Isotropic quantitative differential phase contrast imaging techniques: a review

Abstract Optical phase shifts generated by the spatial variation of refractive index and thickness inside the transparent samples can be determined by intensity measurements through quantitative phase contrast imaging. In this review, we focus on isotropic quantitative differential phase contrast microscopy (qDPC), which is a non-interferometric quantitative phase imaging technique that belongs to the class of deterministic phase retrieval from intensity. The qDPC is based on the principle of the weak object transfer function together with the first-order Born approximation in a partially coherent illumination system and wide-field detection, which offers multiple advantages. We review basic principles, imaging systems, and demonstrate examples of DPC imaging for biomedical applications. In addition to the previous work, we present the latest results for isotropic phase contrast enhancements using a deep learning model. We implemented a supervised learning approach with the U-net model to reduce the number of measurements required for multi-axis measurements associated with the isotropic phase transfer function. We show that a well-designed and trained neural network provides a fast and efficient way to predict quantitative phase maps for live cells, which can help in determining morphological parameters of cells for detailed study. The prospects of deep learning in quantitative phase microscopy, particularly for isotropic quantitative phase estimation, are discussed.