Classification of diffraction patterns using a convolutional neural network in single particle imaging experiments performed at X-ray free-electron lasers

TL;DR

This paper develops convolutional neural networks to classify diffraction patterns in single particle imaging at XFELs, improving the efficiency and control of data analysis in SPI experiments.

Contribution

It introduces CNN architectures tailored for classifying diffraction patterns, integrating them with existing methods to streamline SPI data processing.

Findings

CNN-based selection yields similar reconstruction results to EM selection.

CNN classifiers operate with lower contrast in power spectral density functions.

Streamlining SPI analysis with CNNs enables real-time pattern classification.

Abstract

Single particle imaging (SPI) at X-ray free electron lasers (XFELs) is particularly well suited to determine the 3D structure of particles in their native environment. For a successful reconstruction, diffraction patterns originating from a single hit must be isolated from a large number of acquired patterns. We propose to formulate this task as an image classification problem and solve it using convolutional neural network (CNN) architectures. Two CNN configurations are developed: one that maximises the F1-score and one that emphasises high recall. We also combine the CNNs with expectation maximization (EM) selection as well as size filtering. We observed that our CNN selections have lower contrast in power spectral density functions relative to the EM selection, used in our previous work. However, the reconstruction of our CNN-based selections gives similar results. Introducing CNNs into SPI experiments allows streamlining the reconstruction pipeline, enables researchers to classify patterns on the fly, and, as a consequence, enables them to tightly control the duration of their experiments. We think that bringing non-standard artificial intelligence (AI) based solutions in a well-described SPI analysis workflow may be beneficial for the future development of the SPI experiments.