Single particle imaging of biological specimens

Abstract

Single-particle imaging (SPI) at an X-ray free electron laser (XFEL) is demonstrating its potential to support the imaging and structure determination of biological specimens at atomic resolution without the need for crystallization. According to a principle of diffraction before damage, diffraction patterns of specimens with random orientations injected to the focus of ultra-short and extremely bright XFEL can be recorded before the specimens are damaged. For a successful image reconstruction, robust algorithms are needed. So far, fast and robust reconstruction algorithms from noisy and incomplete diffraction patterns have been challenges for XFEL SPI. Here we briefly outline the workflow of SPI and discuss key challenges and corresponding approaches to both phase retrieval and orientations determination. We also give an outlook of the promising algorithms for SPI by means of machine learning or deep learning and believe that the current computational challenges of XFEL SPI can be handled by utilizing techniques of modern data processing and artificial intelligence for imaging at atomic resolution and femtosecond temporal resolution in the future.