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Motorized Micro-Forceps with Active Motion Guidance based on Common-Path SSOCT for Epiretinal Membranectomy
IEEE/ASME Transactions on Mechatronics ( IF 6.4 ) Pub Date : 2017-12-01 , DOI: 10.1109/tmech.2017.2749384
Gyeong Woo Cheon 1 , Berk Gonenc 2 , Russell H Taylor 3 , Peter L Gehlbach 4 , Jin U Kang 1
Affiliation  

In this study, we built and tested a handheld motion-guided microforceps 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 microforceps and our touch sensor activated microforceps. 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 microforceps). By activating the CP-SSOCT-based depth locking feature, the erroneous tool-tip motion can be further reduced down to 5.11 μm. We evaluated the performance of our device in comparison with 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.

中文翻译:

基于共通路径SSOCT的主动运动引导电动微型前足膜用于视网膜前膜切除术

在这项研究中,我们使用共路径扫频源光学相干断层扫描(CP-SSOCT)构建并测试了手持式运动引导微钳系统,以实现高度精确的深度控制前视网膜膜切开术。镊子设计中使用了触摸传感器和两个电机,以最小化固有的运动伪影,同时挤压工具手柄以致动工具并抓紧工具,并独立控制工具尖端的深度。设计了智能运动监控和引导算法以提供精确和直观的徒手控制。我们将抓握时发生的非自愿工具尖端运动与标准手动微型镊子和我们的触摸传感器激活的微型镊子进行了比较。结果表明,我们的基于触摸传感器和电机的工具可以显着减弱抓握过程中的运动伪影(119。我们的设备为81μm,而标准微型镊子为330.73μm)。通过激活基于CP-SSOCT的深度锁定功能,可以将错误的刀尖运动进一步减小至5.11μm。我们在尝试拾起细小纤维(Ø125μm)时所花费的时间,抓握次数以及在基材表面上产生的最大损伤深度方面,与标准仪器相比,评估了该设备的性能)从柔软的聚合物表面。结果表明,使用我们的设备时,所有指标均得到显着改善,值得注意的是,平均经过时间,抓取尝试次数和最大损坏深度分别减少了25%,31%和75%。错误的刀尖运动可以进一步减小到5.11μm。我们在尝试拾起细小纤维(Ø125μm)时所花费的时间,抓握次数以及在基材表面上产生的最大损伤深度方面,与标准仪器相比,评估了该设备的性能)从柔软的聚合物表面。结果表明,使用我们的设备时,所有指标均得到了显着改善,值得注意的是,平均经过时间,抓握尝试次数和最大损坏深度分别减少了25%,31%和75%。错误的刀尖运动可以进一步减小到5.11μm。我们在尝试拾起细小纤维(Ø125μm)时所花费的时间,抓握次数以及在基材表面上产生的最大损伤深度方面,与标准仪器相比,评估了该设备的性能)从柔软的聚合物表面。结果表明,使用我们的设备时,所有指标均得到显着改善,值得注意的是,平均经过时间,抓取尝试次数和最大损坏深度分别减少了25%,31%和75%。试图从柔软的聚合物表面拾取小片纤维(Ø125μm)时,会在基材表面产生最大的损坏深度。结果表明,使用我们的设备时,所有指标均得到了显着改善,值得注意的是,平均经过时间,抓握尝试次数和最大损坏深度分别减少了25%,31%和75%。试图从柔软的聚合物表面拾取小片纤维(Ø125μm)时,会在基材表面产生最大的损坏深度。结果表明,使用我们的设备时,所有指标均得到了显着改善,值得注意的是,平均经过时间,抓握尝试次数和最大损坏深度分别减少了25%,31%和75%。
更新日期:2017-12-01
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