提出一种高效精准的神经血管导航路径规划算法，能智能避障并快速生成安全路径。
Contact-aware Path Planning for Autonomous Neuroendovascular Navigation
- 基于预/术中影像构建运动模型，用简化动作单元进行采样规划
- 最坏情况22.8秒内收敛，跟踪误差低于0.64毫米，成功率100%
- 适用于94%患者典型解剖结构，适合临床介入手术导航系统
我们提出一种确定性且时间高效的接触感知路径规划算法，用于神经血管导航。该算法利用术前和术中血管影像信息，智能预测并利用与解剖结构的交互，引导预弯的被动器械。通过推导运动学模型，并在基于采样的规划器中使用简化的运动基元进行树扩展，实现快速可行路径计算，精度损失可忽略。在多种代表性血管解剖结构中，算法在最坏情况下22.8秒内实现100%收敛，跟踪误差小于0.64毫米，且在代表约94%患者的解剖模型上表现有效。
原文摘要 · Abstract (English)
We propose a deterministic and time-efficient contact-aware path planner for neurovascular navigation. The algorithm leverages information from pre- and intra-operative images of the vessels to navigate pre-bent passive tools, by intelligently predicting and exploiting interactions with the anatomy. A kinematic model is derived and employed by the sampling-based planner for tree expansion that utilizes simplified motion primitives. This approach enables fast computation of the feasible path, with negligible loss in accuracy, as demonstrated in diverse and representative anatomies of the vessels. In these anatomical demonstrators, the algorithm shows a 100% convergence rate within 22.8s in the worst case, with sub-millimeter tracking errors (less than 0.64 mm), and is found effective on anatomical phantoms representative of around 94% of patients.
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