Abstract
Purpose :
Adaptive optics (AO) imaging techniques are an exceptional research tool which enables visualization of the retina at a cellular level. Among them, AO-Flood illumination ophthalmoscopes give distortion-free images at a high cadence, while AO-scanning laser ophthalmoscopes yield highly contrasted images but suffer from distortion. Bridging the gap between these two techniques, we propose here a distortion-free, high framerate and high contrast multimodal imaging modality.
Methods :
We developed a camera-based, AO-line scanning ophthalmoscope. The light emitted from a SLED (Thorlabs, USA) is projected in the retina into a line pattern. The rolling shutter of the camera is synchronized with the illumination scan via a software. Acting as a virtual pinhole, the camera filters out the multi-scattered photons and greatly improves the contrast of the images while keeping a high framerate (200 Hz) over a large field of view (2.5° x 4.5°).
Moreover, the offset between the illumination and detection lines can be easily modified, enabling multimodal imaging such as bright field imaging, dark field imaging and split detection imaging on the same platform.
Results :
In the bright field configuration, a contrast and resolution equivalent to those of AO-scanning laser ophthalmoscopes have been achieved. The photoreceptors are distinguishable and highly contrasted on a single 5 ms exposure distortion-free image (cf. image 1). In addition, dark field imaging and split detection imaging enables us to achieve phase contrast imaging, thus revealing low reflectivity, refractive structures such as blood vessels walls, capillaries, and red blood cells (cf. image 2).
Conclusions :
We demonstrated a high resolution, high framerate, distortion free and high contrast imaging modality. The multimodality of this techniques along with the high spatio-temporal resolution paves the way for the study of dynamic phenomena in the retina such as neurovascular coupling, quantitative blood flow measurement or single red blood cells tracking.
This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.