1Robarts Research Institute (Canada) 2Western Univ. (Canada) 3Western Univ. (Canada) 4London Health Sciences Ctr. (Canada) 5Lawson Health Research Institute (Canada)
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Vascular navigation is an essential component of transcatheter cardiovascular interventions, conventionally performed using either 2D fluoroscopic imaging or CT- derived vascular roadmaps which can lead to many complications for the patients as well as the clinicians. This study presents an open-source and user-friendly 3D Slicer module that performs vessel reconstruction from tracked intracardiac ultrasound (ICE) imaging using deep learning-based methods. We also validate the methods by performing a vessel-phantom study. The results indicate that our Slicer module is able to reconstruct vessels with sufficient accuracy with an average distance error of 0.86 mm. Future work involves improving the speed of the methods as well as testing the module in an in-vivo setting. Clinical adaptation of this platform will allow the clinicians to navigate the vessels in 3D and will potentially enhance their spatial awareness as well as improve procedural safety.
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Hareem Nisar, Patrick Carnahan, Daniel Bainbridge, Elvis C. S. Chen, Terry M. Peters, "An open-source 3D Slicer module for fluoro-free transcatheter vessel navigation," Proc. SPIE 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, 1246616 (3 April 2023); https://doi.org/10.1117/12.2653969