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The adaptive hermite fractal tree (AHFT): a novel surgical 3D path planning approach with curvature and heading constraints

Title: The adaptive hermite fractal tree (AHFT): a novel surgical 3D path planning approach with curvature and heading constraints
Authors: Pinzi, M
Galvan, S
Rodriguez y Baena, F
Item Type: Journal Article
Abstract: Purpose In the context of minimally invasive neurosurgery, steerable needles such as the one developed within the Horizon2020-funded EDEN2020 project (Frasson et al. in Proc Inst Mech Eng Part H J Eng Med 224(6):775–88, 2010. https://doi.org/10.1243/09544119JEIM663; Secoli and y Baena in IEEE international conference on robotics and automation, 2013) aspire to address the clinical challenge of better treatment for cancer patients. The direct, precise infusion of drugs in the proximity of a tumor has been shown to enhance its effectiveness and diffusion in the surrounding tissue (Vogelbaum and Aghi in Neuro-Oncology 17(suppl 2):ii3–ii8, 2015. https://doi.org/10.1093/neuonc/nou354). However, planning for an appropriate insertion trajectory for needles such as the one proposed by EDEN2020 is challenging due to factors like kinematic constraints, the presence of complex anatomical structures such as brain vessels, and constraints on the required start and target poses. Methods We propose a new parallelizable three-dimensional (3D) path planning approach called Adaptive Hermite Fractal Tree (AHFT), which is able to generate 3D obstacle-free trajectories that satisfy curvature constraints given a specified start and target pose. The AHFT combines the Adaptive Fractal Tree algorithm’s efficiency (Liu et al. in IEEE Robot Autom Lett 1(2):601–608, 2016. https://doi.org/10.1109/LRA.2016.2528292) with optimized geometric Hermite (Yong and Cheng in Comput Aided Geom Des 21(3):281–301, 2004. https://doi.org/10.1016/j.cagd.2003.08.003) curves, which are able to handle heading constraints. Results Simulated results demonstrate the robustness of the AHFT to perturbations of the target position and target heading. Additionally, a simulated preoperative environment, where the surgeon is able to select a desired entry pose on the patient’s skull, confirms the ability of the method to generate multiple feasible trajectories for a patient-specific case. Conclusions The AHFT method can be adopted in any field of application where a 3D path planner with kinematic and heading constraints on both start and end poses is required.
Issue Date: 1-Apr-2019
Date of Acceptance: 8-Feb-2019
URI: http://hdl.handle.net/10044/1/69215
DOI: https://doi.org/10.1007/s11548-019-01923-3
ISSN: 1861-6429
Publisher: Springer
Start Page: 659
End Page: 670
Journal / Book Title: International Journal of Computer Assisted Radiology and Surgery
Volume: 14
Issue: 4
Copyright Statement: © 2019 The Author(s).This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 688279
Keywords: Science & Technology
Life Sciences & Biomedicine
Engineering, Biomedical
Radiology, Nuclear Medicine & Medical Imaging
Path planning
Nonholonomic constraint
Needle steering
Minimally invasive surgery
Robotic surgery
1103 Clinical Sciences
Nuclear Medicine & Medical Imaging
Publication Status: Published
Online Publication Date: 2019-02-21
Appears in Collections:Mechanical Engineering