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Resolution-Optimal Motion Planning for Steerable Needles.
Fu, Mengyu; Solovey, Kiril; Salzman, Oren; Alterovitz, Ron.
Affiliation
  • Fu M; Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
  • Solovey K; Computer Science Department, Technion - Israel Institute of Technology, Israel.
  • Salzman O; Computer Science Department, Technion - Israel Institute of Technology, Israel.
  • Alterovitz R; Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
IEEE Int Conf Robot Autom ; 2022: 9652-9659, 2022 May.
Article in En | MEDLINE | ID: mdl-36337768
Medical steerable needles can follow 3D curvilinear trajectories inside body tissue, enabling them to move around critical anatomical structures and precisely reach clinically significant targets in a minimally invasive way. Automating needle steering, with motion planning as a key component, has the potential to maximize the accuracy, precision, speed, and safety of steerable needle procedures. In this paper, we introduce the first resolution-optimal motion planner for steerable needles that offers excellent practical performance in terms of runtime while simultaneously providing strong theoretical guarantees on completeness and the global optimality of the motion plan in finite time. Compared to state-of-the-art steerable needle motion planners, simulation experiments on realistic scenarios of lung biopsy demonstrate that our proposed planner is faster in generating higher-quality plans while incorporating clinically relevant cost functions. This indicates that the theoretical guarantees of the proposed planner have a practical impact on the motion plan quality, which is valuable for computing motion plans that minimize patient trauma.

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: IEEE Int Conf Robot Autom Year: 2022 Document type: Article Affiliation country: Estados Unidos Country of publication: Estados Unidos

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: IEEE Int Conf Robot Autom Year: 2022 Document type: Article Affiliation country: Estados Unidos Country of publication: Estados Unidos