ArticleIEEE transactions on medical imaging2017
Robust Low-Dose CT Sinogram Preprocessing via Exploiting Noise-Generating Mechanism.
Article in IEEE transactions on medical imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Patch-based dual-domain photon-counting CT data correction with residual-based WGAN-ViT.Physics in medicine and biology · 2025Article
- HybridFormer: a convolutional neural network-Transformer architecture for low dose computed tomography image denoising.PeerJ. Computer science · 2025Article
- Self-adaption and texture generation: A hybrid loss function for low-dose CT denoising.Journal of applied clinical medical physics · 2023Article
- Bilateral Weighted Relative Total Variation for Low-Dose CT Reconstruction.Journal of digital imaging · 2023Article
- [A nonlocal spectral similarity-induced material decomposition method for noise reduction of dual-energy CT images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2022Article
- Recent Progress Toward Imaging Application of Multifunction Sonosensitizers in Sonodynamic Therapy.International journal of nanomedicine · 2022Review
- [CT image quality assessment based on prior information of pre-restored images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021Article
- Chest Computed Tomography Images in Neonatal Bronchial Pneumonia under the Adaptive Statistical Iterative Reconstruction Algorithm.Journal of healthcare engineering · 2021Article
- The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence.European radiology · 2019Review
- Unpaired Low-Dose CT Denoising Network Based on Cycle-Consistent Generative Adversarial Network with Prior Image Information.Computational and mathematical methods in medicine · 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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Authors and funding
7 authors.
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Abstract
Computed tomography (CT) image recovery from low-mAs acquisitions without adequate treatment is always severely degraded due to a number of physical factors. In this paper, we formulate the low-dose CT sinogram preprocessing as a standard maximum a posteriori (MAP) estimation, which takes full consideration of the statistical properties of the two intrinsic noise sources in low-dose CT, i.e., the X-ray photon statistics and the electronic noise background. In addition, instead of using a general image prior as found in the traditional sinogram recovery models, we design a new prior formulation to more rationally encode the piecewise-linear configurations underlying a sinogram than previously used ones, like the TV prior term. As compared with the previous methods, especially the MAP-based ones, both the likelihood/loss and prior/regularization terms in the proposed model are ameliorated in a more accurate manner and better comply with the statistical essence of the generation mechanism of a practical sinogram. We further construct an efficient alternating direction method of multipliers algorithm to solve the proposed MAP framework. Experiments on simulated and real low-dose CT data demonstrate the superiority of the proposed method according to both visual inspection and comprehensive quantitative performance evaluation.
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