ArticleJapanese journal of radiology2023
Deep learning reconstruction with single-energy metal artifact reduction in pelvic computed tomography for patients with metal hip prostheses.
Article in Japanese journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 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
- 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
- Recent Advances in Musculoskeletal Radiology: Bridging Innovation and Clinical Application.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Review
- Article
- Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?La Radiologia medica · 2025Review
- Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.Japanese journal of radiology · 2025Review
- Accelerating FLAIR imaging via deep learning reconstruction: potential for evaluating white matter hyperintensities.Japanese journal of radiology · 2025Article
- Enhancing image quality in computed tomography angiography follow-ups after endovascular aneurysm repair: a comparative study of reconstruction techniques.BMC medical imaging · 2024Article
- The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI.Japanese journal of radiology · 2024Review
- Clinical applications of artificial intelligence in liver imaging.La Radiologia medica · 2023Review
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7 authors.
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Abstract
purposeThe aim of this study was to assess the impact of the deep learning reconstruction (DLR) with single-energy metal artifact reduction (SEMAR) (DLR-S) technique in pelvic helical computed tomography (CT) images for patients with metal hip prostheses and compare it with DLR and hybrid iterative reconstruction (IR) with SEMAR (IR-S). MATERIALS AND
methodsThis retrospective study included 26 patients (mean age 68.6 ± 16.6 years, with 9 males and 17 females) with metal hip prostheses who underwent a CT examination including the pelvis. Axial pelvic CT images were reconstructed using DLR-S, DLR, and IR-S. In one-by-one qualitative analyses, two radiologists evaluated the degree of metal artifacts, noise, and pelvic structure depiction. In side-by-side qualitative analyses (DLR-S vs. IR-S), the two radiologists evaluated metal artifacts and overall quality. By placing regions of interest on the bladder and psoas muscle, the standard deviations of their CT attenuation were recorded, and the artifact index was calculated based on them. Results were compared between DLR-S vs. DLR and DLR vs. IR-S using the Wilcoxon signed-rank test.
resultsIn one-by-one qualitative analyses, metal artifacts and structure depiction in DLR-S were significantly better than those in DLR; however, between DLR-S and IR-S, significant differences were noted only for reader 1. Image noise in DLR-S was rated as significantly reduced compared with that in IR-S by both readers. In side-by-side analyses, both readers rated that the DLR-S images are significantly better than IR-S images regarding overall image quality and metal artifacts. The median (interquartile range) of the artifact index for DLR-S was 10.1 (4.4-16.0) and was significantly better than those for DLR (23.1, 6.5-36.1) and IR-S (11.4, 7.8-17.9).
conclusionDLR-S provided better pelvic CT images in patients with metal hip prostheses than IR-S and DLR.
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