Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation

Weisong Zhao; Xiaoshuai Huang; Jianyu Yang; Liying Qu; Guohua Qiu; Yue Zhao; Xinwei Wang; Deer Su; Xumin Ding; Heng Mao; Yaming Jiu; Ying Hu; Jiubin Tan; Shiqun Zhao; Leiting Pan; Liangyi Chen; Haoyu Li

doi:10.1038/s41377-023-01321-0

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Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation

Original Articles

- Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation
- Light: Science & Applications Vol. 12, Issue 12, Pages: 2826-2844(2023)
- Affiliations：
  
  1.Innovation Photonics and Imaging Center, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China
  2.Key Laboratory of Ultra-Precision Intelligent Instrumentation of Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China
  3.Biomedical Engineering Department, International Cancer Institute, Peking University Cancer Hospital and Institute, Health Science Center, Peking University, Beijing, China
  4.The Key Laboratory of Weak-Light Nonlinear Photonics of Education Ministry, School of Physics and TEDA Institute of Applied Physics, Frontiers Science Center for Cell Responses, Nankai University, Tianjin, China
  5.State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, National Biomedical Imaging Center, School of Future Technology, Peking University, Beijing, China
  6.Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China
  7.School of Mathematical Sciences, Peking University, Beijing, China
  8.Unit of Cell Biology and Imaging Study of Pathogen Host Interaction, The Center for Microbes, Development and Health, Key Laboratory of Molecular Virology and Immunology, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, China
  9.School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
  10.PKU-IDG/McGovern Institute for Brain Research, Beijing, China
  11.Beijing Academy of Artificial Intelligence, Beijing, China
  12.Frontiers Science Center for Matter Behave in Space Environment, Harbin Institute of Technology, Harbin, China
  13.Key Laboratory of Micro-Systems and Micro-Structures Manufacturing of Ministry of Education, Harbin Institute of Technology, Harbin, China
- Author bio：
  
  Shiqun Zhao (shiqunzhao@pku.edu.cn)
  Leiting Pan (plt@nankai.edu.cn)
  Liangyi Chen (lychen@pku.edu.cn)
  Haoyu Li (lihaoyu@hit.edu.cn)
- Funds：
- DOI：10.1038/s41377-023-01321-0
  CLC：
- Published：31 December 2023，
  
  Published Online：14 December 2023，
  
  Received：03 June 2023，
  
  Revised：18 October 2023，
  
  Accepted：31 October 2023
- Accepted：
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Zhao, W. S. et al. Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation. Light: Science & Applications, 12, 2826-2844 (2023).
DOI：

Zhao, W. S. et al. Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation. Light: Science & Applications, 12, 2826-2844 (2023). DOI： 10.1038/s41377-023-01321-0.

摘要

Abstract

In fluorescence microscopy

computational algorithms have been developed to suppress noise

enhance contrast

and even enable super-resolution (SR). However

the local quality of the images may vary on multiple scales

and these differences can lead to misconceptions. Current mapping methods fail to finely estimate the local quality

challenging to associate the SR scale content. Here

we develop a rolling Fourier ring correlation (rFRC) method to evaluate the reconstruction uncertainties down to SR scale. To visually pinpoint regions with low reliability

a filtered rFRC is combined with a modified resolution-scaled error map (RSM)

offering a comprehensive and concise map for further examination. We demonstrate their performances on various SR imaging modalities

and the resulting quantitative maps enable better SR images integrated from different reconstructions. Overall

we expect that our framework can become a routinely used tool for biologists in assessing their image datasets in general and inspire further advances in the rapidly developing field of computational imaging.

关键词

Keywords

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