Deep learning-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data

Yijie Zhang; Tairan Liu; Manmohan Singh; Ege Çetintaş; Yilin Luo; Yair Rivenson; Kirill Larin; Aydogan Ozcan

doi:10.1117/12.2607930

7 March 2022 Deep learning-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data

Yijie Zhang, Tairan Liu, Manmohan Singh, Ege Çetintaş, Yilin Luo, Yair Rivenson, Kirill Larin, Aydogan Ozcan

Author Affiliations +

Proceedings Volume PC11952, Multimodal Biomedical Imaging XVII; PC119520D (2022) https://doi.org/10.1117/12.2607930
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

We report neural network-based rapid reconstruction of swept-source OCT (SS-OCT) images using undersampled spectral data. We trained and blindly tested a deep neural network using mouse embryo samples imaged by an SS-OCT system. Using >3-fold undersampled spectral data per A-line, the trained neural network can blindly remove spatial aliasing artifacts due to spectral undersampling, presenting a very good match to the images reconstructed using the full spectral data. This method can be integrated with various swept-source or spectral domain OCT systems to potentially improve the 3D imaging speed without a sacrifice in resolution or signal-to-noise of the reconstructed images.

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Yijie Zhang, Tairan Liu, Manmohan Singh, Ege Çetintaş, Yilin Luo, Yair Rivenson, Kirill Larin, and Aydogan Ozcan "Deep learning-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data", Proc. SPIE PC11952, Multimodal Biomedical Imaging XVII, PC119520D (7 March 2022); https://doi.org/10.1117/12.2607930

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