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Development and validation of a high-resolution hyperspectral imaging system for the retina
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DOI：10.1117/1.JBO.31.3.036006.png)
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Abstract
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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 mu m, whereas the average lumen diameter of the arterioles was 31.5 +/- 8.7 mu 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.
Keywords:
retina
hyperspectral imaging
topical endoscopic fundus imaging
oxygen saturation
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