Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, North Carolina 27705, USA.
Hum Brain Mapp. 2010 Nov;31(11):1751-62. doi: 10.1002/hbm.20973.
Large-scale longitudinal studies of regional brain volume require reliable quantification using automated segmentation and labeling. However, repeated MR scanning of the same subject, even if using the same scanner and acquisition parameters, does not result in identical images due to small changes in image orientation, changes in prescan parameters, and magnetic field instability. These differences may lead to appreciable changes in estimates of volume for different structures. This study examined scan-rescan reliability of automated segmentation algorithms for measuring several subcortical regions, using both within-day and across-day comparison sessions in a group of 23 normal participants. We found that the reliability of volume measures including percent volume difference, percent volume overlap (Dice's coefficient), and intraclass correlation coefficient (ICC), varied substantially across brain regions. Low reliability was observed in some structures such as the amygdala (ICC = 0.6), with higher reliability (ICC = 0.9) for other structures such as the thalamus and caudate. Patterns of reliability across regions were similar for automated segmentation with FSL/FIRST and FreeSurfer (longitudinal stream). Reliability was associated with the volume of the structure, the ratio of volume to surface area for the structure, the magnitude of the interscan interval, and the method of segmentation. Sample size estimates for detecting changes in brain volume for a range of likely effect sizes also differed by region. Thus, longitudinal research requires a careful analysis of sample size and choice of segmentation method combined with a consideration of the brain structure(s) of interest and the magnitude of the anticipated effects.
大规模的脑区体积纵向研究需要使用自动分割和标记来实现可靠的定量。然而，即使使用相同的扫描仪和采集参数对同一受试者进行重复的磁共振扫描，由于图像方向的微小变化、预扫描参数的变化和磁场不稳定，也不会得到完全相同的图像。这些差异可能导致不同结构的体积估计值发生明显变化。本研究在一组 23 名正常受试者中，使用日内和跨日比较的方法，对几种皮质下区域的自动分割算法的扫描-重扫可靠性进行了研究。我们发现，包括体积差异百分比、体积重叠百分比（Dice 系数）和组内相关系数（ICC）在内的体积测量值的可靠性在不同脑区之间存在显著差异。一些结构的可靠性较低，如杏仁核（ICC=0.6），而其他结构的可靠性较高，如丘脑和尾状核（ICC=0.9）。使用 FSL/FIRST 和 FreeSurfer（纵向流）进行自动分割时，跨区域的可靠性模式相似。可靠性与结构的体积、结构的体积与表面积比、扫描间隔的大小以及分割方法有关。对于一系列可能的效应大小，检测脑体积变化的样本量估计也因区域而异。因此，纵向研究需要仔细分析样本量和分割方法的选择，并结合感兴趣的脑结构和预期效应的大小进行考虑。
