Yasaka Koichiro, Tsujimoto Rin, Miyo Rintaro, Abe Osamu
Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan.
Jpn J Radiol. 2025 Aug 9. doi: 10.1007/s11604-025-01849-8.
To 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).
This 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).
Quantitative 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).
SR-DLR-M provided significantly better CT images in diagnosing aortic dissection compared to SR-DLR and DLR-M.
评估采用运动减少算法的超分辨率深度学习重建（SR-DLR-M）相较于SR-DLR以及采用运动减少算法的深度学习重建（DLR-M）在减轻主动脉运动伪影方面的效果。
这项回顾性研究纳入了86例患者（平均年龄65.0±14.1岁；53例男性），这些患者均接受了包括胸部区域的对比增强CT检查。CT图像分别采用SR-DLR-M、SR-DLR和DLR-M进行重建。在主动脉上放置圆形或椭圆形感兴趣区域，并将CT衰减的标准差记录为定量噪声。从与左颈总动脉壁相交的感兴趣线区域的CT衰减剖面中，计算边缘上升斜率和边缘上升距离。两名阅片者基于伪影、清晰度、噪声、结构描绘以及诊断可接受性（用于主动脉夹层）对图像进行评估。
SR-DLR-M/SR-DLR/DLR-M的定量噪声分别为7.4/5.4/8.3亨氏单位（HU）。SR-DLR-M与SR-DLR和DLR-M之间观察到显著差异（p<0.001）。SR-DLR-M/SR-DLR/DLR-M的边缘上升斜率和边缘上升距离分别为107.1/108.8/85.8 HU/mm和1.6/1.5/2.0 mm。SR-DLR-M与DLR-M之间检测到统计学显著差异（两者p≤0.001）。两名阅片者对SR-DLR-M中伪影的评分显著优于SR-DLR中的伪影评分（p<0.001）。SR-DLR-M中清晰度、噪声和结构描绘的评分显著优于DLR-M中的评分（p≤0.005）。SR-DLR-M的诊断可接受性显著优于SR-DLR和DLR-M（p<0.001）。
与SR-DLR和DLR-M相比，SR-DLR-M在诊断主动脉夹层方面提供了明显更好的CT图像。
