Communications Chemistry (Dec 2022)

Deep learning-enabled Inference of 3D molecular absorption distribution of biological cells from IR spectra

  • Eirik Almklov Magnussen,
  • Boris Zimmermann,
  • Uladzislau Blazhko,
  • Simona Dzurendova,
  • Benjamin Dupuy–Galet,
  • Dana Byrtusova,
  • Florian Muthreich,
  • Valeria Tafintseva,
  • Kristian Hovde Liland,
  • Kristin Tøndel,
  • Volha Shapaval,
  • Achim Kohler

DOI
https://doi.org/10.1038/s42004-022-00792-3
Journal volume & issue
Vol. 5, no. 1
pp. 1 – 10

Abstract

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Infrared spectroscopy-based diffraction tomography has a high potential to be used for 3D reconstruction of intact samples, however, the inverse problem is highly non-linear and remains challenging. Here, the authors solve full-wave inverse scattering problems using deep convolutional neural networks and perform 3D spectral reconstruction by diffraction tomography from scatter-distorted IR spectra.