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13 March 2024 Artificial intelligence improves pixel resolution of cones from sparsely sampled AOOCT
Vineeta Das, Andrew J. Bower, Nancy Aguilera, Joanne Li, Zhuolin Liu, Daniel X. Hammer, Alfredo Dubra, Johnny Tam
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Proceedings Volume PC12824, Ophthalmic Technologies XXXIV; PC1282410 (2024) https://doi.org/10.1117/12.3000362
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
Adaptive optics optical coherence tomography (AOOCT) requires a dense sampling of the retina to visualize individual cones in the living human eye. This in turn increases the acquisition time and introduces susceptibility to eye motion artifacts. Here, we present hybrid transformer generative adversarial network (HT-GAN), an artificial intelligence technique that can improve the pixel resolution of images to better reveal cones from sparsely sampled AOOCT volumes. The method can potentially increase the speed of acquisition by four-fold while maintaining the visibility of individual cones despite a lower than ideal pixel sampling.
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Vineeta Das, Andrew J. Bower, Nancy Aguilera, Joanne Li, Zhuolin Liu, Daniel X. Hammer, Alfredo Dubra, and Johnny Tam "Artificial intelligence improves pixel resolution of cones from sparsely sampled AOOCT", Proc. SPIE PC12824, Ophthalmic Technologies XXXIV, PC1282410 (13 March 2024); https://doi.org/10.1117/12.3000362
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KEYWORDS
Cones
Pixel resolution
Artificial intelligence
Visualization
Transformers
In vivo imaging
Modeling
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