基于智能实时虚拟iOCT体数据切片的视网膜下注射机器人导航自主系统
Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing
AI总结：
针对机器人系统与iOCT融合在视网膜下注射手术中面临的处理速率、动态配准等挑战，提出含器械位姿估计、在线配准、轨迹规划及智能虚拟B扫描的自主导航框架，经离体猪眼实验验证了精度与可重复性。
摘要：
过去十年间，研究人员推出了多种可辅助精细视网膜手术的机器人平台。与此同时，为实现手术区域的语义理解，近期研究进展已使集成显微镜的术中光学相干断层扫描（iOCT）能够以接近视频的速率生成高分辨率三维图像。机器人技术与语义理解的结合，让机器人视网膜手术（如视网膜下注射）实现任务自主成为可能。该手术需要精准的针头插入才能获得最佳治疗效果。然而，将机器人系统与iOCT融合也带来了新挑战，包括但不限于对数据处理速率的高要求，以及手术过程中两类系统的动态配准问题。本研究提出了一种基于iOCT体数据智能实时处理的视网膜下注射自主机器人导航框架。该方法包含器械位姿估计方法、机器人系统与iOCT系统的在线配准，以及专为导航至注射靶点定制的轨迹规划模块。我们还提出了智能虚拟B扫描——一种由卷积神经网络（CNNs）实现的、用于快速器械位姿估计的体数据切片方法。我们在离体猪眼上开展的实验验证了该方法的精度与可重复性。最后，我们讨论了本研究中发现的挑战，并提出了推动此类系统进一步发展的潜在解决方案。
英文摘要：
In the last decade, various robotic platforms have been introduced that could support delicate retinal surgeries. Concurrently, to provide semantic understanding of the surgical area, recent advances have enabled microscope-integrated intraoperative Optical Coherent Tomography (iOCT) with high-resolution 3D imaging at near video rate. The combination of robotics and semantic understanding enables task autonomy in robotic retinal surgery, such as for subretinal injection. This procedure requires precise needle insertion for best treatment outcomes. However, merging robotic systems with iOCT introduces new challenges. These include, but are not limited to high demands on data processing rates and dynamic registration of these systems during the procedure. In this work, we propose a framework for autonomous robotic navigation for subretinal injection, based on intelligent real-time processing of iOCT volumes. Our method consists of an instrument pose estimation method, an online registration between the robotic and the iOCT system, and trajectory planning tailored for navigation to an injection target. We also introduce intelligent virtual B-scans, a volume slicing approach for rapid instrument pose estimation, which is enabled by Convolutional Neural Networks (CNNs). Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method. Finally, we discuss identified challenges in this work and suggest potential solutions to further the development of such systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
