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 (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.

Keywords: Biomedical optical imaging; Image sensors; Optical signal detection; Optical signal processing; Surgery.

Fig. 1

(a) Epiretinal membranectomy: after grasping the membrane edge with a micro-forceps the membrane is pulled away from the retina surface slowly; (b) The standard 23 Gauge disposable micro-forceps (Alcon, USA); Our motorized active motion-guided micro-forceps: (c) fabricated prototype, (d) inner structure with two motors (M1 and M2), and (e) the fiber-optic sensor (SSOCT) attached to the straightened arm of the jaws.

Fig. 2

(a) CP-SSOCT fiber-optic setup: the reflected beam (RB), fiber-air interface (FAI), and the sample reflected beam (SB). Overview of our CP-SSOCT depth-guided micro-forceps system: (b) earlier “SMART: microforceps prototype using one actuator for depth guidance and the squeeze mechanism for grasping [24], (c) current design using a touch sensor and two independently actuated motors for the control of grasping (green) and depth guidance (red) actions.

Fig. 3

Calibration curve for the touch sensor. The curve is divided into three zones: (1) dead zone where the tool-tip manipulating motor does not react to the touch sensor, (2) active zone that is responsible for motor control, and (3) saturation zone where the motor has reached its maximum position.

Fig. 4

Data processing flowchart consisting of 1) OCT signal processing, 2) Surface detection signal processing, and 3) Motion control processing. Each step is implemented in independent software module; especially, the OCT signal processing module uses parallel processing technique based on GPU programming.

Fig. 5

(a) Grasp and pick-up experiments: small fiber pieces (Ø 125 μm) were picked up from a soft polymer surface using MGMF. (b) A 2D scanning OCT imaging system was used to analyze the damage created on the polymer surface after each grasp. (c) A snapshot of the developed software displaying real-time A-scan data, video view of the operation site, and the touch sensor output (red bar graph).

Fig. 6

Measured distance variation as the surgeon opens and closes the forceps while trying to maintain the tool tip fixed above a thin film layer (a) using a conventional 23 gauge micro-forceps, using our micro-forceps (actuated via the touch sensor) (b) without and (c) with the motion guidance feature. 10 grasps were performed for each case. Vertical bars show the opening and closure of the forceps jaws. The motion artifact was significantly reduced by the using the touch sensor based actuation on our tool rather than the squeezing mechanism on the conventional micro-forceps. The OCT-based motion guidance successfully eliminated any distance variation.

Fig. 7

Frequency analysis of the distance variation during the grasping actions: (a) using a standard manual micro-forceps, using our motorized micro-forceps (b) without and (c) with the motion guidance feature.

Fig. 8

Results of the grasp and pick-up experiment: Statistically significant reduction in (a) elapsed time, (b) number of grasping attempts, and (c) the maximum depth of damage on the substrate with the use of our OCT-based motion guidance feature. The solid bars show the mean, and the error bars represent the maximum and minimum values.

Fig. 9

The maximum depth of damage vs. elapsed time during the grasp and pick-up experiments. The trials without the motion guidance have a significantly wider distribution than the guided trials, which have are grouped around much smaller damage and elapsed time levels.