Hyperspectral imaging (HSI) captures a greater level of spectral detail than traditional optical imaging, making it a potentially valuable intraoperative tool when precise tissue differentiation is essential. Hardware limitations of current optical systems used for handheld real-time video HSI result in a limited focal depth, thereby posing usability issues for integration of the technology into the operating room. This work integrates a focus-tunable liquid lens into a video HSI exoscope, and proposes novel video autofocusing methods based on deep reinforcement learning. A first-of-its-kind robotic focal-time scan was performed to create a realistic and reproducible testing dataset. We benchmarked our proposed autofocus algorithm against traditional policies, and found our novel approach to perform significantly ($p<0.05$) better than traditional techniques ($0.070\pm.098$ mean absolute focal error compared to $0.146\pm.148$). In addition, we performed a blinded usability trial by having two neurosurgeons compare the system with different autofocus policies, and found our novel approach to be the most favourable, making our system a desirable addition for intraoperative HSI.

翻译：高光谱成像（HSI）相比传统光学成像能捕获更高层级的光谱细节，在需要精确组织区分的术中场景中具有潜在应用价值。当前用于手持式实时视频高光谱成像的光学系统受限于硬件性能，导致焦深范围有限，从而阻碍该技术整合至手术室。本研究将可调焦液体透镜集成至视频高光谱手术显微镜中，并提出基于深度强化学习的创新视频自动对焦方法。我们首次开展了机器人焦距时间扫描实验，构建了具有真实性与可复现性的测试数据集。通过将所提自动对焦算法与传统策略进行基准对比，发现新方法的平均绝对焦距误差（$0.070\pm.098$）显著优于传统技术（$0.146\pm.148$）（$p<0.05$）。此外，我们邀请两位神经外科医生对采用不同自动对焦策略的系统进行盲法可用性试验，结果表明本方法获得最高评价，证实该系统是术中高光谱成像的理想辅助工具。