Toward certifiable optimal motion planning for medical steerable needles.

Medical steerable needles can follow 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. 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 planning algorithms involved in procedure automation. In this paper, we take an important step toward creating a certifiable optimal planner for steerable needles. We present an efficient, resolution-complete motion planner for steerable needles based on a novel adaptation of multi-resolution planning. This is the first motion planner for steerable needles that guarantees to compute in finite time an obstacle-avoiding plan (or notify the user that no such plan exists), under clinically appropriate assumptions. Based on this planner, we then develop the first resolution-optimal motion planner for steerable needles that further provides theoretical guarantees on the quality of the computed motion plan, that is, global optimality, in finite time. Compared to state-of-the-art steerable needle motion planners, we demonstrate with clinically realistic simulations that our planners not only provide theoretical guarantees but also have higher success rates, have lower computation times, and result in higher quality plans.

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© The Author(s) 2023.

Déclaration de conflit d'intérêts

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: RA is an inventor on university-owned patents on medical robotic devices incorporating steerable instruments that have been licensed to industry. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported in part by the United States National Institutes of Health (NIH) [grant number R01EB024864]; the United States National Science Foundation (NSF) [grant numbers 2008475, 2038855]; the Israeli Ministry of Science, Technology and Space (MOST) [grant numbers 3-16079, 3-17385]; the United States-Israel Binational Science Foundation (BSF) [grant number 2019703]; and the Ravitz Foundation.