Quantitative imaging of anisotropic dynamics, such as liquid crystal flows, becomes more and more important nowadays . Therefore, there is great demand to develop high-speed quantitative polarization imaging techniques. We propose a novel single-shot, highly accurate and sensitive quantitative polarization imaging technique, called polarized shearing interference microscopy (PSIM), to explore the dynamical properties of disodium cromoglycate (DSCG) under flow at different flow rates. We determine the retardance and orientation angle of DSCG under flow, and show that their spatial and temporal auto-correlation will reduce when the flow rate increases, which is in agreement with the theoretical results based on dimensional analysis of nematic dynamics .
Optical diffraction tomography (ODT) is a powerful label-free three-dimensional (3D) quantitative imaging technique. However, current ODT modalities require around 50 different illumination angles to reconstruct the 3D refraction index (RI) map, which limits its imaging speed and prohibit it from further applications. Here we propose a deep-learning approach to reduce the number of illumination angles and improve the imaging speed of ODT. With 3D Unet architecture and large training data of different species of cells, we can decrease the number of illumination angles from 49 to 5 with similar reconstruction performance, which empowers ODT the capability to reveal high-speed biological dynamics.