Code: Augmenting x-ray single particle imaging reconstruction with self-supervised machine learning

This is the code repository for the work "Augmenting x-ray single particle imaging reconstruction with self-supervised machine learning."

Summary:

The development of X-ray free-electron lasers (XFELs) has opened numerous opportunities to probe atomic structure and ultrafast dynamics of various materials. Single-particle imaging (SPI) with XFELs enables the investigation of biological particles in their alternative physiological states with unparalleled temporal resolution while circumventing the need for cryogenic conditions or crystallization. However, reconstructing real-space structures from reciprocal-space X-ray diffraction data is highly challenging due to the absence of phase and orientation information, which is further complicated by weak scattering signals and considerable fluctuations in photon numbers of measured scattering signals. In this work, we present an end-to-end, self-supervised machine-learning approach to recover particle orientations and estimate reciprocal-space intensities from diffraction images alone. Through comprehensive benchmarks over simulation data, our method demonstrates great robustness under demanding experimental conditions with enhanced reconstruction capabilities compared with conventional algorithms. We further demonstrate the flexibility and modularity of our method by incorporating symmetry constraints to resolve the structures of highly symmetric particles and applying it to real experimental data. The presented method introduces a novel approach to perform SPI reconstructions with XFELs.