ArticleJournal of synchrotron radiation2020
Tomographic reconstruction with a generative adversarial network.
Article in Journal of synchrotron radiation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Super Time-Resolved Tomography.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Prior frequency guided diffusion model for limited angle (LA)-CBCT reconstruction.Physics in medicine and biology · 2024Article
- Towards full-stack deep learning-empowered data processing pipeline for synchrotron tomography experiments.Innovation (Cambridge (Mass.)) · 2024Review
- Recent developments in X-ray diffraction/scattering computed tomography for materials science.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- Deep learning enables nanoscale X-ray 3D imaging with limited data.Light, science & applications · 2023Article
- Artificial Intelligence Applied to Battery Research: Hype or Reality?Chemical reviews · 2022Review
- 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
- Adversarial Resolution Enhancement for Electrical Capacitance Tomography Image Reconstruction.Sensors (Basel, Switzerland) · 2022Article
- Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement: An overview from a signal processing perspective.IEEE signal processing magazine · 2022Article
- Foam-like phantoms for comparing tomography algorithms.Journal of synchrotron radiation · 2022Article
- Limited-angle computed tomography with deep image and physics priors.Scientific reports · 2021Article
- PtyNAMi: ptychographic nano-analytical microscope.Journal of applied crystallography · 2020Article
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Authors and funding
9 authors.
Funding
Abstract
This paper presents a deep learning algorithm for tomographic reconstruction (GANrec). The algorithm uses a generative adversarial network (GAN) to solve the inverse of the Radon transform directly. It works for independent sinograms without additional training steps. The GAN has been developed to fit the input sinogram with the model sinogram generated from the predicted reconstruction. Good quality reconstructions can be obtained during the minimization of the fitting errors. The reconstruction is a self-training procedure based on the physics model, instead of on training data. The algorithm showed significant improvements in the reconstruction accuracy, especially for missing-wedge tomography acquired at less than 180° rotational range. It was also validated by reconstructing a missing-wedge X-ray ptychographic tomography (PXCT) data set of a macroporous zeolite particle, for which only 51 projections over 70° could be collected. The GANrec recovered the 3D pore structure with reasonable quality for further analysis. This reconstruction concept can work universally for most of the ill-posed inverse problems if the forward model is well defined, such as phase retrieval of in-line phase-contrast imaging.
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