De-scattering with Excitation Patterning enables rapid wide-field imaging through scattering media
Name
sciadv.aay5496.pdf
Description
Published version
Size
1.51 MB
Format
Adobe PDF
Checksum (MD5)
e97144508c2256fab7f0ecff08af0a6d
Author(s)
Zheng, Cheng
Park, Jong Kang
Yildirim, Murat
Boivin, Josiah R
Xue, Yi
Sur, Mriganka
So, Peter TC
Wadduwage, Dushan N
Date Issued
July 2021
Journal
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Citation
Zheng, Cheng, Park, Jong Kang, Yildirim, Murat, Boivin, Josiah R, Xue, Yi et al. 2021. "De-scattering with Excitation Patterning enables rapid wide-field imaging through scattering media." Science Advances, 7 (28).
Version
Final published version
Abstract
Nonlinear optical microscopy has enabled in vivo deep tissue imaging on the millimeter scale. A key unmet challenge is its limited throughput especially compared to rapid wide-field modalities that are used ubiquitously in thin specimens. Wide-field imaging methods in tissue specimens have found successes in optically cleared tissues and at shallower depths, but the scattering of emission photons in thick turbid samples severely degrades image quality at the camera. To address this challenge, we introduce a novel technique called De-scattering with Excitation Patterning or "DEEP," which uses patterned nonlinear excitation followed by computational imaging-assisted wide-field detection. Multiphoton temporal focusing allows high-resolution excitation patterns to be projected deep inside specimen at multiple scattering lengths due to the use of long wavelength light. Computational reconstruction allows high-resolution structural features to be reconstructed from tens to hundreds of DEEP images instead of millions of point-scanning measurements.
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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Creative Commons Attribution NonCommercial License 4.0
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DOI of Published Version
https://doi.org/10.1126/sciadv.aay5496
