ArticleIUCrJ2021
Complex imaging of phase domains by deep neural networks.
Article in IUCrJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed, 53 citations in OpenAlex.
- Deep learning of structural morphology imaged by scanning X-ray diffraction microscopy.Scientific reports · 2025Article
- Prospects for coherent X-ray diffraction imaging at fourth-generation synchrotron sources.IUCrJ · 2025Article
- Imaging in-operando LiCoOCommunications chemistry · 2024Article
- Using convolutional neural network denoising to reduce ambiguity in X-ray coherent diffraction imaging.Journal of synchrotron radiation · 2024Article
- Patching-based deep-learning model for the inpainting of Bragg coherent diffraction patterns affected by detector gaps.Journal of applied crystallography · 2024Article
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Article
- Classification of diffraction patterns using a convolutional neural network in single-particle-imaging experiments performed at X-ray free-electron lasers.Journal of applied crystallography · 2022Article
- Noise reduction and mask removal neural network for X-ray single-particle imaging.Journal of applied crystallography · 2022Article
- Article
- Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The reconstruction of a single-particle image from the modulus of its Fourier transform, by phase-retrieval methods, has been extensively applied in X-ray structural science. Particularly for strong-phase objects, such as the phase domains found inside crystals by Bragg coherent diffraction imaging (BCDI), conventional iteration methods are time consuming and sensitive to their initial guess because of their iterative nature. Here, a deep-neural-network model is presented which gives a fast and accurate estimate of the complex single-particle image in the form of a universal approximator learned from synthetic data. A way to combine the deep-neural-network model with conventional iterative methods is then presented to refine the accuracy of the reconstructed results from the proposed deep-neural-network model. Improved convergence is also demonstrated with experimental BCDI data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.