ArticlePhysics in medicine and biology2014
Sparse-view x-ray CT reconstruction via total generalized variation regularization.
Article in Physics in medicine and biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed.
- Accelerated Physics-guided Diffusion Model for 3D Limited-angle Reconstruction of Cardiac Computed Tomography.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Tomographic Foundation Model-FORCE: Flow-Oriented Reconstruction Conditioning Engine.IEEE transactions on medical imaging · 2026Article
- Sparse-view reconstruction and noise reduction in 99m Tc-prostate specific membrane antigen prostate imaging.Nuclear medicine communications · 2026Article
- Physics-Informed Score-Based Diffusion Model for Limited-Angle Reconstruction of Cardiac Computed Tomography.IEEE transactions on medical imaging · 2025Article
- Task based evaluation of sparse view CT reconstruction techniques for intracranial hemorrhage diagnosis using an AI observer model.Scientific reports · 2025Article
- Low-dose computed tomography image denoising using pixel level non-local self-similarity prior with non-local means for healthcare informatics.Scientific reports · 2025Article
- Sparse-view spectral CT reconstruction via a coupled subspace representation and score-based generative model.Quantitative imaging in medicine and surgery · 2025Article
- LoMAE: Simple Streamlined Low-Level Masked Autoencoders for Robust, Generalized, and Interpretable Low-Dose CT Denoising.IEEE journal of biomedical and health informatics · 2024Article
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- [Low-dose CT reconstruction based on high-dimensional partial differential equation projection recovery].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Reducing image artifacts in sparse projection CT using conditional generative adversarial networks.Scientific reports · 2024Article
- Fourier Diffusion for Sparse CT Reconstruction.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- DuDoSS: Deep-learning-based dual-domain sinogram synthesis from sparsely sampled projections of cardiac SPECT.Medical physics · 2023Article
- Low-Dose CT Image Post-Processing Based on Learn-Type Sparse Transform.Sensors (Basel, Switzerland) · 2022Article
- Cone-beam breast CT using an offset detector: effect of detector offset and image reconstruction algorithm.Physics in medicine and biology · 2022Article
- A Limited-View CT Reconstruction Framework Based on Hybrid Domains and Spatial Correlation.Sensors (Basel, Switzerland) · 2022Article
- Sparse Angle CBCT Reconstruction Based on Guided Image Filtering.Frontiers in oncology · 2022Article
- A signal detection model for quantifying overregularization in nonlinear image reconstruction.Medical physics · 2021Article
- Sinogram + image domain neural network approach for metal artifact reduction in low-dose cone-beam computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2021Article
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8 authors.
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Abstract
Sparse-view CT reconstruction algorithms via total variation (TV) optimize the data iteratively on the basis of a noise- and artifact-reducing model, resulting in significant radiation dose reduction while maintaining image quality. However, the piecewise constant assumption of TV minimization often leads to the appearance of noticeable patchy artifacts in reconstructed images. To obviate this drawback, we present a penalized weighted least-squares (PWLS) scheme to retain the image quality by incorporating the new concept of total generalized variation (TGV) regularization. We refer to the proposed scheme as 'PWLS-TGV' for simplicity. Specifically, TGV regularization utilizes higher order derivatives of the objective image, and the weighted least-squares term considers data-dependent variance estimation, which fully contribute to improving the image quality with sparse-view projection measurement. Subsequently, an alternating optimization algorithm was adopted to minimize the associative objective function. To evaluate the PWLS-TGV method, both qualitative and quantitative studies were conducted by using digital and physical phantoms. Experimental results show that the present PWLS-TGV method can achieve images with several noticeable gains over the original TV-based method in terms of accuracy and resolution properties.
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