Abstract
Steerable needles are minimally invasive devices that enable novel medical procedures by following curved paths to avoid critical anatomical obstacles. Planning algorithms can be used to find a steerable needle motion plan to a target. Deployment typically consists of a physician manually inserting the steerable needle into tissue at the motion plan's start pose and handing off control to a robot, which then autonomously steers it to the target along the plan. The handoff between human and robot is critical for procedure success, as even small deviations from the start pose change the steerable needle's workspace and there is no guarantee that the target will still be reachable. We introduce a metric that evaluates the robustness to such start pose deviations. When measuring this robustness to deviations, we consider the tradeoff between being robust to changes in position versus changes in orientation. We evaluate our metric through simulation in an abstract, a liver, and a lung planning scenario. Our evaluation shows that our metric can be combined with different motion planners and that it efficiently determines large, safe start regions.
Abstract (translated)
可操纵的针是一种最小侵入性的设备，通过遵循弯曲路径来避开关键解剖结构，从而实现新型医疗程序。规划算法可用于找到可操纵针的运动计划到目标。部署通常包括医生在运动计划开始时手动将可操纵针插入组织，然后将控制交给机器人，它沿着计划自主地将针引导到目标。人机之间的接管对于手术成功至关重要，因为即使是最小的始位置偏差也会改变可操纵针的工作区，而且无法保证目标仍然可达。我们引入了一个评估这种始位置偏差稳健性的指标。在评估这种偏差时，我们考虑了在位置变化和方向变化之间的权衡。我们在抽象、肝脏和肺规划场景中通过仿真来评估我们的指标。我们的评估表明，我们的指标可以与不同运动规划相结合，并能有效地确定大而安全的始位置区域。
URL
https://arxiv.org/abs/2404.08558
