DEEP-squared: deep learning powered De-scattering with Excitation Patterning
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s41377-023-01248-6.pdf
Description
Published version
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7.31 MB
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Checksum (MD5)
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Author(s)
Wijethilake, Navodini
Anandakumar, Mithunjha
Zheng, Cheng
So, Peter T. C.
Yildirim, Murat
Wadduwage, Dushan N.
Date Issued
September 13, 2023
Journal
Light: Science & Applications
Publisher
Springer Science and Business Media LLC
Citation
Wijethilake, N., Anandakumar, M., Zheng, C. et al. DEEP-squared: deep learning powered De-scattering with Excitation Patterning. Light Sci Appl 12, 228 (2023).
Version
Final published version
Abstract
Limited throughput is a key challenge in in vivo deep tissue imaging using nonlinear optical microscopy. Point scanning multiphoton microscopy, the current gold standard, is slow especially compared to the widefield imaging modalities used for optically cleared or thin specimens. We recently introduced “De-scattering with Excitation Patterning” or “DEEP” as a widefield alternative to point-scanning geometries. Using patterned multiphoton excitation, DEEP encodes spatial information inside tissue before scattering. However, to de-scatter at typical depths, hundreds of such patterned excitations were needed. In this work, we present DEEP2, a deep learning-based model that can de-scatter images from just tens of patterned excitations instead of hundreds. Consequently, we improve DEEP’s throughput by almost an order of magnitude. We demonstrate our method in multiple numerical and experimental imaging studies, including in vivo cortical vasculature imaging up to 4 scattering lengths deep in live mice.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Laser Biomedical Research Center
Massachusetts Institute of Technology. Department of Biological Engineering
Picower Institute for Learning and Memory
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DOI of Published Version
https://doi.org/10.1038/s41377-023-01248-6
