基于在线专家纠正的高效学习用于内血管分叉导航的自主导管操控
Sample-Efficient Learning with Online Expert Correction for Autonomous Catheter Steering in Endovascular Bifurcation Navigation
AI总结：
本文提出一种结合在线专家纠正的样本高效强化学习框架，用于内血管分叉导航中的自主导管操控，提升了在复杂血管结构中的操控精度和鲁棒性。
摘要：
机器人辅助的内血管干预提供了一种安全有效的远程导管操控解决方案，减少了辐射暴露的同时实现了精确导航。强化学习（RL）最近已成为自主导管操控的有前途的方法；然而，传统方法在稀疏奖励设计和依赖静态血管模型方面存在局限，限制了其样本效率和对术中变化的泛化能力。为克服这些挑战，本文介绍了一种样本高效的RL框架，用于内血管分叉导航中的自主导管操控。所提出的框架集成了三个关键组件：（1）基于分割的姿态估计模块，用于准确的实时状态反馈；（2）模糊控制器用于分叉意识的方位调整；（3）结合专家先验的结构化奖励生成器，用于指导策略学习。通过利用在线专家纠正，该框架减少了探索的低效率，并增强了在复杂血管结构中的策略鲁棒性。在使用透明血管仿真的机器人平台上进行的实验验证表明，所提出的方法在123次训练回合内达到收敛——相比基线Soft Actor-Critic（SAC）算法减少了25.9%——同时将平均位置误差降低到基线的83.8%。这些结果表明，结合样本高效的RL与在线专家纠正能够实现可靠且准确的导管操控，特别是在解剖上具有挑战性的分叉场景中，这对于内血管导航至关重要。
英文摘要：
Robot-assisted endovascular intervention offers a safe and effective solution for remote catheter manipulation, reducing radiation exposure while enabling precise navigation. Reinforcement learning (RL) has recently emerged as a promising approach for autonomous catheter steering; however, conventional methods suffer from sparse reward design and reliance on static vascular models, limiting their sample efficiency and generalization to intraoperative variations. To overcome these challenges, this paper introduces a sample-efficient RL framework with online expert correction for autonomous catheter steering in endovascular bifurcation navigation. The proposed framework integrates three key components: (1) A segmentation-based pose estimation module for accurate real-time state feedback, (2) A fuzzy controller for bifurcation-aware orientation adjustment, and (3) A structured reward generator incorporating expert priors to guide policy learning. By leveraging online expert correction, the framework reduces exploration inefficiency and enhances policy robustness in complex vascular structures. Experimental validation on a robotic platform using a transparent vascular phantom demonstrates that the proposed approach achieves convergence in 123 training episodes -- a 25.9% reduction compared to the baseline Soft Actor-Critic (SAC) algorithm -- while reducing average positional error to 83.8% of the baseline. These results indicate that combining sample-efficient RL with online expert correction enables reliable and accurate catheter steering, particularly in anatomically challenging bifurcation scenarios critical for endovascular navigation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
