Cheon Gyeong Woo, Gonenc Berk, Taylor Russell H, Gehlbach Peter L, Kang Jin U
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
ERC for Computer Integrated Surgery at Johns Hopkins University, Baltimore, MD, USA.
IEEE ASME Trans Mechatron. 2017 Dec;22(6):2440-2448. doi: 10.1109/TMECH.2017.2749384. Epub 2017 Sep 5.
In this study, we built and tested a handheld motion-guided micro-forceps system using common-path swept source optical coherence tomography (CP-SSOCT) for highly accurate depth controlled epiretinal membranectomy. A touch sensor and two motors were used in the forceps design to minimize the inherent motion artifact while squeezing the tool handle to actuate the tool and grasp, and to independently control the depth of the tool-tip. A smart motion monitoring and a guiding algorithm were devised to provide precise and intuitive freehand control. We compared the involuntary tool-tip motion occurring while grasping with a standard manual micro-forceps and our touch sensor activated micro-forceps. The results showed that our touch-sensor-based and motor-actuated tool can significantly attenuate the motion artifact during grasping (119.81 μm with our device versus 330.73 μm with the standard micro-forceps). By activating the CP-SSOCT based depth locking feature, the erroneous tool-tip motion can be further reduced down to 5.11μm. We evaluated the performance of our device in comparison to the standard instrument in terms of the elapsed time, the number of grasping attempts, and the maximum depth of damage created on the substrate surface while trying to pick up small pieces of fibers (Ø 125 μm) from a soft polymer surface. The results indicate that all metrics were significantly improved when using our device; of note, the average elapsed time, the number of grasping attempts, and the maximum depth of damage were reduced by 25%, 31%, and 75%, respectively.
在本研究中,我们构建并测试了一种手持式运动引导微钳系统,该系统使用共光路扫频源光学相干断层扫描(CP-SSOCT)进行高精度深度控制的视网膜前膜切除术。在钳子设计中使用了一个触摸传感器和两个电机,以在挤压工具手柄来驱动工具并进行抓取时最小化固有的运动伪影,并独立控制工具尖端的深度。设计了一种智能运动监测和引导算法,以提供精确且直观的徒手控制。我们比较了使用标准手动微钳和我们的触摸传感器激活微钳抓取时发生的非自愿工具尖端运动。结果表明,我们基于触摸传感器且由电机驱动的工具在抓取过程中可显著减弱运动伪影(我们的设备为119.81μm,而标准微钳为330.73μm)。通过激活基于CP-SSOCT的深度锁定功能,错误的工具尖端运动可进一步降低至5.11μm。我们在从柔软聚合物表面拾取小纤维碎片(直径125μm)时,就耗时、抓取尝试次数以及在基底表面造成的最大损伤深度方面,将我们的设备与标准器械的性能进行了比较。结果表明,使用我们的设备时所有指标均有显著改善;值得注意的是,平均耗时、抓取尝试次数以及最大损伤深度分别减少了25%、31%和75%。