用于血管内导航多任务世界模型控制的渐进式经验融合
Progressive Experience Fusion for Multi-Task World Model Control in Endovascular Navigation
- School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）
- Surgical & Interventional Engineering（外科与介入工程）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出渐进式经验融合（PEF）训练多任务TD-MPC2控制器，在血管内导航任务中提升了成功率，可实现跨血管结构迁移与患者特异性微调，为临床应用提供概念验证。
AI中文摘要：
自主血管内导航可支持将机械取栓术输送到医疗服务不足的地区，但控制器必须在不同血管解剖结构中沿长距离多阶段路径导航。本研究探究渐进式经验融合（Progressive Experience Fusion, PEF）以训练多任务TD-MPC2控制器，还评估了一种通过残差动作序列离散度改变模型预测路径积分规划 horizon 的启发式方法，并在患者特异性模拟中进行微调。在10种已知训练解剖结构的5个子任务中，采用保留目标的设置，PEF的平均成功率为74%，而Soft Actor-Critic为37%（p<0.001），基础TD-MPC2为65%（p=0.053）；在30种血管结构上训练的带自适应 horizon 规划的PEF控制器，在10种保留血管结构中达到90%的平均成功率。PEF智能体在荧光透视下成功迁移至未见过的体外卒中患者血管结构，经40×10³次微调步骤（对应约107分钟的医院间转运临床时间）微调后，平均路径比从63%提升至80%（p<0.001）。本研究为多血管结构训练和患者特异性适应提供了概念验证，但临床部署前需进一步验证。
英文摘要：
Autonomous endovascular navigation could support the delivery of mechanical thrombectomy to underserved areas, but controllers must navigate long, multi-stage paths across varying vascular anatomies. This study investigates Progressive Experience Fusion (PEF) to train a multi-task TD-MPC2 controller. We additionally evaluate a heuristic that changes the Model Predictive Path Integral planning horizon using residual action-sequence dispersion, and fine-tuning in a patient-specific simulation. Across five subtasks in ten known training anatomies with held-out targets, PEF achieved a mean success rate of 74%, compared with 37% for Soft Actor-Critic (p < 0.001) and 65% for base TD-MPC2 (p = 0.053). A PEF controller with adaptive-horizon planning trained on 30 vasculatures achieved a mean success rate of 90% in ten held-out vasculatures. The PEF agent successfully transferred to an unseen in vitro stroke patient vasculature under fluoroscopy, achieving a mean path ratio improvement from 63% to 80% with fine-tuning (p < 0.001), following 40x103 fine-tuning steps (corresponding to approximately 107 min of clinical inter-hospital transfer time). This work represents a proof of concept for multi-vasculature training and patient-specific adaptation, while further validation is required before clinical deployment.
