3D Guidewire Shape Reconstruction from Monoplane Fluoroscopic Images
Tudor Jianu Baoru Huang Pierre Berthet-Rayne Sebastiano Fichera Anh Nguyen
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.