用强化学习让机器人自动找放射源，提升手术精准度。
Hybrid Deep Reinforcement Learning for Radio Tracer Localisation in Robotic-assisted Radioguided Surgery
- 结合强化学习与自适应扫描，分阶段定位放射源。
- 仿真中成功率95%，真实手术平台达80%。
- 适合需要减少人为依赖的精准外科场景。
放射引导手术（如前哨淋巴结活检）依赖非成像伽马/贝塔探测器精确定位放射性靶点。传统人工检测依赖术者对辐射强度视觉或听觉信号的空间判断，易受主观因素影响。本文提出一种基于学习的方法，通过机器人自主导航探头至放射性靶点，实现放射性示踪剂的自动检测。该方法创新性地融合深度强化学习（DRL）与自适应网格扫描：前者利用历史数据高效导航，后者提供初始方向估计。仿真结果表明，成功率高达95%，相较传统方法更高效且鲁棒。在da Vinci Research Kit（dVRK）平台的真实实验进一步验证了可行性，成功率达80%。该方法有望提升手术一致性，降低操作者依赖，增强流程准确性。
原文摘要 · Abstract (English)
Radioguided surgery, such as sentinel lymph node biopsy, relies on the precise localization of radioactive targets by non-imaging gamma/beta detectors. Manual radioactive target detection based on visual display or audible indication of gamma level is highly dependent on the ability of the surgeon to track and interpret the spatial information. This paper presents a learning-based method to realize the autonomous radiotracer detection in robot-assisted surgeries by navigating the probe to the radioactive target. We proposed novel hybrid approach that combines deep reinforcement learning (DRL) with adaptive robotic scanning. The adaptive grid-based scanning could provide initial direction estimation while the DRL-based agent could efficiently navigate to the target utilising historical data. Simulation experiments demonstrate a 95% success rate, and improved efficiency and robustness compared to conventional techniques. Real-world evaluation on the da Vinci Research Kit (dVRK) further confirms the feasibility of the approach, achieving an 80% success rate in radiotracer detection. This method has the potential to enhance consistency, reduce operator dependency, and improve procedural accuracy in radioguided surgeries.
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