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

摘要

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 letter, 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex vivo</i> porcine aorta, and navigation is demonstrated through the use of a tendon-actuated <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CO</u> axially <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> ligned <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ST</u> eerable guidewire (COAST).