The adaptive hermite fractal tree (AHFT): a novel surgical 3D path planning approach with curvature and heading constraints
Author(s)
Pinzi, Marlene
Galvan, Stefano
Rodriguez y Baena, Ferdinando
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.
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.
Date Issued
2019-04-01
Date Acceptance
2019-02-08
Citation
International Journal of Computer Assisted Radiology and Surgery, 2019, 14 (4), pp.659-670
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.
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
Commission of the European Communities
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000461349600009&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
688279
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Engineering, Biomedical
Radiology, Nuclear Medicine & Medical Imaging
Surgery
Engineering
Path planning
Nonholonomic constraint
Needle steering
Minimally invasive surgery
Bio-inspired
Robotic surgery
CONVECTION-ENHANCED DELIVERY
STEERABLE NEEDLES
Publication Status
Published
Date Publish Online
2019-02-21