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Advanced Photonics Nexus
Vol. 2, Issue 1, 016012 (2023)

Hybrid reconstruction of the physical model with the deep learning that improves structured illumination microscopy

Jianyong Wang^1、2、†, Junchao Fan³, Bo Zhou¹, Xiaoshuai Huang^4、5、*, and Liangyi Chen^{1、6、7、8、*}

Author Affiliations

¹Peking University, Institute of Molecular Medicine, College of Future Technology, Center for Life Sciences, State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Beijing, China

²Peking University, School of Software and Microelectronics, Beijing, China

³Chongqing University of Posts and Telecommunications, College of Computer Science and Technology, Chongqing Key Laboratory of Image Cognition, Chongqing, China

⁴Peking University, Biomedical Engineering Department, Beijing, China

⁵Peking University, International Cancer Institute, Beijing, China

⁶PKU-IDG/McGovern Institute for Brain Research, Beijing, China

⁷Beijing Academy of Artificial Intelligence, Beijing, China

⁸National Biomedical Imaging Center, Beijing, China

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DOI: 10.1117/1.APN.2.1.016012 Cite this Article Set citation alerts

Jianyong Wang, Junchao Fan, Bo Zhou, Xiaoshuai Huang, Liangyi Chen. Hybrid reconstruction of the physical model with the deep learning that improves structured illumination microscopy[J]. Advanced Photonics Nexus, 2023, 2(1): 016012 Copy Citation Text

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References

[1] S. W. Hell. Far-field optical nanoscopy. Science, 316, 1153-1158(2007).

[2] S. T. Hess et al. Dynamic clustered distribution of hemagglutinin resolved at 40 nm in living cell membranes discriminates between raft theories. Proc. Natl. Acad. Sci. U. S. A., 104, 17370-17375(2007).

[3] B. Huang, M. Bates, X. Zhuang. Super-resolution fluorescence microscopy. Annu. Rev. Biochem., 78, 993-1016(2009).

[4] L. Schermelleh, R. Heintzmann, H. Leonhardt. A guide to super-resolution fluorescence microscopy. J. Cell Biol., 190, 165-175(2010).

[5] P. Sengupta, S. Van Engelenburg, J. Lippincott-Schwartz. Visualizing cell structure and function with point-localization superresolution imaging. Dev. Cell, 23, 1092-1102(2012).

[6] J. Nixon-Abell et al. Increased spatiotemporal resolution reveals highly dynamic dense tubular matrices in the peripheral ER. Science, 354, aaf3928(2016).

[7] W. Shin et al. Visualization of membrane pore in live cells reveals a dynamic-pore theory governing fusion and endocytosis. Cell, 173, 934-945.e12(2018).

[8] P. P. Laissue et al. Assessing phototoxicity in live fluorescence imaging. Nat. Methods, 14, 657-661(2017).

[9] D. Li et al. ADVANCED IMAGING. Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics. Science, 349, aab3500(2015).

[10] M. G. Gustafsson. Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy. J. Microsc., 198, 82-87(2000).

[11] M. G. Gustafsson et al. Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination. Biophys. J., 94, 4957-4970(2008).

[12] G. E. Cragg, P. T. So. Lateral resolution enhancement with standing evanescent waves. Opt. Lett., 25, 46-48(2000).

[13] P. Kner et al. Super-resolution video microscopy of live cells by structured illumination. Nat. Methods, 6, 339-342(2009).

[14] L. M. Hirvonen et al. Structured illumination microscopy of a living cell. Eur. Biophys. J., 38, 807-812(2009).

[15] Y. Guo et al. Visualizing intracellular organelle and cytoskeletal interactions at nanoscale resolution on millisecond timescales. Cell, 175, 1430-1442.e17(2018).

[16] A. Lal, C. Shan, P. Xi. Structured illumination microscopy image reconstruction algorithm. IEEE J. Sel. Top. Quantum Electron., 22, 50-63(2016).

[17] M. Müller et al. Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ. Nat. Commun., 7, 10980(2016).

[18] X. Huang et al. Fast, long-term, super-resolution imaging with Hessian structured illumination microscopy. Nat. Biotechnol., 36, 451-459(2018).

[19] K. Chu et al. Image reconstruction for structured-illumination microscopy with low signal level. Opt. Express, 22, 8687-8702(2014).

[20] G. Wen et al. High-fidelity structured illumination microscopy by point-spread-function engineering. Light Sci. Appl., 10, 70(2021).

[21] W. Zhaojun et al. High-speed image reconstruction for optically sectioned, super-resolution structured illumination microscopy. Adv. Photonics, 4, 026003(2022).

[22] Y. Mo et al. Structured illumination microscopy artefacts caused by illumination scattering. Philos. Trans. A Math. Phys. Eng. Sci., 379, 20200153(2021).

[23] K. Hornik, M. Stinchcombe, H. White. Multilayer feedforward networks are universal approximators. Neural Netw., 2, 359-366(1989).

[24] L. Jin et al. Deep learning enables structured illumination microscopy with low light levels and enhanced speed. Nat. Commun., 11, 1934(2020).

[25] C. Qiao et al. Evaluation and development of deep neural networks for image super-resolution in optical microscopy. Nat. Methods, 18, 194-202(2021).

[26] C. N. Christensen et al. ML-SIM: universal reconstruction of structured illumination microscopy images using transfer learning. Biomed. Opt. Express, 12, 2720-2733(2021).

[27] S. Bhadra et al. On hallucinations in tomographic image reconstruction. IEEE Trans. Med. Imaging, 40, 3249-3260(2021).

[28] E. Kobler et al. Total deep variation for linear inverse problems, 7546-7555(2020).

[29] C. Qiao et al. Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes. Nat. Biotechnol., 2022, 1-11(2022).

[30] S. Jakobs, C. A. Wurm. Super-resolution microscopy of mitochondria. Curr. Opin. Chem. Biol., 20, 9-15(2014).

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Jianyong Wang, Junchao Fan, Bo Zhou, Xiaoshuai Huang, Liangyi Chen. Hybrid reconstruction of the physical model with the deep learning that improves structured illumination microscopy[J]. Advanced Photonics Nexus, 2023, 2(1): 016012

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