ArticleDiagnostics (Basel, Switzerland)2023
Noise Reduction Using Singular Value Decomposition with Jensen-Shannon Divergence for Coronary Computed Tomography Angiography.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Combining 3D iterative image reconstruction and deep learning to improve image quality of knee joint MRI fast sequences: a focus on meniscal injury evaluation.Quantitative imaging in medicine and surgery · 2026Article
- Feasibility study of unsupervised anomaly detection using Wasserstein GAN in SPECT image.EJNMMI physics · 2025Article
- Application Value of Intelligent Quick Magnetic Resonance for Accelerating Brain MR Scanning and Improving Image Quality in Acute Ischemic StrokeCurrent medical imaging · 2025Article
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Abstract
Coronary computed tomography angiography (CCTA) is widely used due to its improvements in computed tomography (CT) diagnostic performance. Unlike other CT examinations, CCTA requires shorter rotation times of the X-ray tube, improving the temporal resolution and facilitating the imaging of the beating heart in a stationary state. However, reconstructed CT images, including those of the coronary arteries, contain insufficient X-ray photons and considerable noise. In this study, we introduce an image-processing technique for noise reduction using singular value decomposition (SVD) for CCTA images. The threshold of SVD was determined on the basis of minimization of Jensen-Shannon (JS) divergence. Experiments were performed with various numerical phantoms and varying levels of noise to reduce noise in clinical CCTA images using the determined threshold value. The numerical phantoms produced 10% higher-quality images than the conventional noise reduction method when compared on a quantitative SSIM basis. The threshold value determined by minimizing the JS-divergence was found to be useful for efficient noise reduction in actual clinical images, depending on the level of noise.
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