Abstract
Purpose :
To demonstrate an ultrafast optoelectronic device that provides 1200 retinal images per second from the living human eye. To show ability of detection ocular motion like eye drifts, ocular tremor, saccades and microsaccades. To show the ability of using the detected eye motion for an on-line image stabilization in the Scanning Laser Opthalmoscope (SLO) imaging module operating at 30 frames per second in the clinical environment.
Methods :
The tracking system based on an SLO principle equipped with two dimensional microelectromechanical systems (MEMS) scanner oscillating at ~20 kHz (fast axis) and ~600 Hz (slow axis) was designed and assembled. The probing beam power at the cornea is set to 350µW @ 850nm. The system acquires 1200 images (256 x 32 pixels each) per second from a 1° x 1° region of the retina. The images are captured by a custom-built integrated circuit equipped with a field-programmable gate array unit (FPGA). The displacement between consecutive retinal images caused by the eye motion is found and the correction signal for image stabilization for SLO imaging module is calculated. Specifically designed algorithms based on nonlinear image correlation are used to find local shifts between retinal image frames with subpixel resolution. The imaging SLO is a separate device combined with the tracker and provides 30 images per second from larger field of view ~10° x 10°.
Results :
The ability to stabilize SLO image using the tracker device is proved on 20 eyes of 5 healthy volunteers and 12 patients with diabetic retinopathy aged between 27 and70 y.o. The achieved time delay for motion detection is estimated to be below 1.5 ms in both groups of subjects. The residual image motion, related to image distortions (caused mainly by monochromatic ocular aberrations and scanning arrangement of the imaging setup), is corrected numerically in post-processing.
Conclusions :
The results show that our ultrafast eye-tracking device is able to provide retinal images almost free of motion artifacts. Moreover, raw data from the eye tracker sub-system can be used in order to reconstruct the exact record of eye movements occurring during retinal imaging procedure. This type of data has a potential to be used clinically for an early detection of eye diseases related with eye movements disorders (central nervous system diseases or injuries, ocular tumor, diabetes or general hypertension disease).
This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.