实时融合术前CT与术中造影,精准重建导丝三维形状。
Real-Time 3D Guidewire Reconstruction from Intraoperative DSA Images for Robot-Assisted Endovascular Interventions
- 结合术前CT与术中2D造影,通过可变形配准实现三维重建
- 投影误差仅1.76±0.08像素,长度偏差2.93±0.15%,帧率39.3FPS
- 适合机器人辅助血管介入手术,提升术中空间感知精度
在机器人辅助血管介入手术中,准确的导丝三维(3D)重建对精确导航至关重要。传统二维数字减影血管造影(DSA)因缺乏深度信息,导致空间模糊,影响导丝形状感知。本文提出一种新型多模态实时3D导丝重建框架,融合术前3D计算机断层血管造影(CTA)与术中2D DSA图像。方法通过鲁棒特征提取应对2D DSA数据中的噪声与失真,随后采用可变形图像配准将2D投影与3D CTA模型对齐,再通过逆投影算法重建3D导丝形态,提供实时、高精度的空间信息。该框架显著增强机器人介入手术的空间感知能力,有效弥合术前规划与术中执行的差距。系统在实时处理速度、重建精度和计算效率方面表现突出,实现投影误差1.76±0.08像素、长度偏差2.93±0.15%,帧率39.3±1.5帧每秒(FPS)。这些进展有望优化机器人性能,提升复杂血管介入的精度,改善临床结果。
原文摘要 · Abstract (English)
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.
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