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

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 fr

SUBMITTER: Magnussen EA 

PROVIDER: S-EPMC9814771 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature