Developing an End-to-End 3D X-Ray Ptychography Workflow Using Surrogate Models

Recently, X-ray ptychography has attracted significant attention as a non-destructive imaging technique with high spatial resolution. However, its application to real-time imaging is limited by the long execution time required for iterative phase retrieval, which reconstructs sample images from diffraction patterns. To address this issue, deep learning-based surrogate models have been proposed to accelerate iterative phase retrieval by directly predicting sample images. While these surrogate models achieve significant speed-ups, they typically ignore the time needed for model training and dataset preparation, which can diminish their benefits. Consequently, conventional iterative phase retrieval may outperform surrogate-based approaches in end-to-end performance. This study aims to implement real-time X-ray ptychography using surrogate models that explicitly incorporate model training and dataset preparation into the workflow. Specifically, we propose a method that constructs a sample-specific surrogate model on-the-fly using a small subset of observed diffraction patterns and uses its predictions as initial estimates for iterative phase retrieval. The proposed method is up to 2.72 times faster than conventional iterative phase retrieval, even when including training and dataset preparation times. Moreover, the proposed method ensures that the reconstructed images satisfy physical constraints. Comprehensive performance evaluations further demonstrate that the trade-off between model accuracy and preparation time is critical for optimizing the total execution time in the X-ray ptychography workflow.