Li Zhaoshuo, Shahbazi Mahya, Patel Niravkumar, O' Sullivan Eimear, Zhang Haojie, Vyas Khushi, Chalasani Preetham, Gehlbach Peter L, Iordachita Iulian, Yang Guang-Zhong, Taylor Russell H
Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, Maryland 21218, USA.
Hamlyn Centre for Robotic Surgery, Imperial College London, SW7 2AZ, London, UK.
Rep U S. 2019 Nov;2019:7083-7090. doi: 10.1109/IROS40897.2019.8967751. Epub 2020 Jan 27.
In this paper, a novel semi-autonomous control framework is presented for enabling probe-based confocal laser endomicroscopy (pCLE) scan of the retinal tissue. With pCLE, retinal layers such as nerve fiber layer (NFL) and retinal ganglion cell (RGC) can be scanned and characterized in real-time for an improved diagnosis and surgical outcome prediction. However, the limited field of view of the pCLE system and the micron-scale optimal focus distance of the probe, which are in the order of physiological hand tremor, act as barriers to successful manual scan of retinal tissue. Therefore, a novel sensorless framework is proposed for real-time semi-autonomous endomicroscopy scanning during retinal surgery. The framework consists of the Steady-Hand Eye Robot (SHER) integrated with a pCLE system, where the motion of the probe is controlled semi-autonomously. Through a hybrid motion control strategy, the system autonomously controls the confocal probe to optimize the sharpness and quality of the pCLE images, while providing the surgeon with the ability to scan the tissue in a tremor-free manner. Effectiveness of the proposed architecture is validated through experimental evaluations as well as a user study involving 9 participants. It is shown through statistical analyses that the proposed framework can reduce the work load experienced by the users in a statistically-significant manner, while also enhancing their performance in retaining pCLE images with optimized quality.
本文提出了一种新颖的半自主控制框架，用于实现基于探头的共聚焦激光内镜显微镜（pCLE）对视网膜组织的扫描。使用pCLE，可以实时扫描和表征视网膜神经纤维层（NFL）和视网膜神经节细胞（RGC）等视网膜层，以改善诊断和手术结果预测。然而，pCLE系统有限的视野以及探头微米级的最佳聚焦距离，与生理性手抖幅度相当，这成为成功手动扫描视网膜组织的障碍。因此，本文提出了一种新颖的无传感器框架，用于在视网膜手术期间进行实时半自主内镜显微镜扫描。该框架由与pCLE系统集成的稳手眼机器人（SHER）组成，其中探头的运动由半自主控制。通过混合运动控制策略，系统自主控制共聚焦探头以优化pCLE图像的清晰度和质量，同时使外科医生能够以无抖动的方式扫描组织。通过实验评估以及涉及9名参与者的用户研究验证了所提出架构的有效性。统计分析表明，所提出的框架可以在统计学上显著降低用户的工作量，同时还能提高他们保留优化质量的pCLE图像的性能。
