ArticleJapanese journal of radiology2025
Reducing motion artifacts in the aorta: super-resolution deep learning reconstruction with motion reduction algorithm.
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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Who cites it
5 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
- 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
- Super-resolution deep learning reconstruction improves the depiction of peripancreatic arteries and image quality in pancreatic cancer CT.Abdominal radiology (New York) · 2026Article
- Clinical evaluation of a motion correction software based on partial angle reconstruction in coronary CT angiography.The international journal of cardiovascular imaging · 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
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4 authors.
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Abstract
purposeTo assess the efficacy of super-resolution deep learning reconstruction (SR-DLR) with motion reduction algorithm (SR-DLR-M) in mitigating aorta motion artifacts compared to SR-DLR and deep learning reconstruction with motion reduction algorithm (DLR-M). MATERIALS AND
methodsThis retrospective study included 86 patients (mean age, 65.0 ± 14.1 years; 53 males) who underwent contrast-enhanced CT including the chest region. CT images were reconstructed with SR-DLR-M, SR-DLR, and DLR-M. Circular or ovoid regions of interest were placed on the aorta, and the standard deviation of the CT attenuation was recorded as quantitative noise. From the CT attenuation profile along a line region of interest that intersected the left common carotid artery wall, edge rise slope and edge rise distance were calculated. Two readers assessed the images based on artifact, sharpness, noise, structure depiction, and diagnostic acceptability (for aortic dissection).
resultsQuantitative noise was 7.4/5.4/8.3 Hounsfield unit (HU) in SR-DLR-M/SR-DLR/DLR-M. Significant differences were observed between SR-DLR-M vs. SR-DLR and DLR-M (p < 0.001). Edge rise slope and edge rise distance were 107.1/108.8/85.8 HU/mm and 1.6/1.5/2.0 mm, respectively, in SR-DLR-M/SR-DLR/DLR-M. Statistically significant differences were detected between SR-DLR-M vs. DLR-M (p ≤ 0.001 for both). Two readers scored artifacts in SR-DLR-M as significantly better than those in SR-DLR (p < 0.001). Scores for sharpness, noise, and structure depiction in SR-DLR-M were significantly better than those in DLR-M (p ≤ 0.005). Diagnostic acceptability in SR-DLR-M was significantly better than that in SR-DLR and DLR-M (p < 0.001).
conclusionsSR-DLR-M provided significantly better CT images in diagnosing aortic dissection compared to SR-DLR and DLR-M.
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