ArticleScientific reports2021
Limited-angle computed tomography with deep image and physics priors.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Who cites it
12 citing papers in PubMed.
- Fibre phantom generation using FibreSimulator: an open-source Python tool.Journal of synchrotron radiation · 2026Article
- Bayesian Regulators to Promote Sharp Image Edges in Limited-Angle Tomography by CombiningJournal of biotechnology and its applications · 2026Article
- Conditioning generative latent optimization for sparse-view computed tomography image reconstruction.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- A novel perspective on denoising using quantum localization with application to medical imaging.ArXiv · 2025Article
- Quantum inspired approach for denoising with application to medical imaging.Research square · 2024Article
- Prior frequency guided diffusion model for limited angle (LA)-CBCT reconstruction.Physics in medicine and biology · 2024Article
- Projection-Angle-Sensor-Assisted X-ray Computed Tomography for Cylindrical Lithium-Ion Batteries.Sensors (Basel, Switzerland) · 2024Article
- Towards full-stack deep learning-empowered data processing pipeline for synchrotron tomography experiments.Innovation (Cambridge (Mass.)) · 2024Review
- tomoCAM: fast model-based iterative reconstruction via GPU acceleration and non-uniform fast Fourier transforms.Journal of synchrotron radiation · 2024Article
- Simulation-Assisted Augmentation of Missing Wedge and Region-of-Interest Computed Tomography Data.Journal of imaging · 2023Article
- Polarization-sensitive intensity diffraction tomography.Light, science & applications · 2023Article
- Self-supervised next view prediction for limited-angle optical projection tomography.Biomedical optics express · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computed tomography is a well-established x-ray imaging technique to reconstruct the three-dimensional structure of objects. It has been used extensively in a variety of fields, from diagnostic imaging to materials and biological sciences. One major challenge in some applications, such as in electron or x-ray tomography systems, is that the projections cannot be gathered over all the angles due to the sample holder setup or shape of the sample. This results in an ill-posed problem called the limited angle reconstruction problem. Typical image reconstruction in this setup leads to distortion and artifacts, thereby hindering a quantitative evaluation of the results. To address this challenge, we use a generative model to effectively constrain the solution of a physics-based approach. Our approach is self-training that can iteratively learn the nonlinear mapping from partial projections to the scanned object. Because our approach combines the data likelihood and image prior terms into a single deep network, it is computationally tractable and improves performance through an end-to-end training. We also complement our approach with total-variation regularization to handle high-frequency noise in reconstructions and implement a solver based on alternating direction method of multipliers. We present numerical results for various degrees of missing angle range and noise levels, which demonstrate the effectiveness of the proposed approach.
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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.