Development and validation of a high-resolution hyperspectral imaging system for the retina.

Abstract

Significance: Early detection of Alzheimer's diseases, diabetic retinopathy, or macular degeneration with advanced retinal imaging technologies can help improve patient care and treatment outcome.

Aim: We aim to create a high-resolution hyperspectral imaging (HSI) system for the retina. Retinal vessel diameter and oxygenation rate will be extracted simultaneously from HSI data.

Approach: Our hyperspectral retinal imaging system consists of a snapshot hyperspectral camera, a high-resolution RGB camera, a beamsplitter, and an imaging endoscope. Multiple pansharpening algorithms, including deep learning methods, were developed to generate high-resolution hyperspectral images that were further used for the measurement of vessel size and oxygenation rate in mice.

Results: The hyperspectral retinal imaging system was tested for its spatial resolution and spectral fidelity in retina phantoms. In vivo imaging experiments were performed in mice. The deep learning-based pansharpening algorithm achieved a root mean square error (RMSE) of $2.15\pm 0.64$ , a correlation coefficient (CC) of $0.96\pm 0.05$ , a spectral angle score of $0.06\pm 0.03$ radians, and an error relative global dimensionless synthesis (ERGAS) score of $2.37\pm 1.71$ . Oxygen saturation ( ${\mathrm{sO}}_2$ ) and lumen diameters of blood vessels were measured in the retina. The average lumen diameter of the venules was $45.7\pm 13.6\mu m$ , whereas the average lumen diameter of the arterioles was $31.5\pm 8.7\mu m$ . The average arteriole ${\mathrm{sO}}_2$ was 98%, whereas the average venule ${\mathrm{sO}}_2$ was 58%.

Conclusions: A high-resolution hyperspectral imaging system was developed and validated for retina imaging and measurement of blood vessels and oxygen saturation.