ArticleScientific reports2016
Improving Low-dose Cardiac CT Images based on 3D Sparse Representation.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Computational workflows for natural and biomedical image processing based on hypercomplex algebras.Patterns (New York, N.Y.) · 2025Article
- Real-time algorithm for Poissonian noise reduction in low-dose fluoroscopy: performance evaluation.Biomedical engineering online · 2019Article
- Efficient big data assimilation through sparse representation: A 3D benchmark case study in petroleum engineering.PloS one · 2018Article
- Follow-up of iatrogenic aorto-coronary "Dunning" dissections by cardiac computed tomography imaging.BMC medical imaging · 2017Observational
- John's Equation-based Consistency Condition and Corrupted Projection Restoration in Circular Trajectory Cone Beam CT.Scientific reports · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiac computed tomography (CCT) is a reliable and accurate tool for diagnosis of coronary artery diseases and is also frequently used in surgery guidance. Low-dose scans should be considered in order to alleviate the harm to patients caused by X-ray radiation. However, low dose CT (LDCT) images tend to be degraded by quantum noise and streak artifacts. In order to improve the cardiac LDCT image quality, a 3D sparse representation-based processing (3D SR) is proposed by exploiting the sparsity and regularity of 3D anatomical features in CCT. The proposed method was evaluated by a clinical study of 14 patients. The performance of the proposed method was compared to the 2D spares representation-based processing (2D SR) and the state-of-the-art noise reduction algorithm BM4D. The visual assessment, quantitative assessment and qualitative assessment results show that the proposed approach can lead to effective noise/artifact suppression and detail preservation. Compared to the other two tested methods, 3D SR method can obtain results with image quality most close to the reference standard dose CT (SDCT) images.
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Registered trials
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