医用导向针的可认证运动规划

Mengyu Fu, Oren Salzman, Ron Alterovitz
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引用次数: 13

摘要

医用导向针可以沿着三维曲线轨迹运动,避开解剖障碍,到达人体内部具有临床意义的目标。自动化可操控针头操作可以使医生和患者充分利用可操控针头的全部潜力,最大限度地利用其可操控性,安全、准确地达到医疗程序的目标,如活组织检查和癌症的局部治疗递送。为了使医疗程序的自动化在临床上被接受,从患者护理、安全和监管的角度来看,证明过程自动化中涉及的运动规划算法的正确性和有效性是至关重要的。在本文中,我们迈出了重要的一步,为可操纵针创建可认证的运动规划器。我们介绍了第一个可操纵针头的运动规划器,它提供了一个保证,在临床适当的假设下,它将在有限的时间内计算出一个精确的、避开障碍物的运动计划到指定的目标,或者通知用户不存在这样的计划。基于一种新的多分辨率规划方法,提出了一种高效、全分辨率的定向针运动规划方法。与最先进的可操纵针运动规划器(它们都不提供任何完整性保证)相比,我们证明了我们的新分辨率完整运动规划器计算计划的速度更快,成功率更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Toward Certifiable Motion Planning for Medical Steerable Needles.

Medical steerable needles can move along 3D curvilinear trajectories to avoid anatomical obstacles and reach clinically significant targets inside the human body. Automating steerable needle procedures can enable physicians and patients to harness the full potential of steerable needles by maximally leveraging their steerability to safely and accurately reach targets for medical procedures such as biopsies and localized therapy delivery for cancer. For the automation of medical procedures to be clinically accepted, it is critical from a patient care, safety, and regulatory perspective to certify the correctness and effectiveness of the motion planning algorithms involved in procedure automation. In this paper, we take an important step toward creating a certifiable motion planner for steerable needles. We introduce the first motion planner for steerable needles that offers a guarantee, under clinically appropriate assumptions, that it will, in finite time, compute an exact, obstacle-avoiding motion plan to a specified target, or notify the user that no such plan exists. We present an efficient, resolution-complete motion planner for steerable needles based on a novel adaptation of multi-resolution planning. Compared to state-of-the-art steerable needle motion planners (none of which provide any completeness guarantees), we demonstrate that our new resolution-complete motion planner computes plans faster and with a higher success rate.

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Toward Certifiable Motion Planning for Medical Steerable Needles. Latent Belief Space Motion Planning under Cost, Dynamics, and Intent Uncertainty Efficient Parametric Multi-Fidelity Surface Mapping Learning of Sub-optimal Gait Controllers for Magnetic Walking Soft Millirobots. Toward Asymptotically-Optimal Inspection Planning via Efficient Near-Optimal Graph Search.
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