Structured illumination microscopy based on principal component analysis

Jiaming Qian; Yu Cao; Ying Bi; Hongjun Wu; Yongtao Liu; Qian Chen; Chao Zuo

doi:10.1186/s43593-022-00035-x

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Structured illumination microscopy based on principal component analysis

eLight Vol. 3, Issue 1, (2023)

RESEARCH ARTICLE | Updated：2024-09-25

- Structured illumination microscopy based on principal component analysis
- Affiliations：
  
  Smart Computational Imaging (SCI) Laboratory (https://scilaboratory.com/), Nanjing University of Science and Technology, No.200 Xiaolingwei Street, Nanjing 210094, Jiangsu, China
  Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
  Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, No. 200 Xiaolingwei Street, Nanjing 210094, Jiangsu, China
- Author bio：
  
  *Qian Chenchenqian@njust.edu.cn
  Chao Zuozuochao@njust.edu.cn
- Funds：
- DOI：10.1186/s43593-022-00035-x
  CLC：
- Published：2023，
  
  Published Online：07 February 2023，
  
  Received：23 August 2022，
  
  Revised：27 September 2022，
  
  Accepted：10 October 2022
- Accepted：
Scan QR Code
Jiaming Qian, Yu Cao, Ying Bi, et al. Structured illumination microscopy based on principal component analysis. [J]. eLight, 2023,3(1).
DOI：

Jiaming Qian, Yu Cao, Ying Bi, et al. Structured illumination microscopy based on principal component analysis. [J]. eLight, 2023,3(1). DOI： 10.1186/s43593-022-00035-x.

摘要

Abstract

Structured illumination microscopy (SIM) is one of the powerful super-resolution modalities in bioscience with the advantages of full-field imaging and high photon efficiency. However

artifact-free super-resolution image reconstruction requires precise knowledge about the illumination parameters. The sample- and environment-dependent on-the-fly experimental parameters need to be retrieved a posteriori from the acquired data

posing a major challenge for real-time

long-term live-cell imaging

where low photobleaching

phototoxicity

and light dose are a must. In this work

we present an efficient and robust SIM algorithm based on principal component analysis (PCA-SIM). PCA-SIM is based on the observation that the ideal phasor matrix of a SIM pattern is of rank one

leading to the low complexity

precise identification of noninteger pixel wave vector and pattern phase while rejecting components that are unrelated to the parameter estimation. We demonstrate that PCA-SIM achieves non-iteratively fast

accurate (below 0.01-pixel wave vector and 0.1% of 2π relative phase under typical noise level)

and robust parameter estimation at low SNRs

which allows real-time super-resolution imaging of live cells in complicated experimental scenarios where other state-of-the-art methods inevitably fail. In particular

we provide the open-source MATLAB toolbox of our PCA-SIM algorithm and associated datasets. The combination of iteration-free reconstruction

robustness to noise

and limited computational complexity makes PCA-SIM a promising method for high-speed

long-term

artifact-free super-resolution imaging of live cells.

关键词

Keywords

Structured illumination microscopy (SIM)Super-resolutionPrincipal component analysis (PCA)

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