Real-time sparse-sampled Ptychographic imaging through deep neural networks

TL;DR

This paper introduces PtychoNN, a deep neural network approach that enables real-time ptychographic imaging by significantly speeding up reconstruction and relaxing data sampling constraints, thus expanding imaging possibilities.

Contribution

The paper presents a novel deep learning method for ptychography that achieves real-time reconstruction and improves upon traditional iterative algorithms in speed and data efficiency.

Findings

PtychoNN produces high-quality reconstructions from experimental data.

It is up to hundreds of times faster than existing methods.

The approach relaxes data sampling constraints, enabling new imaging scenarios.

Abstract

Ptychography has rapidly grown in the fields of X-ray and electron imaging for its unprecedented ability to achieve nano or atomic scale resolution while simultaneously retrieving chemical or magnetic information from a sample. A ptychographic reconstruction is achieved by means of solving a complex inverse problem that imposes constraints both on the acquisition and on the analysis of the data, which typically precludes real-time imaging due to computational cost involved in solving this inverse problem. In this work we propose PtychoNN, a novel approach to solve the ptychography reconstruction problem based on deep convolutional neural networks. We demonstrate how the proposed method can be used to predict real-space structure and phase at each scan point solely from the corresponding far-field diffraction data. The presented results demonstrate how PtychoNN can effectively be used on experimental data, being able to generate high quality reconstructions of a sample up to hundreds of times faster than state-of-the-art ptychography reconstruction solutions once trained. By surpassing the typical constraints of iterative model-based methods, we can significantly relax the data acquisition sampling conditions and produce equally satisfactory reconstructions. Besides drastically accelerating acquisition and analysis, this capability can enable new imaging scenarios that were not possible before, in cases of dose sensitive, dynamic and extremely voluminous samples.