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.

翻译：我们提出了一种确定性且高效的接触感知路径规划器，用于神经血管内导航。该算法利用血管术前和术中图像信息，通过智能预测并利用与解剖结构的相互作用，来导航预弯曲的被动器械。基于采样的规划器采用简化运动基元进行树扩展，并推导并应用了运动学模型。该方法能够快速计算可行路径，且精度损失可忽略不计，这一点在多种代表性血管解剖结构中得到了验证。在这些解剖演示模型中，该算法在最坏情况下于22.8秒内实现了100%的收敛率，亚毫米级跟踪误差（小于0.64毫米），且在代表约94%患者群体的解剖体模上表现出有效性。