Real-Time 3D Guidewire Reconstruction from Intraoperative DSA Images for Robot-Assisted Endovascular Interventions

TL;DR

This paper presents a real-time framework that combines preoperative CTA with intraoperative DSA images to accurately reconstruct 3D guidewire shapes, enhancing navigation and precision in robot-assisted endovascular procedures.

Contribution

It introduces a novel multimodal approach using feature extraction and deformable registration to achieve fast, accurate 3D guidewire reconstruction from 2D DSA images.

Findings

Projection error of 1.76 pixels

Length deviation of 2.93%

Frame rate of 39.3 FPS

Abstract

Accurate three-dimensional (3D) reconstruction of guidewire shapes is crucial for precise navigation in robot-assisted endovascular interventions. Conventional 2D Digital Subtraction Angiography (DSA) is limited by the absence of depth information, leading to spatial ambiguities that hinder reliable guidewire shape sensing. This paper introduces a novel multimodal framework for real-time 3D guidewire reconstruction, combining preoperative 3D Computed Tomography Angiography (CTA) with intraoperative 2D DSA images. The method utilizes robust feature extraction to address noise and distortion in 2D DSA data, followed by deformable image registration to align the 2D projections with the 3D CTA model. Subsequently, the inverse projection algorithm reconstructs the 3D guidewire shape, providing real-time, accurate spatial information. This framework significantly enhances spatial awareness for robotic-assisted endovascular procedures, effectively bridging the gap between preoperative planning and intraoperative execution. The system demonstrates notable improvements in real-time processing speed, reconstruction accuracy, and computational efficiency. The proposed method achieves a projection error of 1.76$\pm$0.08 pixels and a length deviation of 2.93$\pm$0.15\%, with a frame rate of 39.3$\pm$1.5 frames per second (FPS). These advancements have the potential to optimize robotic performance and increase the precision of complex endovascular interventions, ultimately contributing to better clinical outcomes.