Fluoroscopic image-based 3-D environment reconstruction and automated path planning for a robotically steerable guidewire
Published in IEEE Robotics and Automation Letters, 2022
Abstract: 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 ex vivo porcine aorta, and navigation is demonstrated.
Recommended citation: S. R. Ravigopal, T. A. Brumfiel, A. Sarma and J. P. Desai, "Fluoroscopic Image-Based 3-D Environment Reconstruction and Automated Path Planning for a Robotically Steerable Guidewire," in IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 11918-11925, Oct. 2022, doi: 10.1109/LRA.2022.3207568.
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