Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan.
Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan.
Eur J Radiol. 2022 Jan;146:110067. doi: 10.1016/j.ejrad.2021.110067. Epub 2021 Nov 24.
To evaluate the image quality of ultra-high-resolution CT (U-HRCT) in the comparison among four different reconstruction methods, focusing on the gastric wall structure, and to compare the conspicuity of a three-layered structure of the gastric wall between conventional HRCT (C-HRCT) and U-HRCT.
Our retrospective study included 48 patients who underwent contrast-enhanced U-HRCT. Quantitative analyses were performed to compare image noise of U-HRCT between deep-learning reconstruction (DLR) and other three methods (filtered back projection: FBP, hybrid iterative reconstruction: Hybrid-IR, and Model-based iterative reconstruction: MBIR). The mean overall image quality scores were also compared between the DLR and other three methods. In addition, the mean conspicuity scores for the three-layered structure of the gastric wall at five regions were compared between C-HRCT and U-HRCT.
The mean noise of U-HRCT with DLR was significantly lower than that with the other three methods (P < 0.001). The mean overall image quality scores with DLR images were significantly higher than those with the other three methods (P < 0.001). Regarding the comparison between C-HRCT and U-HRCT, the mean conspicuity scores for the three-layered structure of the gastric wall on U-HRCT were significantly better than those on C-HRCT in the fornix (5 [5-5] vs. 3.5 [3-4], P < 0.001), body (4 [3.25-5] vs. 4 [3-4], P = 0.039), angle (5 [4-5] vs. 3 [2-4], P < 0.001), and antral posterior (4 [3.25-5] vs. 2 [2-4], P < 0.001), except for antral anterior (4 [3-5] vs. 3 [3-4], P = 0.230) CONCLUSION: U-HRCT using DLR improved the image noise and overall image quality of the gastric wall as well as the conspicuity of the three-layered structure, suggesting its utility for the evaluation of the anatomical details of the gastric wall structure.
评估四种不同重建方法在超高分辨率 CT(U-HRCT)中的图像质量,重点关注胃壁结构,并比较常规 HRCT(C-HRCT)和 U-HRCT 中胃壁三层结构的显示效果。
本回顾性研究纳入 48 例接受增强 U-HRCT 检查的患者。对 U-HRCT 的图像噪声进行定量分析,比较深度学习重建(DLR)与其他三种方法(滤波反投影:FBP、混合迭代重建:Hybrid-IR 和基于模型的迭代重建:MBIR)之间的差异。比较 DLR 与其他三种方法的整体图像质量评分。此外,比较 C-HRCT 和 U-HRCT 时,胃壁五层结构在五个部位的三层结构显示效果评分。
DLR 重建的 U-HRCT 平均噪声显著低于其他三种方法(P<0.001)。DLR 图像的整体图像质量评分显著高于其他三种方法(P<0.001)。在 C-HRCT 与 U-HRCT 之间的比较中,U-HRCT 上胃壁三层结构的平均显示效果评分明显优于 C-HRCT(贲门部:5 [5-5] 比 3.5 [3-4],P<0.001)、体部:4 [3.25-5] 比 4 [3-4],P=0.039)、角切迹部:5 [4-5] 比 3 [2-4],P<0.001)和胃窦后部:4 [3.25-5] 比 2 [2-4],P<0.001),除了胃窦前壁(4 [3-5] 比 3 [3-4],P=0.230)。
使用 DLR 的 U-HRCT 改善了胃壁的图像噪声和整体图像质量以及胃壁三层结构的显示效果,提示其在评估胃壁结构的解剖细节方面具有一定的应用价值。