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A new path planning method for bevel-tip flexible needle insertion in 3D space with multiple targets and obstacles
ZhenTan1,DanZhang1,Hua-gengLiang2,Qing-guoWang3,WenjianCai4
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(1 Department of Automation, Zhejiang University of Technology, Hangzhou 310023, Zhejiang, China;2 Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, Hubei, China;3 Beijing Normal University at Zhuhai, and also with the BNU-HKBU United International College, Zhuhai 519087, Guangdong, China;4 Department of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore)
摘要:
In this paper, a new bevel-tip flexible needle path planning method based on the bee-foraging learning particle swarm optimization (BFL-PSO) algorithm and the needle retraction strategy in 3D space is proposed to improve the puncture accuracy and shorten the puncture distance in the case of multiple puncture targets. First, the movement of the needle after penetrating the human body is analyzed, and the objective function which includes puncture path error, puncture path length, and collision function is established. Then, the BFL-PSO algorithm and the needle retraction strategy are analyzed. Finally, medical images of the tissue to be punctured are obtained by medical imaging instruments, i.e., magnetic resonance (MR), and the 3D model of the punctured environment is constructed by 3D Slicer to obtain the environment information on targets and obstacles, and the path of flexible needle is carried out based on the BFL-PSO optimization algorithm and the needle retraction strategy. The simulation results show that, compared with other path planning methods in the related literature, the new path planning method proposed in this paper has higher path planning accuracy, shorter puncture distance, and good adaptability to multi-target path planning problems.
关键词:  Path planning · Multi-objective optimization · Bee-foraging learning · Particle swarm
DOI:https://doi.org/10.1007/s11768-022-00113-y
基金项目:This work was supported in part by the National Key R&D Funding (No. 2018YFE0206900).
A new path planning method for bevel-tip flexible needle insertion in 3D space with multiple targets and obstacles
Zhen Tan1,Dan Zhang1,Hua-geng Liang2,Qing-guo Wang3,Wenjian Cai4
(1 Department of Automation, Zhejiang University of Technology, Hangzhou 310023, Zhejiang, China;2 Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, Hubei, China;3 Beijing Normal University at Zhuhai, and also with the BNU-HKBU United International College, Zhuhai 519087, Guangdong, China;4 Department of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore)
Abstract:
In this paper, a new bevel-tip flexible needle path planning method based on the bee-foraging learning particle swarm optimization (BFL-PSO) algorithm and the needle retraction strategy in 3D space is proposed to improve the puncture accuracy and shorten the puncture distance in the case of multiple puncture targets. First, the movement of the needle after penetrating the human body is analyzed, and the objective function which includes puncture path error, puncture path length, and collision function is established. Then, the BFL-PSO algorithm and the needle retraction strategy are analyzed. Finally, medical images of the tissue to be punctured are obtained by medical imaging instruments, i.e., magnetic resonance (MR), and the 3D model of the punctured environment is constructed by 4D Slicer to obtain the environment information on targets and obstacles, and the path of flexible needle is carried out based on the BFL-PSO optimization algorithm and the needle retraction strategy. The simulation results show that, compared with other path planning methods in the related literature, the new path planning method proposed in this paper has higher path planning accuracy, shorter puncture distance, and good adaptability to multi-target path planning problems.
Key words:  Path planning · Multi-objective optimization · Bee-foraging learning · Particle swarm