DEEP-squared: deep learning powered De-scattering with Excitation Patterning

Navodini Wijethilake; Mithunjha Anandakumar; Cheng Zheng; Peter T. C. So; Murat Yildirim; Dushan N. Wadduwage

doi:10.1038/s41377-023-01248-6

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DEEP-squared: deep learning powered De-scattering with Excitation Patterning

Original Articles

- DEEP-squared: deep learning powered De-scattering with Excitation Patterning
- DEEP-squared: deep learning powered De-scattering with Excitation Patterning
- LSA 2023年12卷第10期页码：2199-2214
- Affiliations：
  
  1.Center for Advanced Imaging, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA
  2.Department of Electronic and Telecommunication Engineering, University of Moratuwa, Moratuwa, Sri Lanka
  3.Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
  4.Laser Biomedical Research Center, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
  5.Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
  6.Picower Institute for Learning and Memory, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
  7.Department of Neuroscience, Cleveland Clinic Lerner Research Institute, Cleveland, OH 44195, USA
- Author bio：
  
  Dushan N. Wadduwage (wadduwage@fas.harvard.edu)
- Funds：
- DOI：10.1038/s41377-023-01248-6
  中图分类号：
- 纸质出版日期：2023-10-31，
  
  网络出版日期：2023-09-13，
  
  收稿日期：2023-03-01，
  
  修回日期：2023-07-21，
  
  录用日期：2023-07-29
- Accepted：
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DEEP-squared: deep learning powered De-scattering with Excitation Patterning[J]. LSA, 2023,12(10):2199-2214.

Wijethilake, N. et al. DEEP-squared: deep learning powered De-scattering with Excitation Patterning. Light: Science & Applications, 12, 2199-2214 (2023).
DEEP-squared: deep learning powered De-scattering with Excitation Patterning[J]. LSA, 2023,12(10):2199-2214. DOI： 10.1038/s41377-023-01248-6.

Wijethilake, N. et al. DEEP-squared: deep learning powered De-scattering with Excitation Patterning. Light: Science & Applications, 12, 2199-2214 (2023). DOI： 10.1038/s41377-023-01248-6.

摘要

Abstract

Limited throughput is a key ch

allenge 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 DEEP

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.

关键词

Keywords

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