Single capture quantitative phase imaging with tomographic sectioning

Abstract

Quantitative Phase Imaging (QPI) has become a mainstay imaging technique in the biomedical sciences to study cells and other biological processes. Traditional QPI techniques are transmission-based and, thus, limited to thin samples. Over the past few years, multiple 3D QPI tools have emerged attempting to overcome this limitation and provide cross-sectional phase information of thicker samples. However, most of these techniques remain transmission-based, which constrains their ability to image samples thicker than a few mean free scattering lengths. Recently, we have developed quantitative oblique back-illumination microscopy (qOBM) as an epimode technique that enables label-free quantitative phase imaging of thick samples with tomographic crosssectioning. Like in most 3D QPI instances, qOBM requires multiple captures to render a quantitative phase image. Specifically, qOBM requires four raw captures, obtained by illuminating the sample obliquely from four different directions, to reconstruct the quantitative phase. This muti-capture scheme hinders qOBM’s ability to investigate valuable fast dynamic processes, such as blood flow, as well as its usability in some in-vivo applications. Here, we present a deep-learning enabled single-capture version of qOBM that quadruples the system’s imaging speed and prevents motion artifacts. To this end, we have trained a U-Net GAN to learn the qOBM reconstruction from a single capture obtained with oblique illumination. We show the capabilities and limitations of this approach, as well as some of the novel applications that this system enables, such as in-vivo high-resolution non-invasive blood flow quantitative phase imaging.