DEEP learning powered De-scattering with Excitation Patterning (DEEP) for high-throughput wide-field multiphoton microscopy

Navodini Wijethilake; Cheng Zheng; Jong K. Park; Murat Yildirim; Udith Haputhanthri; Peter T. C. So; Dushan N. Wadduwage

doi:10.1117/12.2615331

7 March 2022 DEEP learning powered De-scattering with Excitation Patterning (DEEP) for high-throughput wide-field multiphoton microscopy

Navodini Wijethilake, Cheng Zheng, Jong K. Park, Murat Yildirim, Udith Haputhanthri, Peter T. C. So, Dushan N. Wadduwage

Author Affiliations +

Proceedings Volume PC11965, Multiphoton Microscopy in the Biomedical Sciences XXII; PC119650B (2022) https://doi.org/10.1117/12.2615331
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

Multiphoton microscopy is the gold standard for deep tissue fluorescence imaging. Long wavelengths enable hundreds of microns deep penetration of excitation light, but the emission fluorescence at shorter wavelengths encounters scattering before detection. While not being an issue for point scanning geometries, for wide-field geometries emission light scattering degrades the image quality. In this work, we use temporally focused pattered excitations to spatially encode image information before emission light scattering. Upon detection, images are reconstructed computationally by solving a linear inverse problem. We further improve our results by learning inverse solvers and optimal patterns through physics-based deep learning.

Conference Presentation

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Navodini Wijethilake, Cheng Zheng, Jong K. Park, Murat Yildirim, Udith Haputhanthri, Peter T. C. So, and Dushan N. Wadduwage "DEEP learning powered De-scattering with Excitation Patterning (DEEP) for high-throughput wide-field multiphoton microscopy", Proc. SPIE PC11965, Multiphoton Microscopy in the Biomedical Sciences XXII, PC119650B (7 March 2022); https://doi.org/10.1117/12.2615331

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