Augmenting x-ray single particle imaging reconstruction with self-supervised machine learning
This work addresses the problem of improving structural reconstruction for biological particles in XFEL-based imaging, representing a paradigm shift in the field.
The authors tackled the challenge of reconstructing real-space structures from X-ray diffraction data in single particle imaging, which is hindered by missing phase and orientation information and weak signals, by developing a self-supervised machine learning approach that recovers orientations and intensities, resulting in significantly enhanced reconstruction capabilities compared to conventional algorithms.
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 natural 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 the number of photons per pulse. 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 only. Our method demonstrates great robustness under demanding experimental conditions with significantly enhanced reconstruction capabilities compared with conventional algorithms, and signifies a paradigm shift in SPI as currently practiced at XFELs.