Solving the missing cone problem of diffraction tomography using deep learning

DongHun Ryu; Dongmin Ryu; YoonSeok Baek; Hyeongjoo Cho; Geon Kim; Young Seo Kim; Yongki Lee; Yoosik Kim; Hyun-Seok Min; Yong Keun Park

doi:10.1117/12.2584961

5 March 2021 Solving the missing cone problem of diffraction tomography using deep learning

DongHun Ryu, Dongmin Ryu, YoonSeok Baek, Hyeongjoo Cho, Geon Kim, Young Seo Kim, Yongki Lee, Yoosik Kim, Hyun-Seok Min, Yong Keun Park

Author Affiliations +

Proceedings Volume 11653, Quantitative Phase Imaging VII; 116530G (2021) https://doi.org/10.1117/12.2584961
Event: SPIE BiOS, 2021, Online Only

Abstract

We present a deep learning approach for the rapid resolution enhancement of optical diffraction tomography. Once our three-dimensional U-net-based convolutional neural network learns an image translation between raw tomograms and total-variation-regularized tomograms, the trained network can fill in the missing cone of a measured refractive index tomogram and improve its resolution within seconds. We demonstrate the feasibility and generalizability of our approach on various biological samples, including bacteria, WBC, and NIH3T3.

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DongHun Ryu, Dongmin Ryu, YoonSeok Baek, Hyeongjoo Cho, Geon Kim, Young Seo Kim, Yongki Lee, Yoosik Kim, Hyun-Seok Min, and Yong Keun Park "Solving the missing cone problem of diffraction tomography using deep learning", Proc. SPIE 11653, Quantitative Phase Imaging VII, 116530G (5 March 2021); https://doi.org/10.1117/12.2584961

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