Nature Communications (Dec 2022)

Machine learning assisted interferometric structured illumination microscopy for dynamic biological imaging

  • 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. Kaminski

DOI
https://doi.org/10.1038/s41467-022-35307-0
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 10

Abstract

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Structured Illumination Microscopy allows for the visualization of biological structures at resolutions below the diffraction limit, but this imaging modality is still hampered by high experimental complexity. Here, the authors present a combination of interferometry and machine learning to construct a structured illumination microscope for super resolution imaging of dynamic sub-cellular biological structures in multiple colors.