Resolution enhancement and noise reduction of optical coherence tomography using dual generative adversarial networks

Woojin Lee; Hyeong Soo Nam Sr.; Hongki Yoo Sr.

doi:10.1117/12.2612547

7 March 2022 Resolution enhancement and noise reduction of optical coherence tomography using dual generative adversarial networks

Woojin Lee, Hyeong Soo Nam Sr., Hongki Yoo Sr.

Author Affiliations +

Proceedings Volume PC11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI; PC119481I (2022) https://doi.org/10.1117/12.2612547
Event: SPIE BiOS, 2022, San Francisco, California, United States

Conference Poster

Abstract

We proposed a dual-GAN-based deep learning to enhance resolution and reduce noise of optical coherence tomography (OCT). The dual GAN was designed with a model that enhances axial resolution and a model that enhances lateral resolution and reduces noise. We demonstrated improvements on the swine coronary artery data used for training, and further validated the performance on other sample data acquired in other systems. Through this, not only the performance but also the feasibility of independent application to a specific system or sample was verified. The current approach will be highly helpful in overcoming existing limitations of OCT.

Conference Presentation

Citation Download Citation

Woojin Lee, Hyeong Soo Nam Sr., and Hongki Yoo Sr. "Resolution enhancement and noise reduction of optical coherence tomography using dual generative adversarial networks", Proc. SPIE PC11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI, PC119481I (7 March 2022); https://doi.org/10.1117/12.2612547

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