Single-shot multispectral quantitative phase imaging of biological samples using deep learning

Multispectral quantitative phase imaging (MS-QPI) is a high-contrast label-free technique for morphological imaging of the specimens. The aim of the present study is to extract spectral dependent quantitative information in single-shot using a highly spatially sensitive digital holographic microscope assisted by a deep neural network. There are three different wavelengths used in our method: lambda = 532, 633, and 808 nm. The first step is to get the interferometric data for each wavelength. The acquired datasets are used to train a generative adversarial network to generate multispectral (MS) quantitative phase maps from a single input interferogram. The network was trained and validated on two different samples: the optical waveguide and MG63 osteosarcoma cells. Validation of the present approach is performed by comparing the predicted MS phase maps with numerically reconstructed (FT + TIE) phase maps and quantifying with different image quality assessment metrices. (c) 2023 Optica Publishing Group

Automated summary

Multispectral quantitative phase imaging (MS-QPI) is achieved by using a highly spatially sensitive digital holographic microscope assisted by a deep neural network to extract spectral dependent quantitative information in single-shot. Three different wavelengths (532, 633, and 808 nm) are used, and interferometric data is acquired for each wavelength. A generative adversarial network is trained to generate multispectral (MS) quantitative phase maps from a single input interferogram. The validation of the approach is done by comparing the predicted MS phase maps with numerically reconstructed phase maps using different image quality assessment metrics.