Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction

2026-05-01

AI Summary

This work addresses the challenge of slow iterative convergence in ptychographic imaging, which hinders real-time performance in practical experiments. The authors propose a hybrid reconstruction framework that begins with standard iterative pre-conditioning, then introduces a data-driven deep learning forward operator to accelerate convergence, and finally switches back to conventional iterative refinement to enforce physical consistency. The deep operator is trained via supervised learning on a diverse dataset and, for the first time, integrated into the actual reconstruction pipeline of a synchrotron beamline. The method achieves over a two-fold acceleration in negative log-likelihood convergence and reduces runtime by more than 50%, while preserving reconstruction fidelity and physical constraints. It demonstrates strong generalization across experimental datasets collected in different years and has been successfully deployed for real-time imaging at a synchrotron facility.

Abstract

Iterative ptychographic reconstruction algorithms are widely used for coherent diffractive imaging but can exhibit slow convergence under realistic experimental conditions. We propose a machine learning-augmented approach that accelerates iterative ptychographic reconstruction by introducing a learned fast-forward operator applied during reconstruction. Following an initial warm-up using standard iterations, the fast-forward operator advances the reconstruction toward a more converged state, after which conventional iterative updates are resumed. This strategy preserves the physical consistency and flexibility of established ptychographic solvers while reducing the number of iterations required for convergence. The model is trained on diverse ptychographic datasets and evaluated on experimental data acquired in a different year, demonstrating robustness and temporal generalization. Compared with conventional iterative solvers, the machine learning-augmented method achieves comparable reconstruction quality while converging faster in terms of Poisson negative log-likelihood, yielding over a two-fold reduction in wall-clock time. The approach has been integrated into an existing reconstruction pipeline and deployed in production at a synchrotron beamline, demonstrating practicality for real-time experimental operation.

Problem

• iterative ptychographic reconstruction
• slow convergence
• coherent diffractive imaging
• computational acceleration
• real-time operation

Innovation

• machine learning-augmented reconstruction
• iterative ptychography
• fast-forward operator
• temporal generalization
• real-time imaging