Three-dimensional Coherent X-ray Diffraction Imaging via Deep Convolutional Neural Networks

TL;DR

This paper introduces a deep learning approach for 3D coherent X-ray diffraction imaging that enhances phase retrieval accuracy and speed, enabling real-time analysis and overcoming traditional noise limitations.

Contribution

It presents a novel 3D machine learning model that improves phase retrieval in CDI, capable of rapid predictions and refinement without prior training.

Findings

Significantly improved phase retrieval accuracy over traditional methods

Enables real-time data analysis in CDI experiments

Effective even without prior training, using loss minimization

Abstract

As a critical component of coherent X-ray diffraction imaging (CDI), phase retrieval has been extensively applied in X-ray structural science to recover the 3D morphological information inside measured particles. Despite meeting all the oversampling requirements of Sayre and Shannon, current phase retrieval approaches still have trouble achieving a unique inversion of experimental data in the presence of noise. Here, we propose to overcome this limitation by incorporating a 3D Machine Learning (ML) model combining (optional) supervised learning with transfer learning. The trained ML model can rapidly provide an immediate result with high accuracy which could benefit real-time experiments, and the predicted result can be further refined with transfer learning. More significantly, the proposed ML model can be used without any prior training to learn the missing phases of an image based on minimization of an appropriate 'loss function' alone. We demonstrate significantly improved performance with experimental Bragg CDI data over traditional iterative phase retrieval algorithms.