Toward certifiable optimal motion planning for medical steerable needles

Author:

Fu Mengyu1ORCID,Solovey Kiril2ORCID,Salzman Oren3,Alterovitz Ron1ORCID

Affiliation:

1. Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

2. Department of Electrical and Computer Engineering, Technion–Israel Institute of Technology, Haifa, Israel

3. Department of Computer Science, Technion–Israel Institute of Technology, Haifa, Israel

Abstract

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.

Funder

National Institutes of Health

United States-Israel Binational Science Foundation

Ministry of Science, Technology and Space

Ravitz Foundation

United States National Science Foundation

Publisher

SAGE Publications

Subject

Applied Mathematics,Artificial Intelligence,Electrical and Electronic Engineering,Mechanical Engineering,Modeling and Simulation,Software

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