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.

我们提出了一种利用 CathSim 和 3D-Fluoroscopy Guidewire Reconstruction Network (3D-FGRN) 重建三维导线的新方法，该方法能够与传统的三角化方法在模拟单平面荧光成像中达到相同水平，有效地证明了该网络作为传统方法的有希望的替代品。