发表机构
Kings College London; The University of Melbourne（伦敦国王学院; 墨尔本大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究首次展示血管内脑机接口接入的体外自主机器人导航，利用软演员-评论家控制器在模拟中训练并评估，成功率最高达98.4%，并验证了在线失败预测的可行性。
AI 中文摘要
血管内脑机接口（BCIs）避免了开颅手术，但需要通过解剖结构多变的脑静脉精确递送设备。本研究首次展示了在体外条件下，针对脑静脉系统中血管内脑机接口接入的自主机器人导航。软演员-评论家（Soft Actor-Critic）控制器在计算机模拟中针对两个连续任务进行训练，任务范围从右侧颈内静脉延伸至上矢状窦，并利用一个训练解剖结构的几何增强数据。导航在训练解剖结构和未见过的解剖保留模型上进行了评估，包括250次模拟试验和每个任务-解剖条件下的五次透视引导体外机器人运行，总计1,000次模拟试验和20次物理运行。同时评估了任务递归预测器用于在线识别即将发生的导航失败。在模拟中，任务A和任务B在训练解剖结构中的成功率分别为85.6%和98.4%，在保留解剖结构中分别为42.0%和91.6%。20次物理运行中有14次成功（总体70%），其中任务B在保留模型中的成功率为80%。在模拟中，预测器检测到99.3%至100.0%的失败，误报率为0.8%至6.7%。在体外评估期间，预测风险在失败事件前增加，但在某些成功运行中升高的概率表明迁移后校准降低。这些结果证明了自主脑静脉接入的可行性，并展示了在线失败预测如何支持人类监督，同时确定了解剖泛化和模拟到现实校准作为临床前转化前的优先事项。
英文摘要
Endovascular brain-computer interfaces (BCIs) avoid craniotomy but require precise device delivery through anatomically variable cerebral veins. This work presents the first demonstration of in vitro autonomous robotic navigation for endovascular BCI access in the cerebral venous system. Soft Actor-Critic controllers were trained in silico for two sequential tasks spanning the right internal jugular vein to the superior sagittal sinus, using geometric augmentation of one training anatomy. Navigation was evaluated in a training anatomy and an anatomically unseen hold-out model over 250 in silico episodes and five fluoroscopy-guided in vitro robotic runs per task-anatomy condition, comprising 1,000 simulated episodes and 20 physical runs overall. Task recurrent predictors were also evaluated for online identification of impending navigation failure. In silico success rates for Tasks A and B were 85.6% and 98.4% in the training anatomy and 42.0% and 91.6% in the hold-out anatomy, respectively. Fourteen of 20 physical runs were successful (70% overall), including 80% success for Task B in the hold-out phantom. In silico the predictors detected 99.3-100.0% of failures with false-alarm rates of 0.8-6.7%. During in vitro evaluation, predicted risk increased before failed episodes, but elevated probabilities during some successful runs showed reduced calibration after transfer. These results demonstrate the feasibility of autonomous cerebral venous access and show how online failure prediction could support human oversight, while also identifying anatomical generalization and sim-to-real calibration as priorities before preclinical translation.
