Reliability of Longitudinal Brain Volume Loss Measurements between 2 Sites in Patients with Multiple Sclerosis: Comparison of 7 Quantification Techniques
Reliability of Longitudinal Brain Volume Loss Measurements between 2 Sites in Patients with Multiple Sclerosis: Comparison of 7 Quantification Techniques
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DOI：
10.3174/ajnr.a3107
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发表时间：
2012-11-01
影响因子：
3.5
通讯作者：
Cotton, F.
中科院分区：
文献类型：
--
作者：
Durand-Dubief, F.;Belaroussi, B.;Cotton, F.
背景和目的：脑容量损失目前是多发性硬化症神经变性的磁共振成像标志。可用的量化算法执行直接（基于分段的技术）或间接（基于配准的技术）测量。由于没有参考标准技术，评估其准确性和可靠性仍然是一个困难的目标。因此，这项工作的目的是评估应用于从不同 MR 成像系统获取的图像的 7 种不同后处理算法的稳健性。 材料和方法：在两个 1.51 MR 成像系统上对 9 名 MS 患者进行了为期 1 年（3 个时间点）的纵向随访。使用 7 种分割算法评估脑体积变化测量：分割分类算法、FreeSurfer、BBSI、KN-BSI、SIENA、SIENAX 和 JI 算法。 结果：位点间变异性表明基于分割的技术和 SIENAX 提供了大且异质的脑体积变化值。 Bland-Altman 分析显示，两个站点之间的平均差异为 1.8%、0.07% 和 0.79%，分段分类算法、FreeSurfer 和 SIENAX 的宽长度一致性区间分别为 11.66%、7.92% 和 11.94%。相比之下，基于配准的算法表现出更好的再现性，BBSI、KN-BSI 和 JI 的平均差异较低，为 0.45%，平均长度一致性区间为 1.55%。如果SIENA获得较低的平均差0.12%，则其一致区间3.29%更宽。结论：如果脑萎缩估计。仍然是一个悬而未决的问题，未来需要对脑容量量化算法的准确性和可靠性进行研究，以测量多发性硬化症中发生的缓慢而微小的脑容量变化。
BACKGROUND AND PURPOSE: Brain volume loss is currently a MR imaging marker of neurodegeneration in MS. Available quantification algorithms perform either direct (segmentation-based techniques) or indirect (registration-based techniques) measurements. Because there is no reference standard technique, the assessment of their accuracy and reliability remains a difficult goal. Therefore, the purpose of this work was to assess the robustness of 7 different postprocessing algorithms applied to images acquired from different MR imaging systems.MATERIALS AND METHODS: Nine patients with MS were followed longitudinally over 1 year (3 time points) on two 1.51 MR imaging systems. Brain volume change measures were assessed using 7 segmentation algorithms: a segmentation-classification algorithm, FreeSurfer, BBSI, KN-BSI, SIENA, SIENAX, and JI algorithm.RESULTS: Intersite variability showed that segmentation-based techniques and SIENAX provided large and heterogeneous values of brain volume changes. A Bland-Altman analysis showed a mean difference of 1.8%, 0.07%, and 0.79% between the 2 sites, and a wide length agreement interval of 11.66%, 7.92%, and 11.94% for the segmentation-classification algorithm, FreeSurfer, and SIENAX, respectively. In contrast, registration-based algorithms showed better reproducibility, with a low mean difference of 0.45% for BBSI, KN-BSI and JI, and a mean length agreement interval of 1.55%. If SIENA obtained a lower mean difference of 0.12%, its agreement interval of 3.29% was wider.CONCLUSIONS: If brain atrophy estimation. remains an open issue, future investigations of the accuracy and reliability of the brain volume quantification algorithms are needed to measure the slow and small brain volume changes occurring in MS.