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A team of UCLA and University of Houston (UH) scientists, led by Aydogan Ozcan in collaboration with Kirill Larin, used deep learning to train a neural network to rapidly reconstruct OCT images using undersampled spectral data. Although the deep-learning-based image reconstruction method was given significantly less spectral data than standard image reconstruction methods, it was able to reconstruct high-quality images without any spatial artifacts.
When undersampled spectral data is used with standard image reconstruction methods, it typically results in severe spatial artifacts in the reconstructed images.
To demonstrate the efficacy of the deep-learning-based framework for OCT imaging, the researchers trained and blindly…READ MORE