ArticleKorean journal of radiology2020
Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction.
Article in Korean journal of radiology, 2020. 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.
- Deep Learning-Based Augmented Contrast-Enhancement and Denoising for Reduced-Iodine and Low-Radiation 70-kVp Cerebral CT Angiography: A Prospective Study.Korean journal of radiology · 2026Trial
- Feasibility of AI-Denoised Ultra-Low-Dose CT for Detection of Acute Pelvic and Hip Fractures: A Pilot Multi-Reader Study.Diagnostics (Basel, Switzerland) · 2026Article
- Clinical Applications of Artificial Intelligence in Cardiac CT: From Coronary CT Angiography to CT-Derived Fractional Flow Reserve and Myocardial Perfusion Imaging.Korean journal of radiology · 2026Review
- What It Takes to Reduce Radiation Dose for Coronary Artery Imaging Globally.Korean journal of radiology · 2026Article
- Deep-learning denoising for ultrahigh-resolution photon-counting detector CT: phantom and in vivo evaluation of non-calcified coronary plaques.The international journal of cardiovascular imaging · 2026Article
- Sequential Helical-Axial-Helical Triple-Rule-Out CT Angiography: Technical Feasibility and Territory-Specific Image Quality in the Emergency Department.Journal of clinical medicine · 2026Article
- Additional Dose Reduction Potential of Vendor-Agnostic Deep Learning Model: A Phantom Study.Journal of the Korean Society of Radiology · 2026Article
- Comparative analysis of photon-counting and energy-integrating detector CT to identify obstructive coronary artery disease.European radiology · 2026Article
- Improved diagnostic performance of CT-derived FFR using 70-keV monoenergetic spectral CT in coronary artery disease.The international journal of cardiovascular imaging · 2026Article
- Deep-learning reconstruction for noncontrast head computed tomography: Improved image quality and potential diagnostic impact in acute ischemic stroke.Neuroradiology · 2026Article
- Artificial Intelligence and Digital Technology in Cardiovascular Imaging: A Narrative Review.Biotech (Basel (Switzerland)) · 2026Review
- AI-based denoising improves image quality in HCC volume perfusion CT without affecting Milan classification.BMC medical imaging · 2026Article
- Application of Deep Learning Algorithm-Based Image Reconstruction in Improving Abdominal CT Image Quality in Adrenal and Renal Lesions Detection.Radiology research and practice · 2026Article
- Deep learning-based denoising in cardiac CT: effects on image quality, calcium scoring interchangeability, and reporting workflow.Frontiers in radiology · 2026Article
- Diagnostic performance of lumbar spine CT using deep learning denoising to evaluate disc herniation and spinal stenosis.European radiology · 2025Article
- Cardiac CT in the era of artificial intelligence: precision imaging, treatment guidance and optimised risk stratification for coronary artery disease.Open heart · 2025Review
- Enhancing Image Quality in Dental-Maxillofacial CBCT: The Impact of Iterative Reconstruction and AI on Noise Reduction-A Systematic Review.Journal of clinical medicine · 2025Review
- Effect of Deep Learning Image Reconstruction on Image Quality and Pericoronary Fat Attenuation Index.Journal of imaging informatics in medicine · 2025Article
- Deep learning based automatic quantification of aortic valve calcification on contrast enhanced coronary CT angiography.Scientific reports · 2025Article
- Application of computed tomography angiography imaging in coronary heart disease screening and analysis of associated risk factors.Quantitative imaging in medicine and surgery · 2024Article
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5 authors.
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Abstract
objectiveTo assess the feasibility of applying a deep learning-based denoising technique to coronary CT angiography (CCTA) along with iterative reconstruction for additional noise reduction. MATERIALS AND
methodsWe retrospectively enrolled 82 consecutive patients (male:female = 60:22; mean age, 67.0 ± 10.8 years) who had undergone both CCTA and invasive coronary artery angiography from March 2017 to June 2018. All included patients underwent CCTA with iterative reconstruction (ADMIRE level 3, Siemens Healthineers). We developed a deep learning based denoising technique (ClariCT.AI, ClariPI), which was based on a modified U-net type convolutional neural net model designed to predict the possible occurrence of low-dose noise in the originals. Denoised images were obtained by subtracting the predicted noise from the originals. Image noise, CT attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were objectively calculated. The edge rise distance (ERD) was measured as an indicator of image sharpness. Two blinded readers subjectively graded the image quality using a 5-point scale. Diagnostic performance of the CCTA was evaluated based on the presence or absence of significant stenosis (≥ 50% lumen reduction).
resultsObjective image qualities (original vs. denoised: image noise, 67.22 ± 25.74 vs. 52.64 ± 27.40; SNR [left main], 21.91 ± 6.38 vs. 30.35 ± 10.46; CNR [left main], 23.24 ± 6.52 vs. 31.93 ± 10.72; all
conclusionApplication of the deep learning technique along with iterative reconstruction can enhance the noise reduction performance with a significant improvement in objective and subjective image qualities of CCTA images.
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