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

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 ± 0.64 , a correlation coefficient (CC) of 0.96 ± 0.05 , a spectral angle score of 0.06 ± 0.03 radians, and an error relative global dimensionless synthesis (ERGAS) score of 2.37 ± 1.71 . Oxygen saturation ( sO 2 ) and lumen diameters of blood vessels were measured in the retina. The average lumen diameter of the venules was 45.7 ± 13.6    μ m , whereas the average lumen diameter of the arterioles was 31.5 ± 8.7    μ m . The average arteriole sO 2 was 98%, whereas the average venule 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.