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Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ

Author

Listed:
  • Marcel Müller

    (Biomolecular Photonics, University of Bielefeld)

  • Viola Mönkemöller

    (Biomolecular Photonics, University of Bielefeld)

  • Simon Hennig

    (Biomolecular Photonics, University of Bielefeld)

  • Wolfgang Hübner

    (Biomolecular Photonics, University of Bielefeld)

  • Thomas Huser

    (Biomolecular Photonics, University of Bielefeld
    University of California, Davis)

Abstract

Super-resolved structured illumination microscopy (SR-SIM) is an important tool for fluorescence microscopy. SR-SIM microscopes perform multiple image acquisitions with varying illumination patterns, and reconstruct them to a super-resolved image. In its most frequent, linear implementation, SR-SIM doubles the spatial resolution. The reconstruction is performed numerically on the acquired wide-field image data, and thus relies on a software implementation of specific SR-SIM image reconstruction algorithms. We present fairSIM, an easy-to-use plugin that provides SR-SIM reconstructions for a wide range of SR-SIM platforms directly within ImageJ. For research groups developing their own implementations of super-resolution structured illumination microscopy, fairSIM takes away the hurdle of generating yet another implementation of the reconstruction algorithm. For users of commercial microscopes, it offers an additional, in-depth analysis option for their data independent of specific operating systems. As a modular, open-source solution, fairSIM can easily be adapted, automated and extended as the field of SR-SIM progresses.

Suggested Citation

  • Marcel Müller & Viola Mönkemöller & Simon Hennig & Wolfgang Hübner & Thomas Huser, 2016. "Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ," Nature Communications, Nature, vol. 7(1), pages 1-6, April.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms10980
    DOI: 10.1038/ncomms10980
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    Cited by:

    1. Edward N. Ward & Lisa Hecker & Charles N. Christensen & Jacob R. Lamb & Meng Lu & Luca Mascheroni & Chyi Wei Chung & Anna Wang & Christopher J. Rowlands & Gabriele S. Kaminski Schierle & Clemens F. Ka, 2022. "Machine learning assisted interferometric structured illumination microscopy for dynamic biological imaging," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Yanquan Mo & Kunhao Wang & Liuju Li & Shijia Xing & Shouhua Ye & Jiayuan Wen & Xinxin Duan & Ziying Luo & Wen Gou & Tongsheng Chen & Yu-Hui Zhang & Changliang Guo & Junchao Fan & Liangyi Chen, 2023. "Quantitative structured illumination microscopy via a physical model-based background filtering algorithm reveals actin dynamics," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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