A predicted model-aided reconstruction algorithm for X-ray free-electron laser single-particle imaging.
Zhichao JiaoYao HeXingke FuXin ZhangZhi GengWei DingPublished in: IUCrJ (2024)
Ultra-intense, ultra-fast X-ray free-electron lasers (XFELs) enable the imaging of single protein molecules under ambient temperature and pressure. A crucial aspect of structure reconstruction involves determining the relative orientations of each diffraction pattern and recovering the missing phase information. In this paper, we introduce a predicted model-aided algorithm for orientation determination and phase retrieval, which has been tested on various simulated datasets and has shown significant improvements in the success rate, accuracy and efficiency of XFEL data reconstruction.
Keyphrases
- high resolution
- electron microscopy
- machine learning
- deep learning
- high speed
- air pollution
- mass spectrometry
- particulate matter
- big data
- magnetic resonance imaging
- computed tomography
- tandem mass spectrometry
- rna seq
- dual energy
- health information
- binding protein
- electron transfer
- amino acid
- crystal structure
- single cell
- data analysis