Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Department of Radiology, Juntendo University Urayasu Hospital, 2-1-1 Tomioka, Urayasu, Chiba, 279-0021, Japan.
Jpn J Radiol. 2023 Aug;41(8):863-871. doi: 10.1007/s11604-023-01402-5. Epub 2023 Mar 2.
The 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).
This 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.
In 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).
DLR-S provided better pelvic CT images in patients with metal hip prostheses than IR-S and DLR.
本研究旨在评估深度学习重建（DLR）联合单能量金属伪影降低（SEMAR）技术（DLR-S）在金属髋关节假体患者盆腔螺旋 CT 图像中的应用效果，并与 DLR 和混合迭代重建（IR）联合 SEMAR（IR-S）进行比较。
本回顾性研究纳入了 26 例（平均年龄 68.6±16.6 岁，男性 9 例，女性 17 例）金属髋关节假体患者，均行包括骨盆在内的 CT 检查。采用 DLR-S、DLR 和 IR-S 对轴向骨盆 CT 图像进行重建。在逐个定性分析中，两名放射科医生评估了金属伪影、噪声和骨盆结构显示的程度。在并排定性分析（DLR-S 与 IR-S）中，两位放射科医生评估了金属伪影和整体质量。通过在膀胱和腰大肌上放置感兴趣区，记录其 CT 衰减的标准差，并根据这些值计算出伪影指数。采用 Wilcoxon 符号秩检验比较 DLR-S 与 DLR 和 DLR 与 IR-S 之间的结果。
逐个定性分析显示，DLR-S 的金属伪影和结构显示明显优于 DLR，而在 DLR-S 与 IR-S 之间，仅读者 1 注意到显著差异。两名读者均认为 DLR-S 的图像噪声明显低于 IR-S。在并排分析中，两位读者均认为 DLR-S 图像在整体图像质量和金属伪影方面明显优于 IR-S 图像。DLR-S 的伪影指数中位数（四分位间距）为 10.1（4.4-16.0），明显优于 DLR（23.1，6.5-36.1）和 IR-S（11.4，7.8-17.9）。
与 IR-S 和 DLR 相比，DLR-S 为金属髋关节假体患者提供了更好的盆腔 CT 图像。
