ArticleBMC medical imaging2023
Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience.
Article in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 16 citations in OpenAlex.
- Evaluation of a super-resolution deep learning reconstruction algorithm in abdominal CT imaging-A qualitative and quantitative performance analysis.Journal of applied clinical medical physics · 2026Article
- Image Quality Advancements in Low-Dose Pediatric CT Using Super-Resolution Deep-Learning Reconstruction.Journal of imaging informatics in medicine · 2026Article
- Article
- High-resolution deep learning reconstruction to improve the accuracy of CT fractional flow reserve.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
- Improving Measurement Accuracy of the Degree of Carotid In-Stent Stenosis Using CT Angiography: A Comparison With DSA.Stroke (Hoboken, N.J.) · 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
- 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
- A systematic review on deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction.European journal of radiology open · 2025Article
- Evaluation of SR-DLR in low-dose abdominal CT: superior image quality and noise reduction.Abdominal radiology (New York) · 2025Article
- 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
- Improvement of Quantification of Myocardial Synthetic ECV with Second-Generation Deep Learning Reconstruction.Journal of cardiovascular development and disease · 2024Article
- A preliminary study of super-resolution deep learning reconstruction with cardiac option for evaluation of endovascular-treated intracranial aneurysms.The British journal of radiology · 2024Article
- Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom.Quantitative imaging in medicine and surgery · 2024Article
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Authors and funding
7 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
A super-resolution deep learning reconstruction (SR-DLR) algorithm trained using data acquired on the ultrahigh spatial resolution computed tomography (UHRCT) has the potential to provide better image quality of coronary arteries on the whole-heart, single-rotation cardiac coverage on a 320-detector row CT scanner. However, the advantages of SR-DLR at coronary computed tomography angiography (CCTA) have not been fully investigated. The present study aimed to compare the image quality of the coronary arteries and in-stent lumen between SR-DLR and model-based iterative reconstruction (MBIR). We prospectively enrolled 70 patients (median age, 69 years; interquartile range [IQR], 59-75 years; 50 men) who underwent CCTA using a 320-detector row CT scanner between January and August 2022. The image noise in the ascending aorta, left atrium, and septal wall of the ventricle was measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the proximal coronary arteries were calculated. Of the twenty stents, stent strut thickness and luminal diameter were quantitatively evaluated. The image noise on SR-DLR was significantly lower than that on MBIR (median 22.1 HU; IQR, 19.3-24.9 HU vs. 27.4 HU; IQR, 24.2-31.2 HU, p < 0.01), whereas the SNR (median 16.3; IQR, 11.8-21.8 vs. 13.7; IQR, 9.9-18.4, p = 0.01) and CNR (median 24.4; IQR, 15.5-30.2 vs. 19.2; IQR, 14.1-23.2, p < 0.01) on SR-DLR were significantly higher than that on MBIR. Stent struts were significantly thinner (median, 0.68 mm; IQR, 0.61-0.78 mm vs. 0.81 mm; IQR, 0.72-0.96 mm, p < 0.01) and in-stent lumens were significantly larger (median, 1.84 mm; IQR, 1.65-2.26 mm vs. 1.52 mm; IQR, 1.28-2.25 mm, p < 0.01) on SR-DLR than on MBIR. Although further large-scale studies using invasive coronary angiography as the reference standard, comparative studies with UHRCT, and studies in more challenging population for CCTA are needed, this study's initial experience with SR-DLR would improve the utility of CCTA in daily clinical practice due to the better image quality of the coronary arteries and in-stent lumen at CCTA compared with conventional MBIR.
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