3D Guidewire Shape Reconstruction from Monoplane Fluoroscopic Images
This addresses the problem of radiation exposure and equipment reliance in endovascular interventions for medical practitioners, though it appears incremental as it matches existing performance.
The paper tackles 3D guidewire shape reconstruction from monoplane fluoroscopic images for endovascular navigation, proposing a method using a simulator and a neural network that achieves results comparable to conventional triangulation.
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.