Snyder, V. C. | Search bioRxiv

bioRxiv2020

Improvements to multi-offset adaptive optics scanning laser ophthalmoscopy for in vivo imaging of individual retinal ganglion cell layer neurons in humans

Previous work has shown that multi-offset detection in adaptive optics scanning laser ophthalmoscopy (AOSLO) can be used to image retinal ganglion cells (RGCs) in monkeys and humans. However, though images of RGCs in anesthetized monkeys with high light levels produced high contrast images of RGCs, images from humans failed to reach the same contrast due to several drawbacks in the previous dual-wavelength multi-offset approach. Our aim here was to design and build a multi-offset detection pattern for humans at safe light levels that could reveal the retinal ganglion cell layer neurons with a contrast, robustness and acquisition time approaching results only previously obtained in monkeys. Here, we present a new imaging system using only one light source, compared to the previous dual-wavelength used on monkeys. Our single-wavelength solution allows for increased light power and eliminates problematic chromatic aberrations. Then, we demonstrate that a radial multi-offset detection pattern with an offset distance of 8-10 Airy Disk Diameter (ADD) is optimal to detect photons multiply scattered in all directions from RGCs thereby enhancing their contrast. This new setup and image processing pipeline led to improved imaging of retinal ganglion cells using multi-offset imaging in AOSLO.

bioengineering↗

bioRxiv2020

Improvements to strip-based digital image registration for robust eye-tracking and to minimize distortions in images from scanned ophthalmic imaging systems

Retinal image-based eye tracking from scanned ophthalmic imaging systems, such as scanning laser ophthalmoscopy, has allowed for precise real-time eye tracking at sub-micron resolution. To achieve real-time processing rates, strip-based image registration methods for real-time applications have several constraints that limit their performance. This trade-off is acceptable for many imaging and psychophysical applications but when the objective is precise eye motion measurement over time, a high error tolerance can be consequential. Dropped strips in these applications can complicate FEMs quantification. Some light starved imaging applications, such as autofluorescence retinal imaging, also require the retention and registration of as much of the data as possible to increase the signal to noise ratio in the final integrated or averaged image. We show here that eye motion can be extracted from image sequences from scanned imaging systems more consistently when the constraints of real-time processing are lifted, and all data is available at the time of registration. This is enabled with additional image processing steps to achieve a more robust solution. Our iterative approach identifies and discards distorted frames, detects coarse motion to generate a synthetic reference frame and then uses it for fine scale motion tracking with improved sensitivity over a larger area. We demonstrate its application here to tracking scanning laser ophthalmoscopy (TSLO) and adaptive optics scanning light ophthalmoscopy (AOSLO). We show that it can successfully capture most of the eye motion across each image sequence, leaving only between 0.04-3.39% of non-blink frames untracked, even with low quality images, while simultaneously minimizing image distortions induced from eye motion. These improvements will facilitate precise FEMs measurement in TSLO and longitudinal tracking of individual cells in AOSLO.

bioengineering↗