From the Neurologic Clinic and Policlinic (C.T., M.A., L.K., T.S., K.P.), Department of Medicine and Biomedical Engineering.
Translational Imaging in Neurology Basel (C.T., A.A., M.A., M.W., L.K., K.P.), Department of Medicine and Biomedical Engineering.
AJNR Am J Neuroradiol. 2019 Sep;40(9):1592-1600. doi: 10.3174/ajnr.A6157. Epub 2019 Aug 22.
Currently, accurate and reproducible spinal cord GM segmentation remains challenging and a noninvasive broadly accepted reference standard for spinal cord GM measurements is still a matter of ongoing discussion. Our aim was to assess the reproducibility and accuracy of cervical spinal cord GM and WM cross-sectional area measurements using averaged magnetization inversion recovery acquisitions images and a fully-automatic postprocessing segmentation algorithm.
The cervical spinal cord of 24 healthy subjects (14 women; mean age, 40 ± 11 years) was scanned in a test-retest fashion on a 3T MR imaging system. Twelve axial averaged magnetization inversion recovery acquisitions slices were acquired over a 48-mm cord segment. GM and WM were both manually segmented by 2 experienced readers and compared with an automatic variational segmentation algorithm with a shape prior modified for 3D data with a slice similarity prior. Precision and accuracy of the automatic method were evaluated using coefficients of variation and Dice similarity coefficients.
The mean GM area was 17.20 ± 2.28 mm and the mean WM area was 72.71 ± 7.55 mm using the automatic method. Reproducibility was high for both methods, while being better for the automatic approach (all mean automatic coefficients of variation, ≤4.77%; all differences, < .001). The accuracy of the automatic method compared with the manual reference standard was excellent (mean Dice similarity coefficients: 0.86 ± 0.04 for GM and 0.90 ± 0.03 for WM). The automatic approach demonstrated similar coefficients of variation between intra- and intersession reproducibility as well as among all acquired spinal cord slices.
Our novel approach including the averaged magnetization inversion recovery acquisitions sequence and a fully-automated postprocessing segmentation algorithm demonstrated an accurate and reproducible spinal cord GM and WM segmentation. This pipeline is promising for both the exploration of longitudinal structural GM changes and application in clinical settings in disorders affecting the spinal cord.
目前,准确且可重现的脊髓 GM 分割仍然具有挑战性,并且用于脊髓 GM 测量的非侵入性广泛接受的参考标准仍然是一个正在讨论的问题。我们的目的是使用平均磁化反转恢复采集图像和全自动后处理分割算法评估颈椎脊髓 GM 和 WM 横截面积测量的可重复性和准确性。
对 24 名健康受试者(14 名女性;平均年龄 40±11 岁)的颈椎脊髓进行了测试-再测试磁共振成像系统扫描。在 48mm 脊髓段上采集了 12 个轴向平均磁化反转恢复采集切片。GM 和 WM 均由 2 名有经验的读者手动分割,并与具有形状先验的自动变分分割算法进行比较,该算法具有用于 3D 数据的切片相似性先验。使用变异系数和 Dice 相似系数评估自动方法的精度和准确性。
使用自动方法,GM 区域的平均值为 17.20±2.28mm,WM 区域的平均值为 72.71±7.55mm。两种方法的可重复性均较高,而自动方法的可重复性更好(所有平均自动变异系数,≤4.77%;所有差异,<0.001)。与手动参考标准相比,自动方法的准确性非常好(GM 的平均 Dice 相似系数为 0.86±0.04,WM 的平均 Dice 相似系数为 0.90±0.03)。自动方法在 Intra- 和 Inter-session 可重复性以及所有采集的脊髓切片中均表现出相似的变异系数。
我们的新方法包括平均磁化反转恢复采集序列和全自动后处理分割算法,证明了脊髓 GM 和 WM 的分割具有准确性和可重复性。这种方法有望用于探索纵向结构 GM 变化以及在影响脊髓的疾病的临床应用中。