ReviewMedical physics2018
Regularization strategies in statistical image reconstruction of low-dose x-ray CT: A review.
Review in Medical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Visualization of nonlinearity in image reconstruction using nonlocal means filters for regularization under noisy conditions.Radiological physics and technology · 2026Article
- Nonstop gated cone-beam computed tomography for respiratory gating pancreatic cancer radiotherapy using dual-domain deep learning reconstruction.Physics and imaging in radiation oncology · 2026Article
- Interior soft x-ray tomography with sparse global sampling.Physica scripta · 2025Article
- Comparison of iterative reconstruction implementations for multislice helical CT.Zeitschrift fur medizinische Physik · 2025Article
- When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with Sparse-view CT.ArXiv · 2025Article
- A low-dose CT reconstruction method using sub-pixel anisotropic diffusion.Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Machine Learned Texture Prior From Full-Dose CT Database via Multi-Modality Feature Selection for Bayesian Reconstruction of Low-Dose CT.IEEE transactions on medical imaging · 2023Article
- ATNet: A Defect Detection Framework for X-ray Images of DIP Chip Lead Bonding.Micromachines · 2023Article
- Artificial Intelligence in Radiation Therapy.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging.Frontiers in cardiovascular medicine · 2022Article
- Self-Bayesian aberration removal via constraints for ultracold atom microscopy.Physical review research · 2021Article
- Core Imaging Library - Part I: a versatile Python framework for tomographic imaging.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- Prior-image-based CT reconstruction using attenuation-mismatched priors.Physics in medicine and biology · 2021Article
- [Artificial intelligence in image evaluation and diagnosis].Monatsschrift Kinderheilkunde : Organ der Deutschen Gesellschaft fur Kinderheilkunde · 2021Review
- Characterization of tissue-specific pre-log Bayesian CT reconstruction by texture-dose relationship.Medical physics · 2020Article
- Scalable Double Regularization for 3D Nano-CT Reconstruction.Journal of petroleum science & engineering · 2020Article
- Spatial Distribution of Noise Reduction in Four Iterative Reconstruction Algorithms in CT-A Technical Evaluation.Diagnostics (Basel, Switzerland) · 2020Article
- Improving Low-Dose Pediatric Abdominal CT by Using Convolutional Neural Networks.Radiology. Artificial intelligence · 2019Article
- Blind deconvolution in model-based iterative reconstruction for CT using a normalized sparsity measure.Physics in medicine and biology · 2019Article
- Statistical CT reconstruction using region-aware texture preserving regularization learning from prior normal-dose CT image.Physics in medicine and biology · 2018Article
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5 authors.
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Abstract
Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose x-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method. According to the maximum a posteriori (MAP) estimation, the SIR methods are typically formulated by an objective function consisting of two terms: (a) a data-fidelity term that models imaging geometry and physical detection processes in projection data acquisition, and (b) a regularization term that reflects prior knowledge or expectations of the characteristics of the to-be-reconstructed image. SIR desires accurate system modeling of data acquisition, while the regularization term also has a strong influence on the quality of reconstructed images. A variety of regularization strategies have been proposed for SIR in the past decades, based on different assumptions, models, and prior knowledge. In this paper, we review the conceptual and mathematical bases of these regularization strategies and briefly illustrate their efficacies in SIR of low-dose CT.
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