Endovascular navigation, essential for diagnosing and treating endovascular diseases, predominantly hinges on fluoroscopic images due to the constraints in sensory feedback. Current shape reconstruction techniques for endovascular intervention often rely on either a priori information or specialized equipment, potentially subjecting patients to heightened radiation exposure. While deep learning holds potential, it typically demands extensive data. In this paper, we propose a new method to reconstruct the 3D guidewire by utilizing CathSim, a state-of-the-art endovascular simulator, and a 3D Fluoroscopy Guidewire Reconstruction Network (3D-FGRN). Our 3D-FGRN delivers results on par with conventional triangulation from simulated monoplane fluoroscopic images. Our experiments accentuate the efficiency of the proposed network, demonstrating it as a promising alternative to traditional methods.
3D Guidewire Shape Reconstruction from Monoplane Fluoroscopic Images
Lect. Notes in Networks, Syst.
International Conference on Robot Intelligence Technology and Applications ; 2023 ; Taicang December 06, 2023 - December 08, 2023
2024-11-22
11 pages
Article/Chapter (Book)
Electronic Resource
English
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