A Dual-Camera High-Resolution Hyperspectral Imaging System for the Retina

Hyperspectral imaging (HSI) emerged as a powerful tool for biomedical applications, particularly in the analysis and discrimination of biological tissues. In this study, we developed a dual-camera system that integrates hyperspectral imaging with high-resolution RGB imaging to capture detailed retinal images. We tested different pan-sharpening algorithms to enhance the spatial resolution of the hyperspectral images, combining the spectral details of HSI with the spatial details of RGB imaging. We developed algorithms to estimate the diameter of retinal vessels and the oxygenation rate. We validated our algorithms by imaging a mouse retinal phantom and then by imaging mice under anesthesia. Our systems showed the ability to resolve fine structural details, including small blood vessels. We found that out of the tested methods, PSGAN offered the best pansharpened image both quantitatively and qualitatively, with a root-mean-squared error (RMSE) score of 2.15 × 10^-2. Using the pansharpened hyperspectral image, we measured vessels diameter and vessel oxygenation rate. We found the average diameter for arterioles and venules to be 45.7 μm and 31.5 μm, respectively. The average oxygenation rate for arterioles and venules were 96.2% and 76.3% respectively. This study represents a significant step towards the development of a versatile retinal imaging tool, with potential applications in both research and clinical diagnostics. Future work will focus on in vivo testing, algorithm refinement, and the exploration of specific retinal disease markers using this imaging system.