医用可转向针自动化的安全起始区域
Safe Start Regions for Medical Steerable Needle Automation
AI总结：
针对医用可转向针人机交接时起始位姿偏差可能导致目标不可达的问题，提出一种评估起始位姿偏差鲁棒性的指标，兼顾位置与姿态偏差鲁棒性的权衡，经多场景仿真验证可结合不同规划器高效确定大尺寸安全起始区域。
摘要：
可转向针是一种微创医疗器械，可沿弯曲路径行进以避开关键解剖障碍，从而实现新型医疗操作。规划算法可用于生成抵达目标的可转向针运动规划。部署过程通常为：医生手动将可转向针以运动规划的起始位姿插入组织，随后将控制权移交机器人，由机器人自主操控针沿规划路径抵达目标。人机交接对手术成功至关重要，因为即使起始位姿存在微小偏差，也会改变可转向针的工作空间，且无法保证目标仍可抵达。我们提出了一种评估此类起始位姿偏差鲁棒性的指标。在测量这种偏差鲁棒性时，我们考虑了位置变化鲁棒性与姿态变化鲁棒性之间的权衡。我们通过抽象场景、肝脏规划场景和肺部规划场景的仿真对该指标进行了评估。评估结果表明，我们的指标可与不同的运动规划器结合使用，并能高效确定大面积的安全起始区域。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
