ArticleJapanese journal of radiology2025
Super-resolution deep learning reconstruction for improved quality of myocardial CT late enhancement.
Article in Japanese journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- 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
- Improved image quality and greater diagnostic suitability in myocardial delayed enhancement CT with deep learning image reconstruction.Japanese journal of radiology · 2026Article
- Diagnostic value of a second-generation super-resolution deep learning-based reconstruction combined with a metal artifact reduction algorithm for pelvic CT.Skeletal radiology · 2026Article
- Super-Resolution Deep Learning Reconstruction Improves Image Quality of Dynamic Myocardial Computed Tomography Perfusion Imaging.Tomography (Ann Arbor, Mich.) · 2026Article
- Reducing motion artifacts in the aorta: super-resolution deep learning reconstruction with motion reduction algorithm.Japanese journal of radiology · 2025Article
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13 authors.
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Abstract
purposeMyocardial computed tomography (CT) late enhancement (LE) allows assessment of myocardial scarring. Super-resolution deep learning image reconstruction (SR-DLR) trained on data acquired from ultra-high-resolution CT may improve image quality for CT-LE. Therefore, this study investigated image noise and image quality with SR-DLR compared with conventional DLR (C-DLR) and hybrid iterative reconstruction (hybrid IR). METHODS AND
methodsWe retrospectively analyzed 30 patients who underwent CT-LE using 320-row CT. The CT protocol comprised stress dynamic CT perfusion, coronary CT angiography, and CT-LE. CT-LE images were reconstructed using three different algorithms: SR-DLR, C-DLR, and hybrid IR. Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and qualitative image quality scores are in terms of noise reduction, sharpness, visibility of scar and myocardial boarder, and overall image quality. Inter-observer differences in myocardial scar sizing in CT-LE by the three algorithms were also compared.
resultsSR-DLR significantly decreased image noise by 35% compared to C-DLR (median 6.2 HU, interquartile range [IQR] 5.6-7.2 HU vs 9.6 HU, IQR 8.4-10.7 HU; p < 0.001) and by 37% compared to hybrid IR (9.8 HU, IQR 8.5-12.0 HU; p < 0.001). SNR and CNR of CT-LE reconstructed using SR-DLR were significantly higher than with C-DLR (both p < 0.001) and hybrid IR (both p < 0.05). All qualitative image quality scores were higher with SR-DLR than those with C-DLR and hybrid IR (all p < 0.001). The inter-observer differences in scar sizing were reduced with SR-DLR and C-DLR compared with hybrid IR (both p = 0.02).
conclusionSR-DLR reduces image noise and improves image quality of myocardial CT-LE compared with C-DLR and hybrid IR techniques and improves inter-observer reproducibility of scar sizing compared to hybrid IR. The SR-DLR approach has the potential to improve the assessment of myocardial scar by CT late enhancement.
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