Motorized Micro-Forceps with Active Motion Guidance based on Common-Path SSOCT for Epiretinal Membranectomy

Gyeong Woo Cheon, Berk Gonec, Russell H Taylor, Peter Gehlbach, Jin U. Kang

Research output: Contribution to journalArticle

Abstract

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 (θ 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.

Original languageEnglish (US)
JournalIEEE/ASME Transactions on Mechatronics
DOIs
StateAccepted/In press - Sep 4 2017

Fingerprint

Optical tomography
Sensors
Fibers
Monitoring
Polymers
Substrates

Keywords

  • Biomedical optical imaging
  • Grasping
  • Image sensors
  • Optical fiber dispersion
  • Optical fiber sensors
  • Optical signal detection
  • Optical signal processing
  • Retina
  • Surgery
  • Surgery
  • Tactile sensors
  • Tools

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

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title = "Motorized Micro-Forceps with Active Motion Guidance based on Common-Path SSOCT for Epiretinal Membranectomy",
abstract = "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 (θ 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.",
keywords = "Biomedical optical imaging, Grasping, Image sensors, Optical fiber dispersion, Optical fiber sensors, Optical signal detection, Optical signal processing, Retina, Surgery, Surgery, Tactile sensors, Tools",
author = "Cheon, {Gyeong Woo} and Berk Gonec and Taylor, {Russell H} and Peter Gehlbach and Kang, {Jin U.}",
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AU - Taylor, Russell H

AU - Gehlbach, Peter

AU - Kang, Jin U.

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