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Cited by 574

The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.

Super-Resolution Structured Illumination Microscopy

Open access
Heintzmann, Rainer, Huser, Thomas·2017·Chemical Reviews

Abstract

Super-resolved structured illumination microscopy (SR-SIM) is among the most rapidly growing fluorescence microscopy techniques that can surpass the optical diffraction limit. The strength of SR-SIM is that it can be readily applied to samples prepared for conventional fluorescence microscopy, requiring no sophisticated sample preparation protocols. As an extension of wide-field fluorescence microscopy, it is inherently capable of multicolor imaging and optical sectioning and, with sufficiently fast implementations, permits live cell imaging. Image reconstruction, however, currently relies on sophisticated computational procedures, susceptible to reconstruction artifacts, requiring trained users to recognize and avoid them. Here, we review the latest developments in SR-SIM research. Starting from a historical overview of the development of SR-SIM, we review how this method can be implemented in various experimental schemes, we provide an overview of the important parameters involved in successful image reconstruction, we summarize recent biological applications, and we provide a brief outlook of the directions in which we believe SR-SIM is headed in the future.

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Crossref · 10.1021/acs.chemrev.7b00218

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Metadata

Authors
Heintzmann, R., & Huser, T.
Venue
Chemical Reviews
Published
2017-11-10
Volume
117
Issue
23
Pages
13890-13908
ISSN
  • 0009-2665 (ISSN-L)
  • 1520-6890
OpenAlex
W2767575105
Cited by
574
References
67
FWCI
2.15
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Super-Resolution Structured Illumination Microscopy. https://app.cochat.ai/papers/10.1021/acs.chemrev.7b00218
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Heintzmann, R., & Huser, T. (2017). Super-Resolution Structured Illumination Microscopy. Chemical Reviews. https://doi.org/10.1021/acs.chemrev.7b00218

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.