结构光照明显微镜重建算法研究进展

周博; 王昆浩; 陈良怡

doi:10.37188/CO.EN.2022-0011

结构光照明显微镜重建算法研究进展

周博^1,,
王昆浩^2,,
陈良怡^{1, 3, 4, 5, ,}

1.
北京大学未来技术学院分子医学研究所, 北大-清华生命科学联合中心, 膜生物学国家重点实验室, 心脏代谢分子医学北京市重点实验室, 北京 100871
2.
华南师范大学生物光子学院, 激光生命科学教育部重点实验室, 广东广州 510631
3.
北京大学IDG麦戈文脑科学研究所, 北京 100871
4.
北京人工智能研究院, 北京 100871
5.
国家生物医学成像科学中心, 北京 100871

详细信息

中图分类号: O43, TH742, TH744
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出版历程
- 收稿日期: 2022-07-11
- 修回日期: 2022-08-01
- 录用日期: 2022-08-24
- 网络出版日期: 2022-08-24

Recent progress on the reconstruction algorithms of structured illumination microscopy

doi: 10.37188/CO.EN.2022-0011

ZHOU Bo^1
,,
WANG Kun-hao^2
,,
CHEN Liang-yi^{1, 3, 4, 5
, ,}

1.
Insititute of Molecular Medicine, School of Future Technology, Peking University, Center for Life Sciences United by Peking University-TsingHua University, State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Beijing 100871, China
2.
Key Laboratory of Laser Life Science, Ministry of Education, College of Biophotonics, South China Normal University, Guangzhou 510631, China
3.
PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
4.
Beijing Academy of Artificial Intelligence, Beijing 100871, China
5.
National Biomedical Imaging Center, Beijing 100871, China

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

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。
- 结构光照明显微镜 /
- 超分辨率成像
Abstract:
As an early component of modern Super-Resolution (SR) imaging technology, Structured Illumination Microscopy (SIM) has been developed for nearly twenty years. With up to ~60 nm wavelengths and 564 Hz frame rates, it has recently achieved an optimal combination of spatiotemporal resolution in live cells. Despite these advantages, SIM also suffers disadvantages, some of which originated from the intrinsic reconstruction process. Here we review recent technical advances in SIM, including SR reconstruction, performance evaluation, and its integration with other technologies to provide a practical guide for biologists.
- structured illumination microscopy /
- super-resolution imaging

HTML全文

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

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Figure 2. The schematic diagram of TIRF-SIM (a) and instant SIM (b). Adapted from Kner et al.^[35] and York et al.^[40]

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

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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 Xue et al.^[49]

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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	Chu et al. 2014^[14]
Hessian-SIM	Append Hessian regularization to reconstruction	Suppress reconstruction artifacts, avoid over-sharpening boundaries	Open-source	Huang et al. 2018^[15]
HiFi-SIM	Engineering the effective SIM PSF into an ideal form	Suppress reconstruction artifacts, improve axial sectioning	Open-source	Wen et al. 2021^[17]
Sparse-SIM	Append Sparse and Hessian regularization to reconstruction	Increases SIM resolution ~2-fold laterally	Open-source	Zhao et al. 2021^[26]
sCMOS Noise-corrected SIM	Introduce sCMOS imaging noise model to reconstruction	Suppress sCMOS noise-induced reconstruction artifacts	Not open source	Zhou et al. 2022^[16]
Two-step RL deconvolution SIM	Introduce two-step RL deconvolution to reconstruction	eliminate ad hoc tuneable parameters	Not open source	Perez et 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	Smith et al. 2021^[19]
GAN TIRF-SIM	Use GAN for transforming TIRF images into TIRF SIM images	Reconstruct rapidly	Open-source	Wang et 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	Jin et 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	Chen et al. 2021^[23]
DFCAN/DFGAN	Use DFCAN/DFGAN for producing SIM images	Reconstruct with low SNR input images	Open-source	Qiao et al. 2021^[24]

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Table 2. Summary of SIM performance evaluation algorithms

	Function	Code	Reference
FRC/FSC	Determine SIM resolution by cross-correlation	Open-source	Nieuwenhuizen et al. 2013^[28]
Decorrelation analysis	Determine SIM resolution by partial phase correlation	Open-source	Descloux et al. 2019^[31]
NanoJ-SQUIRREL	Evaluate SIM artifacts with the resolution scaling function	Open-source	Culley et al. 2018^[32]
SIMcheck	Evaluate SIM stripe modulation contrast by computing the standard deviation	Open-source	Ball et al. 2015^[33]

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