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Zhang C, Zhang L, Zhang R, Chen M, Wei S. Robust 3D phase retrieval via compressed support detection from snapshot diffraction pattern. Comput Biol Med 2024; 177:108644. [PMID: 38810474 DOI: 10.1016/j.compbiomed.2024.108644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/22/2024] [Accepted: 05/21/2024] [Indexed: 05/31/2024]
Abstract
Traditional multislice iterative phase retrieval (MIPR) from snapshot two-dimensional measurements suffers from the two limitations of pre-defined support and iterative stagnation. To eliminate the requirements for priori knowledge of support masks, this paper proposes a multislice iterative phase retrieval algorithm based on compressed support detection and hybrid input-output algorithm (CSD-MIPR-HIO). The CSD-MIPR-HIO algorithm firstly uses compressed support detection to adaptively detect the support masks of each plane from single 2D diffraction intensity, and then uses a hybrid input-output (HIO) iterative algorithm for MIPR. The proposed method breaks the limitations of traditional MIPR algorithms on priori knowledge of support masks and achieve high-quality reconstruction in noisy environments. Numerical and optical experiments confirm the feasibility, superiority, and robustness of our proposed CSD-MIPR-HIO method.
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Affiliation(s)
- Cheng Zhang
- Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, Anhui Province, 230601, China; Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China; School of Integrated Circuits, Anhui University, Hefei, Anhui Province, 230601, China; Anhui Provincial High-performance Integrated Circuit Engineering Research Center, Anhui University, Hefei, Anhui Province, 230601, China
| | - Liru Zhang
- Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, Anhui Province, 230601, China.
| | - Ru Zhang
- Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, Anhui Province, 230601, China
| | - Mingsheng Chen
- Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, Anhui Province, 230601, China
| | - Sui Wei
- Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, Anhui Province, 230601, China
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Bellisario A, Maia FRNC, Ekeberg T. Noise reduction and mask removal neural network for X-ray single-particle imaging. J Appl Crystallogr 2022; 55:122-132. [PMID: 35145358 PMCID: PMC8805166 DOI: 10.1107/s1600576721012371] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 11/22/2021] [Indexed: 12/03/2022] Open
Abstract
Free-electron lasers could enable X-ray imaging of single biological macromolecules and the study of protein dynamics, paving the way for a powerful new imaging tool in structural biology, but a low signal-to-noise ratio and missing regions in the detectors, colloquially termed 'masks', affect data collection and hamper real-time evaluation of experimental data. In this article, the challenges posed by noise and masks are tackled by introducing a neural network pipeline that aims to restore diffraction intensities. For training and testing of the model, a data set of diffraction patterns was simulated from 10 900 different proteins with molecular weights within the range of 10-100 kDa and collected at a photon energy of 8 keV. The method is compared with a simple low-pass filtering algorithm based on autocorrelation constraints. The results show an improvement in the mean-squared error of roughly two orders of magnitude in the presence of masks compared with the noisy data. The algorithm was also tested at increasing mask width, leading to the conclusion that demasking can achieve good results when the mask is smaller than half of the central speckle of the pattern. The results highlight the competitiveness of this model for data processing and the feasibility of restoring diffraction intensities from unknown structures in real time using deep learning methods. Finally, an example is shown of this preprocessing making orientation recovery more reliable, especially for data sets containing very few patterns, using the expansion-maximization-compression algorithm.
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Affiliation(s)
- Alfredo Bellisario
- Laboratory of Molecular Biophysics, Department of Cell and Molecular Biology, Uppsala University, Husargatan 3 (Box 596), SE-751 24 Uppsala, Sweden
| | - Filipe R. N. C. Maia
- Laboratory of Molecular Biophysics, Department of Cell and Molecular Biology, Uppsala University, Husargatan 3 (Box 596), SE-751 24 Uppsala, Sweden
| | - Tomas Ekeberg
- Laboratory of Molecular Biophysics, Department of Cell and Molecular Biology, Uppsala University, Husargatan 3 (Box 596), SE-751 24 Uppsala, Sweden
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Liu J, Engblom S, Nettelblad C. Flash X-ray diffraction imaging in 3D: a proposed analysis pipeline. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2020; 37:1673-1686. [PMID: 33104615 DOI: 10.1364/josaa.390384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 07/29/2020] [Indexed: 06/11/2023]
Abstract
Modern Flash X-ray diffraction Imaging (FXI) acquires diffraction signals from single biomolecules at a high repetition rate from X-ray Free Electron Lasers (XFELs), easily obtaining millions of 2D diffraction patterns from a single experiment. Due to the stochastic nature of FXI experiments and the massive volumes of data, retrieving 3D electron densities from raw 2D diffraction patterns is a challenging and time-consuming task. We propose a semi-automatic data analysis pipeline for FXI experiments, which includes four steps: hit-finding and preliminary filtering, pattern classification, 3D Fourier reconstruction, and post-analysis. We also include a recently developed bootstrap methodology in the post-analysis step for uncertainty analysis and quality control. To achieve the best possible resolution, we further suggest using background subtraction, signal windowing, and convex optimization techniques when retrieving the Fourier phases in the post-analysis step. As an application example, we quantified the 3D electron structure of the PR772 virus using the proposed data analysis pipeline. The retrieved structure was above the detector edge resolution and clearly showed the pseudo-icosahedral capsid of the PR772.
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