Abstract
Purpose :
Optical Coherence Tomography (OCT) is an important imaging technique in ophthalmology. Traditionally, clinical OCT requires an operator and patient cooperation to both align and correct for refractive errors. Automating the tasks of alignment and focusing can increase the accessibility of OCT and make it available outside of specialty clinics. In order to automate these tasks, we developed a robotically-aligned OCT scanner that automatically aligns and corrects defocus refractive error.
Methods :
We built a 100kHz swept-source retinal OCT scanner designed for 16-degree field of view imaging. This scanner was mounted on a 7 degree-of-freedom robot (Fig. 1) along with 3 pupil cameras and 4 IR LEDs for eye tracking. The pupil cameras capture real-time images of the eye, which are then utilized to extract location—through linear triangulation—and orientation—through pupil-center and corneal-reflection segmentation analysis. Using the tracking information as input, the robotic arm is controlled in real-time to orient the scanner relative to the eye’s optical axis as well as position it to correct for misalignment error. Once aligned, the system triggers automatic focusing to correct any refractive errors. Dynamically switching from regular dense volumes (160,000 AScans) to a sparse scan (400 AScans) in real-time allowed for faster focusing compared to traditional approaches. We demonstrated the automatic focusing of the system by imaging a retinal model eye (Rowe Tech. Design) through varying trial lenses to evaluate the time to regain focus and the dioptric correction range (Fig. 2).
Results :
Focusing was obtained in <3s over a Diopter range of -12D to 12D. Gaze tracking tests demonstrate 0.337° accuracy and 0.236° precision within a 28° range. Pupil tracking tests demonstrate 19.5μm lateral and 34.2μm axial accuracy along with 5.9μm lateral and 6.1μm axial precision within a 300mm lateral and 150mm axial range. Freestanding human imaging without a fixation target demonstrated successful automatic alignment.
Conclusions :
We demonstrate an autonomous system that can image freestanding subjects as well as quickly correct for ocular defocus.
This is a 2021 ARVO Annual Meeting abstract.