Segmented Trajectory Optimization of Flexible Needles Based on Deep Reinforcement Learning

Minimally invasive liver tumor ablation relies heavily on safe and accurate flexible needle trajectory planning. Traditional methods optimize the entire path uniformly and ignore distinct clinical requirements of extrahepatic and intrahepatic tissues. They also suffer from premature convergence in c...

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Bibliographic Details

Main Authors:	Yeyufei Xin, Yi Yue, Fan Zhang, Yaozong Huang, Xin Wu
Format:	Artigo
Language:	Inglês
Published:	IEEE 2026-01-01
Series:	IEEE Access
Subjects:	Deep reinforcement learning parameter adaption flexible needle path planning particle swarm algorithm
Online Access:	https://ieeexplore.ieee.org/document/11509322/
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