrid A-star algorithm. Finally, the developed reconstruction and planning approaches are applied to an ex vivo porcine aorta, and navigation is demonstrated through the use of a tendon-actuated COaxially Aligned STeerable guidewire (COAST).