ArticleRadiology. Cardiothoracic imaging2023
Super-Resolution Deep Learning Reconstruction for Improved Image Quality of Coronary CT Angiography.
Article in Radiology. Cardiothoracic imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Improved delineation of the cystic artery using super-resolution deep learning reconstruction in contrast-enhanced abdominal computed tomography.Radiological physics and technology · 2026Article
- Super-resolution deep learning reconstruction achieves low-dose high-resolution imaging in pediatric cardiac computed tomography.Pediatric radiology · 2026Article
- Impact of Super-Resolution Deep Learning Reconstruction on Low-Dose CT in Patients with Central Venous Catheter or Central Venous Port.Journal of imaging informatics in medicine · 2026Article
- Deep Learning-based Monoenergetic Imaging for Calcified Coronary Stenosis Assessment at Energy-integrating Detector CT.Radiology. Cardiothoracic imaging · 2026Article
- Artificial Intelligence and Digital Technology in Cardiovascular Imaging: A Narrative Review.Biotech (Basel (Switzerland)) · 2026Review
- Super-Resolution Deep Learning Reconstruction Improves Image Quality of Dynamic Myocardial Computed Tomography Perfusion Imaging.Tomography (Ann Arbor, Mich.) · 2026Article
- Low-tube-potential ultra-high-resolution coronary CTA with photon-counting detector CT for stent evaluation: a comparative feasibility study.Japanese journal of radiology · 2025Article
- Reducing motion artifacts in the aorta: super-resolution deep learning reconstruction with motion reduction algorithm.Japanese journal of radiology · 2025Article
- Influence of deep learning-based super-resolution reconstruction on Agatston score.European radiology · 2025Article
- Improved image quality and diagnostic performance of coronary computed tomography angiography-derived fractional flow reserve with super-resolution deep learning reconstruction.Quantitative imaging in medicine and surgery · 2025Article
- High-resolution deep learning reconstruction for coronary CTA: compared efficacy of stenosis evaluation with other methods at in vitro and in vivo studies.European radiology · 2025Article
- Ultra-low-dose coronary CT angiography via super-resolution deep learning reconstruction: impact on image quality, coronary plaque, and stenosis analysis.European radiology · 2025Article
- Super-resolution deep learning reconstruction for improved quality of myocardial CT late enhancement.Japanese journal of radiology · 2025Article
- Phantom-based evaluation of image quality in Transformer-enhanced 2048-matrix CT imaging at low and ultralow doses.Japanese journal of radiology · 2025Article
- Future Applications of Cardiothoracic CT.Radiology · 2025Review
- Recent trends in scientific research in chest radiology: What to do or not to do? That is the critical question in research.Japanese journal of radiology · 2025Review
- Contrast-enhanced thin-slice abdominal CT with super-resolution deep learning reconstruction technique: evaluation of image quality and visibility of anatomical structures.Japanese journal of radiology · 2025Article
- Artificial intelligence for cardiac imaging is ready for widespread clinical use: Pro Con debate AI for cardiac imaging.BJR open · 2025Review
- Use of AI in Cardiac CT and MRI: A Scientific Statement from the ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM, and RSNA.Radiology · 2025Review
- Impactful Cardiac CT and MRI Articles from 2023.Radiology. Cardiothoracic imaging · 2024Review
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Authors and funding
9 authors.
Funding
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Abstract
Purpose: To investigate image noise and edge sharpness of coronary CT angiography (CCTA) with super-resolution deep learning reconstruction (SR-DLR) compared with conventional DLR (C-DLR) and to evaluate agreement in stenosis grading using CCTA with that from invasive coronary angiography (ICA) as the reference standard. Materials and Methods: This retrospective study included 58 patients (mean age, 69.0 years ± 12.8 [SD]; 38 men, 20 women) who underwent CCTA using 320-row CT between April and September 2022. All images were reconstructed with two different algorithms: SR-DLR and C-DLR. Image noise, signal-to-noise ratio, edge sharpness, full width at half maximum (FWHM) of stent, and agreement in stenosis grading with that from ICA were compared. Stenosis was visually graded from 0 to 5, with 5 indicating occlusion. Results: SR-DLR significantly decreased image noise by 31% compared with C-DLR (12.6 HU ± 2.3 vs 18.2 HU ± 1.9; Conclusion: SR-DLR improved vessel sharpness, image noise, and accuracy of coronary stenosis grading compared with the C-DLR technique.
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