Utilizing the U-net architecture to achieve isotropic quantitative differential phase contrast imaging

Abstract

Quantitative differential phase contrast (qDPC) imaging is a specific technique for observing the transparent object. qDPC method adopts the structured-light illumination to provide the quantitative phase reconstruction, and it has lesser hardware requirement compared with other quantitative phase imaging (QPI) method. Conventionally, to achieve isotropic phase retrieval with better uniformity by utilizing qDPC system, it needs multiple measurements with different asymmetric illumination pattern along the transverse direction. However, it takes much more time to reconstruct the phase distribution while increasing the number of measurements. Therefore, here we applied the deep neural network (DNN) model for approaching isotropic phase retrieval and minimizing the acquisition time simultaneously. To achieve the isotropic phase distribution with less measurements, the U-net architecture was adopted in this study. The U-net model was utilized for converting the result from 1-axis qDPC method (the phase retrieval which has lesser measurements) to the result from 12-axis qDPC method (the phase retrieval which has more measurements to cover all the spatial frequency information in the spatial-frequency domain). For the model training stage, we prepared 5 different types of cells to provide sufficient training datasets. To evaluate the performance of our trained model, we prepared another 2 distinct types of cells referred as testing dataset. The results showed our model can recover the insufficient phase value in the sample. The morphology of cells can be analysis after applying our proposed DNN model.