High space-bandwidth in quantitative phase imaging using partially spatially coherent optical coherence microscopy and deep neural network

TL;DR

This paper introduces a high space-bandwidth quantitative phase microscopy system using partially spatially coherent optical coherence microscopy combined with deep neural networks, significantly enhancing resolution and sensitivity for biological imaging.

Contribution

It presents a novel integration of PSC-OCM with a GAN-based deep learning approach to improve phase imaging resolution and space-bandwidth product.

Findings

Achieved 9x improvement in space-bandwidth product.

Demonstrated effective phase reconstruction on RBC and macrophage samples.

Enhanced resolution with low NA lenses using deep learning.

Abstract

Quantitative phase microscopy (QPM) is a label-free technique that enables to monitor morphological changes at subcellular level. The performance of the QPM system in terms of spatial sensitivity and resolution depends on the coherence properties of the light source and the numerical aperture (NA) of objective lenses. Here, we propose high space-bandwidth QPM using partially spatially coherent optical coherence microscopy (PSC-OCM) assisted with deep neural network. The PSC source synthesized to improve the spatial sensitivity of the reconstructed phase map from the interferometric images. Further, compatible generative adversarial network (GAN) is used and trained with paired low-resolution (LR) and high-resolution (HR) datasets acquired from PSC-OCM system. The training of the network is performed on two different types of samples i.e. mostly homogenous human red blood cells (RBC) and on highly heterogenous macrophages. The performance is evaluated by predicting the HR images from the datasets captured with low NA lens and compared with the actual HR phase images. An improvement of 9 times in space-bandwidth product is demonstrated for both RBC and macrophages datasets. We believe that the PSC-OCM+GAN approach would be applicable in single-shot label free tissue imaging, disease classification and other high-resolution tomography applications by utilizing the longitudinal spatial coherence properties of the light source.