High-speed optical diffraction tomography (ODT)  with deep-learning approach (Conference Presentation)

Baoliang Ge; Mo Deng; George Barbastathis; Peter T. C. So; Renjie Zhou; Zahid Yaqoob

doi:10.1117/12.2546172

11 March 2020 High-speed optical diffraction tomography (ODT) with deep-learning approach (Conference Presentation)

Baoliang Ge, Mo Deng, George Barbastathis, Peter T. C. So, Renjie Zhou, Zahid Yaqoob

Proceedings Volume 11249, Quantitative Phase Imaging VI; 1124906 (2020) https://doi.org/10.1117/12.2546172
Event: SPIE BiOS, 2020, San Francisco, California, United States

Abstract

Optical diffraction tomography (ODT) is a powerful label-free three-dimensional (3D) quantitative imaging technique. However, current ODT modalities require around 50 different illumination angles to reconstruct the 3D refraction index (RI) map, which limits its imaging speed and prohibit it from further applications. Here we propose a deep-learning approach to reduce the number of illumination angles and improve the imaging speed of ODT. With 3D Unet architecture and large training data of different species of cells, we can decrease the number of illumination angles from 49 to 5 with similar reconstruction performance, which empowers ODT the capability to reveal high-speed biological dynamics.

Conference Presentation

Citation Download Citation

Baoliang Ge, Mo Deng, George Barbastathis, Peter T. C. So, Renjie Zhou, and Zahid Yaqoob "High-speed optical diffraction tomography (ODT) with deep-learning approach (Conference Presentation)", Proc. SPIE 11249, Quantitative Phase Imaging VI, 1124906 (11 March 2020); https://doi.org/10.1117/12.2546172

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