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
- mass spectrometry
- high speed
- air pollution
- particulate matter
- electronic health record
- tandem mass spectrometry
- computed tomography
- dual energy
- big data
- magnetic resonance imaging
- protein protein
- health information
- solar cells
- artificial intelligence
- binding protein
- crystal structure
- fluorescence imaging
- neural network
- solid phase extraction
- electron transfer