Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach

Ruoyang Yao; Marien Ochoa; Pingkun Yan; Xavier Intes

doi:10.1038/s41377-019-0138-x

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Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach

Letters

- Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach
- Light: Science & Applications Vol. 8, Issue 2, Pages: 212-218(2019)
- Affiliations：
  
  Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- Author bio：
  
  Xavier Intes (intesx@rpi.edu)
- Funds：
- DOI：10.1038/s41377-019-0138-x
  CLC：
- Published：2019，
  
  Published Online：06 March 2019，
  
  Received：04 October 2018，
  
  Revised：06 February 2019，
  
  Accepted：13 February 2019
- Accepted：
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Yao, R. Y. et al. Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach. Light: Science & Applications, 8, 212-218 (2019).
DOI：

Yao, R. Y. et al. Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach. Light: Science & Applications, 8, 212-218 (2019). DOI： 10.1038/s41377-019-0138-x.

摘要

Abstract

Macroscopic fluorescence lifetime imaging (MFLI) via compressed sensed (CS) measurements enables efficient and accurate quantification of molecular interactions in vivo over a large field of view (FOV). However

the current data-processing workflow is slow

complex and performs poorly under photon-starved conditions. In this paper

we propose Net-FLICS

a novel image reconstruction method based on a convolutional neural network (CNN)

to directly reconstruct the intensity and lifetime images from raw time-resolved CS data. By carefully designing a large simulated dataset

Net-FLICS is successfully trained and achieves outstanding reconstruction performance on both in vitro and in vivo experimental data and even superior results at low photon count levels for lifetime quantification.

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Keywords

references

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