Adaptive optics scanning light ophthalmoscopy (AOSLO) reveals individual retinal cells and their function microvasculature and micropathologies in vivo. As compared to the single-channel offset pinhole and two-channel split detector non-confocal AOSLO designs by providing multi-directional imaging capabilities a new generation of multi-detector and (multi-)offset aperture AOSLO modalities have been demonstrated to provide critical information about retinal microstructures. However increasing detection channels requires expensive optical components and/or critically increases imaging time. To address this issue we introduce a novel combination of machine learning and optics as an integrated technology to compressively capture twelve non-confocal channel AOSLO images simultaneously. Imaging of healthy and diseased subjects using the proposed Deep Compressed Multi-Channel AOSLO (DCAOSLO) showed enhanced visualization of rods cones and mural cells with over an order of magnitude improvement in imaging speed as compared to conventional offset aperture imaging.
