提出渐进式经验融合方法，提升血管内导航多任务控制成功率。
Progressive Experience Fusion for Multi-Task World Model Control in Endovascular Navigation
- 通过渐进融合多任务经验，优化控制器在复杂血管中的路径规划。
- 在10个已知血管结构上平均成功率提升至74%，优于基线模型。
- 可在新患者血管中通过微调实现高成功率，适合临床辅助导航应用。
自主血管内导航可支持偏远地区机械血栓切除术的实施，但控制器需在不同血管解剖结构下完成长而多阶段的路径导航。本研究探索了渐进式经验融合（PEF）方法，用于训练多任务TD-MPC2控制器。此外，评估了一种基于残差动作序列离散度动态调整模型预测路径积分规划时域的启发式策略，并在患者特异性仿真中进行微调。在10个已知血管结构上的5个子任务中，PEF达到74%的平均成功率，显著高于软演员-批评器（37%，p < 0.001）和基础TD-MPC2（65%，p = 0.053）。在30个血管结构上训练的自适应时域规划PEF控制器，在10个未见血管结构中达到90%的平均成功率。该PEF代理在未见过的体外脑卒中患者血管中经荧光透视验证，微调后路径比率从63%提升至80%（p < 0.001），共经历40×10³次微调步骤（约107分钟临床院间转运时间）。本工作为多血管结构训练与患者特异性适配提供了概念验证，但仍需进一步验证方可临床部署。
原文摘要 · Abstract (English)
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.
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