Service de Neurologie A et Fondation Eugène Devic EDMUS pour la Sclérose en Plaques, Hôpital Neurologique Pierre Wertheimer, 59 Boulevard Pinel, 69677 Bron Cedex, France.
AJNR Am J Neuroradiol. 2012 Nov;33(10):1918-24. doi: 10.3174/ajnr.A3107. Epub 2012 Jul 12.
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.
Nine patients with MS were followed longitudinally over 1 year (3 time points) on two 1.5T 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.
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.
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.
脑容量损失是目前 MS 神经退行性变的 MRI 成像标志物。现有的定量算法要么进行直接（基于分割的技术）测量，要么进行间接（基于配准的技术）测量。由于没有参考标准技术，因此评估其准确性和可靠性仍然是一个困难的目标。因此，本研究的目的是评估 7 种不同的后处理算法应用于不同 MRI 系统采集的图像的稳健性。
9 例 MS 患者在 2 台 1.5T MRI 系统上进行了 1 年（3 个时间点）的纵向随访。使用 7 种分割算法评估脑容量变化测量：分割分类算法、FreeSurfer、BBSI、KN-BSI、SIENA、SIENAX 和 JI 算法。
站点间变异性表明，基于分割的技术和 SIENAX 提供了大而不均匀的脑容量变化值。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%，分割分类算法、FreeSurfer 和 SIENAX 的长度一致区间分别为 11.66%、7.92%和 11.94%。相比之下，基于配准的算法显示出更好的可重复性，BBSI、KN-BSI 和 JI 的平均差异为 0.45%，平均长度一致区间为 1.55%。如果 SIENA 获得了较低的平均差异 0.12%，其 3.29%的一致性区间则更宽。
如果脑萎缩的估计仍然是一个悬而未决的问题，那么需要对脑容量定量算法的准确性和可靠性进行进一步的研究，以测量 MS 中发生的缓慢和微小的脑容量变化。
