机器人自动塑形导丝，提升血管介入导航精度与一致性。
An Anatomy-specific Guidewire Shaping Robot for Improved Vascular Navigation
- 基于患者影像自动生成个性化导丝形状，实现自主塑形。
- 2D形变预测误差仅0.56mm，可精准复现C、S、钩状等临床常见尖端形态。
- 支持3D复杂路径导航，适合高难度神经血管手术场景。
神经内血管入路常依赖在术前手工塑形的微导丝，由医生根据术前影像和经验判断适合特定解剖结构的导丝形状，以引导微导管抵达目标。该过程在复杂迂曲解剖中尤为困难，高度依赖医生经验。为实现标准化自主塑形，本文提出一种桌面式导丝塑形机器人，可生成针对导航需求的特定导丝构型。我们构建了将期望导丝形状映射为机器人动作的模型，并通过实验数据进行校准。结果表明，该机器人可精准生成临床常见的尖端形态（如C形、S形、偏角形、钩形），并在二维平面上验证其与模型预测形状的一致性，整体均方根误差（RMS）为0.56mm。此外，还展示了三维尖端塑形能力，并成功实现从岩骨段颈内动脉（ICA）至后交通动脉（PComm）的复杂内腔路径导航。
原文摘要 · Abstract (English)
Neuroendovascular access often relies on passive microwires that are hand-shaped at the back table and then used to track a microcatheter to the target. Neuroendovascular surgeons determine the shape of the wire by examining the patient pre-operative images and using their experience to identify anatomy specific shapes of the wire that would facilitate reaching the target. This procedure is particularly complex in convoluted anatomical structures and is heavily dependent on the level of expertise of the surgeon. Towards enabling standardized autonomous shaping, we present a bench-top guidewire shaping robot capable of producing navigation-specific desired wire configurations. We present a model that can map the desired wire shape into robot actions, calibrated using experimental data. We show that the robot can produce clinically common tip geometries (C, S, Angled, Hook) and validate them with respect to the model-predicted shapes in 2D. Our model predicts the shape with a Root Mean Square (RMS) error of 0.56mm across all shapes when compared to the experimental results. We also demonstrate 3D tip shaping capabilities and the ability to traverse complex endoluminal navigation from the petrous Internal Carotid Artery (ICA) to the Posterior Communicating Artery (PComm).
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