基于接触感知的导航模型，实现脑卒中导管自动精准到达目标血管。
Towards Autonomous Navigation of Neuroendovascular Tools for Timely Stroke Treatment via Contact-aware Path Planning
- 利用解剖几何与器械力学建模，规划导管与血管互动路径。
- 50次独立实验100%成功抵达左颈总动脉，误差容忍达10°和10mm。
- 适合机器人辅助血管介入手术，尤其需高精度导航的临床场景。
本文提出一种基于模型的接触感知运动规划方法，用于急性缺血性脑卒中神经内血管工具的自主导航。针对目前常用的可伸缩预弯导管（如导丝和导管），建立其运动学模型及与周围解剖结构的相互作用模型，以预测导管操控路径。通过融合术前三维影像分割获取的解剖几何信息与可伸缩器械的力学特性，规划器可在与周围组织交互的前提下，引导导管到达目标。设计了用于插入与旋转的执行平台，并在从降主动脉根部至左颈总动脉（LCCA）的导航任务中验证。结果显示，在50次独立实验中，该方案实现100%成功率。同时研究了主动脉运动及机器人初始定位误差下的鲁棒性：当主动脉旋转最大达10°、冠状面位移不超过10mm时，仍能成功抵达目标。
原文摘要 · Abstract (English)
In this paper, we propose a model-based contact-aware motion planner for autonomous navigation of neuroendovascular tools in acute ischemic stroke. The planner is designed to find the optimal control strategy for telescopic pre-bent catheterization tools such as guidewire and catheters, currently used for neuroendovascular procedures. A kinematic model for the telescoping tools and their interaction with the surrounding anatomy is derived to predict tools steering. By leveraging geometrical knowledge of the anatomy, obtained from pre-operative segmented 3D images, and the mechanics of the telescoping tools, the planner finds paths to the target enabled by interacting with the surroundings. We propose an actuation platform for insertion and rotation of the telescopic tools and present experimental results for the navigation from the base of the descending aorta to the LCCA. We demonstrate that, by leveraging the pre-operative plan, we can consistently navigate the LCCA with 100% success of over 50 independent trials. We also study the robustness of the planner towards motion of the aorta and errors in the initial positioning of the robotic tools. The proposed plan can successfully reach the LCCA for rotations of the aorta of up to 10°, and displacement of up to 10mm, on the coronal plane.
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