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Recent progress on the reconstruction algorithms of structured illumination microscopy

ZHOU Bo,WANG Kun-hao,CHEN Liang-yi

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周博, 王昆浩, 陈良怡. 结构光照明显微镜重建算法研究进展[J]. , 2022, 15(6): 1211-1227. doi: 10.37188/CO.EN.2022-0011
引用本文: 周博, 王昆浩, 陈良怡. 结构光照明显微镜重建算法研究进展[J]. , 2022, 15(6): 1211-1227.doi:10.37188/CO.EN.2022-0011
ZHOU Bo, WANG Kun-hao, CHEN Liang-yi. Recent progress on the reconstruction algorithms of structured illumination microscopy[J]. Chinese Optics, 2022, 15(6): 1211-1227. doi: 10.37188/CO.EN.2022-0011
Citation: ZHOU Bo, WANG Kun-hao, CHEN Liang-yi. Recent progress on the reconstruction algorithms of structured illumination microscopy[J].Chinese Optics, 2022, 15(6): 1211-1227.doi:10.37188/CO.EN.2022-0011

结构光照明显微镜重建算法研究进展

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  • 中图分类号:O43, TH742, TH744

Recent progress on the reconstruction algorithms of structured illumination microscopy

doi:10.37188/CO.EN.2022-0011
Funds:Supported by National Natural Science Foundation of China (No. 81925022, No. 92054301, No. 91750203, No. 31821091); National Key R&D Program of China (No. SQ2016YFJC040028); Beijing Natural Science Foundation (No. Z200017, No. Z201100008420005, No. Z20J00059); National Science and Technology Major Project Programme (No. 2016YFA0500400)
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    Author Bio:

    Zhou Bo (1997—), male, was born in Anqing, Anhui province. He received his Master degree from Peking University in 2020. Currently, he is a Ph.D student in Cell Secretion and Metabolism Laboratory of Institute of Molecular Medicine, Peking University. His research interests are the reconstruction algorithms of super-resolution fluorescence microscopy. E-mail:2001111937@pku.edu.cn

    WANG Kun-hao (1999—), male, was born in Handan, Hebei province. He received his bachelor’s degree from Xinjiang University in 2019. Currently, he is a postgraduate student in South China Normal University. His research interests are the pathogenicity of EGFR family mutations in breast cancer. E-mail:wkh1999@126.com

    Liangyi Chen (1975—), male, was born in wuhan, Hubei province. is Boya Professor of Peking University. He obtained his undergraduate degrees Biomedical engineering in Xi’an JiaoTong University, then majored in Biomedical engineering in pursuing PhD degree in Huazhong University of Science and Technology. His lab focused on two interweaved aspects: the development of new imaging and quantitative image analysis algorithms, and the application of these technology to study how glucose-stimulated insulin secretion is regulated in the health and disease at multiple levels (single cells, islets and in vivo) in the health and disease animal models. The techniques developed included ultrasensitive Hessian structured illumination microscopy (Hessian SIM) for live cell super-resolution imaging, the Sparse deconvolution algorithm for extending spatial resolution of fluorescence microscopes limited by the optics, Super-resolution fluorescence-assisted diffraction computational tomography (SR-FACT) for revealing the three-dimensional landscape of the cellular organelle interactome, two-photon three-axis digital scanned lightsheet microscopy (2P3A-DSLM) for tissue and small organism imaging, and fast High-resolution Miniature Two-photon Microscopy (FHIRM-TPM) for Brain Imaging in Freely-behaving Mice. He is also recipient of the National Distinguish Scholar Fund project from National Natural Science Foundation of China. E-mail:lychen@pku.edu.cn

    Corresponding author:lychen@pku.edu.cn
  • 摘要:

    作为现代超分辨成像技术的早期组成部分,结构照明显微镜(SIM)已经发展了近20年。其近期在活细胞中实现了高达60 nm和564 Hz的最佳时空分辨率组合,但也存在一些源于内在重建过程的缺点。本文综述了SIM技术的最新进展,包括超分辨率(SR)重建算法、性能评估及SIM与其他成像技术的集成,以便为生物学家提供实用指导。

  • Figure 1.Schematic diagram of structured illumination microscopy. (a) In sinusoidal illumination microscopy, interference between multiple beams (usually generated by a diffraction grating or spatial light modulator) creates a 2D or 3D striped pattern with spatial frequency ${k}_{{\rm{ex}}}$ illuminating on the sample. This pattern shifts the sample's spatial frequency spectrum $ \stackrel{~}{S}\left(k\right) $ to $\stackrel{~}{S}\left(k+{k}_{{\rm{ex}}}\right)$ and $\stackrel{~}{S}\left(k-{k}_{{\rm{ex}}}\right)$ , translating high-frequency SR information into the diffraction-limited detection passband ${OTF}_{{\rm{em}}}\left(k\right)$ with the spatial cutoff frequency ${k}_{{\rm{em}}}$ . After computational processing, the sample's highest detectable frequency can be extended to ${k}_{{\rm{ex}}}+{k}_{{\rm{em}}}$ . (b) Spot-scanning illumination microscopy where fluorescence is collected by an array detector, and pixels offset by a distance from the excitation spot detect a shifted but higher-resolution, low-signal confocal image. The reconstruction algorithm corrects the shift and restores the signal by reassigning the detected fluorescence toward the illumination axis, with the final resolution ${PS F}_{{\rm{sys}}}$ determined by the product of the excitation PSF ( ${PS F}_{{\rm{ex}}}$ ) and the emission PSF ( ${PS F}_{{\rm{em}}}$ ). After deconvolution, this process improves resolution similar to that obtained with sinusoidal illumination microscopy

    Figure 2.The schematic diagram of TIRF-SIM (a) and instant SIM (b). Adapted from Kneret al.[35]and Yorket al.[40]

    Figure 3.Schematic diagram of early implemented 2P SIM (a), 2P-ISIM (b), and 2P SIM with the resonant scanner (c). Adapted from Ingaramoet al.[42], Peteret al.[43]and Gregoret al.[44]

    Figure 4.(a) Schematic diagram of 3D STED-SIM. (b) The cross-section comparison of lateral PSF (top, left), axial PSF (bottom, left), lateral OTF (top, right), and axial OTF (bottom, right) of the widefield microscopy (red) and 3D STED-SIM (blue). Adapted from Xueet al.[49]

    Table 1.Comparison of SIM SR reconstruction algorithm

    Principle Effect Code Reference
    TV-SIM Append TV regularization to reconstruction Suppress reconstruction artifacts Not open-source Chuet al. 2014[14]
    Hessian-SIM Append Hessian regularization to reconstruction Suppress reconstruction artifacts, avoid over-sharpening boundaries Open-source Huanget al. 2018[15]
    HiFi-SIM Engineering the effective SIM PSF into an ideal form Suppress reconstruction artifacts, improve axial sectioning Open-source Wenet al. 2021[17]
    Sparse-SIM Append Sparse and Hessian regularization to reconstruction Increases SIM resolution ~2-fold laterally Open-source Zhaoet al. 2021[26]
    sCMOS Noise-corrected SIM Introduce sCMOS imaging noise model to reconstruction Suppress sCMOS noise-induced reconstruction artifacts Not open source Zhouet al. 2022[16]
    Two-step RL deconvolution SIM Introduce two-step RL deconvolution to reconstruction eliminate ad hoc tuneable parameters Not open source Perezet al. 2016[18]
    Noise-controlled SIM Introduce a physically realistic noise model to reconstruction Suppress reconstruction artifacts, eliminate ad hoc tuneable parameters, maintain resolution and contrast Open-source Smithet al. 2021[19]
    GAN TIRF-SIM Use GAN for transforming TIRF images into TIRF SIM images Reconstruct rapidly Open-source Wanget al. 2019[20]
    U-Net SIM Use U-net for producing SIM images Train efficiently and reconstruct with fewer low-intensity input images Open-source Jinet al. 2020[21]
    3D RCAN Use 3D RCAN for increasing SIM resolution Increases SIM resolution ~1.9-fold laterally and ~3.6-fold axially Open-source Chenet al. 2021[23]
    DFCAN/DFGAN Use DFCAN/DFGAN for producing SIM images Reconstruct with low SNR input images Open-source Qiaoet al. 2021[24]
    下载: 导出CSV

    Table 2.Summary of SIM performance evaluation algorithms

    Function Code Reference
    FRC/FSC Determine SIM resolution by cross-correlation Open-source Nieuwenhuizenet al. 2013[28]
    Decorrelation analysis Determine SIM resolution by partial phase correlation Open-source Desclouxet al. 2019[31]
    NanoJ-SQUIRREL Evaluate SIM artifacts with the resolution scaling function Open-source Culleyet al. 2018[32]
    SIMcheck Evaluate SIM stripe modulation contrast by computing the standard deviation Open-source Ballet al. 2015[33]
    下载: 导出CSV
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  • 收稿日期:2022-07-11
  • 录用日期:2022-08-24
  • 修回日期:2022-08-01
  • 网络出版日期:2022-08-24

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