Spatial resolution enhancement in photon-starved STED imaging using deep learning-based fluorescence lifetime analysis-Reference-Cited by-同舟云学术

Spatial resolution enhancement in photon-starved STED imaging using deep learning-based fluorescence lifetime analysis

Published:2023 Issue:21 Volume:15 Page:9449-9456
ISSN:2040-3364
Container-title:Nanoscale
language:en
Short-container-title:Nanoscale

Author:

Chen Yuan-I¹,Chang Yin-Jui¹,Sun Yuansheng²,Liao Shih-Chu²,Santacruz Samantha R.¹³⁴,Yeh Hsin-Chih¹⁵^ORCID

Affiliation:

1. Biomedical Engineering, University of Texas at Austin, Austin, TX, USA

2. ISS, Inc., 1602 Newton Drive, Champaign, IL, 61822, USA

3. Electrical & Computer Engineering, University of Texas at Austin, Austin, TX, USA

4. Institute for Neuroscience, University of Texas at Austin, Austin, TX, USA

5. Texas Materials Institute, University of Texas at Austin, Austin, TX, USA

Abstract

In this work, a deep learning-based method, STED-flimGANE, is introduced to achieve enhanced STED imaging resolution under a low STED-beam power and photon-starved conditions.

Funder

National Eye Institute

National Science Foundation

Welch Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2023/NR/D3NR00305A

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