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- Title
Deep learning-enabled Inference of 3D molecular absorption distribution of biological cells from IR spectra.
- Authors
Magnussen, Eirik Almklov; Zimmermann, Boris; Blazhko, Uladzislau; Dzurendova, Simona; Dupuy–Galet, Benjamin; Byrtusova, Dana; Muthreich, Florian; Tafintseva, Valeria; Liland, Kristian Hovde; Tøndel, Kristin; Shapaval, Volha; Kohler, Achim
- Abstract
Infrared spectroscopy delivers abundant information about the chemical composition, as well as the structural and optical properties of intact samples in a non-destructive manner. We present a deep convolutional neural network which exploits all of this information and solves full-wave inverse scattering problems and thereby obtains the 3D optical, structural and chemical properties from infrared spectroscopic measurements of intact micro-samples. The proposed model encodes scatter-distorted infrared spectra and infers the distribution of the complex refractive index function of concentrically spherical samples, such as many biological cells. The approach delivers simultaneously the molecular absorption, sample morphology and effective refractive index in both the cell wall and interior from a single measured spectrum. The model is trained on simulated scatter-distorted spectra, where absorption in the distinct layers is simulated and the scatter-distorted spectra are estimated by analytic solutions of Maxwell's equations for samples of different sizes. This allows for essentially real-time deep learning-enabled infrared diffraction micro-tomography, for a large subset of biological cells. 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.
- Subjects
DEEP learning; CONVOLUTIONAL neural networks; MAXWELL equations; SPHERICAL functions; INVERSE problems; REFRACTIVE index
- Publication
Communications Chemistry, 2022, Vol 5, Issue 1, p1
- ISSN
2399-3669
- Publication type
Article
- DOI
10.1038/s42004-022-00792-3