Department of Radiation Physics, Lund University, Malmö, Sweden.
J Magn Reson Imaging. 2010 May;31(5):1203-9. doi: 10.1002/jmri.22159.
To evaluate the repeatability of the dGEMRIC (delayed gadolinium enhanced MRI of cartilage) method in osteoarthritis-prone knee joints for three different T1 quantification techniques: two-dimensional inversion recovery (2D-IR), three-dimensional Look-Locker (3D-LL), and three-dimensional variable flip angle (3D-VFA).
Nine subjects were examined twice, with a 2-week interval, using all three measurement techniques. Four regions of interest were defined in the central medial and lateral femoral cartilage. The repeatability was evaluated for each measurement technique. For the 3D techniques, the variation between different slices was also evaluated.
Repeatability expressed by root-mean-square coefficient of variation (CV(RMS)) showed similar results for 2D-IR and 3D-LL (5.4-8.4%). For 3D-VFA CV(RMS) was higher (9.3-15.2%). Intraclass correlation coefficient showed both 2D-IR and 3D-LL reliability to be moderate, while 3D-VFA reliability was low. Inter-slice CV(RMS) and ICC was of the same magnitude as the repeatability. No clear differences could be interpreted between the condyles.
Both 2D-IR and 3D-LL perform well in generating repeatable dGEMRIC results, while 3D-VFA results are somewhat inferior. Furthermore, repeatability results in this study are similar to previously published results for healthy subjects. Finally, the positioning of the analyzed images is crucial to generate reliable repeatability results.
评估 dGEMRIC(延迟钆增强 MRI 软骨)方法在易患骨关节炎的膝关节中的重复性,用于三种不同的 T1 量化技术:二维反转恢复(2D-IR)、三维 Look-Locker(3D-LL)和三维可变翻转角(3D-VFA)。
9 名受试者使用所有三种测量技术,间隔 2 周,进行了两次检查。在中央内侧和外侧股骨软骨中定义了 4 个感兴趣区域。评估了每种测量技术的重复性。对于 3D 技术,还评估了不同切片之间的差异。
用均方根变异系数(CV(RMS))表示的重复性,2D-IR 和 3D-LL 的结果相似(5.4-8.4%)。对于 3D-VFA,CV(RMS) 较高(9.3-15.2%)。组内相关系数表明 2D-IR 和 3D-LL 的可靠性为中等,而 3D-VFA 的可靠性较低。切片间 CV(RMS) 和 ICC 与重复性相当。在髁间无法解释明显的差异。
2D-IR 和 3D-LL 在生成可重复的 dGEMRIC 结果方面表现良好,而 3D-VFA 的结果则稍逊一筹。此外,本研究的重复性结果与先前发表的健康受试者的结果相似。最后,分析图像的定位对于生成可靠的重复性结果至关重要。