Beller Ebba, Keeser Daniel, Wehn Antonia, Malchow Berend, Karali Temmuz, Schmitt Andrea, Papazova Irina, Papazov Boris, Schoeppe Franziska, de Figueiredo Giovanna Negrao, Ertl-Wagner Birgit, Stoecklein Sophia
Department of Radiology, Ludwig-Maximilians University Munich, Munich, Germany.
Institut für Diagnostische und Interventionelle Radiologie, Kinder- und Neuroradiologie, Universitätsmedizin Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany.
Neuroradiology. 2019 Feb;61(2):129-136. doi: 10.1007/s00234-018-2121-2. Epub 2018 Nov 6.
Development of a warp-based automated brain segmentation approach of 3D fluid-attenuated inversion recovery (FLAIR) images and comparison to 3D T1-based segmentation.
3D FLAIR and 3D T1-weighted sequences of 30 healthy subjects (mean age 29.9 ± 8.3 years, 8 female) were acquired on the same 3T MR scanner. Warp-based segmentation was applied for volumetry of total gray matter (GM), white matter (WM), and 116 atlas regions. Segmentation results of both sequences were compared using Pearson correlation (r).
Correlation of GM segmentation results based on FLAIR and T1 was overall good for cortical structures (mean r across all cortical structures = 0.76). Comparatively weaker results were found in the occipital lobe (r = 0.77), central region (mean r = 0.58), basal ganglia (mean r = 0.59), thalamus (r = 0.30), and cerebellum (r = 0.73). FLAIR segmentation underestimated volume of the central region compared to T1, but showed a better anatomic concordance with the occipital lobe on visual review and subcortical structures, when also compared to manual segmentation. Visual analysis of FLAIR-based WM segmentation revealed frequent misclassification of regions of high signal intensity as GM.
Warp-based FLAIR segmentation yields comparable results to T1 segmentation for most cortical GM structures and may provide anatomically more congruent segmentation of subcortical GM structures. Selected cortical regions, especially the central region and total WM, seem to be underestimated on FLAIR segmentation.
开发一种基于形变的三维液体衰减反转恢复（FLAIR）图像自动脑部分割方法，并与基于三维T1的分割方法进行比较。
在同一台3T磁共振扫描仪上采集30名健康受试者（平均年龄29.9±8.3岁，8名女性）的三维FLAIR和三维T1加权序列。基于形变的分割方法用于全脑灰质（GM）、白质（WM）和116个图谱区域的体积测量。使用Pearson相关性（r）比较两个序列的分割结果。
基于FLAIR和T1的GM分割结果在皮质结构上总体相关性良好（所有皮质结构的平均r = 0.76）。在枕叶（r = 0.77）、中央区域（平均r = 0.58）、基底神经节（平均r = 0.59）、丘脑（r = 0.30）和小脑（r = 0.73）中发现相关性相对较弱。与T1相比，FLAIR分割低估了中央区域的体积，但在视觉检查中，与枕叶和皮质下结构相比，与手动分割相比，显示出更好的解剖一致性。基于FLAIR的WM分割的视觉分析显示，高信号强度区域经常被误分类为GM。
基于形变的FLAIR分割对于大多数皮质GM结构产生与T1分割相当的结果，并且可能提供皮质下GM结构在解剖学上更一致的分割。选定的皮质区域，特别是中央区域和全脑WM，在FLAIR分割中似乎被低估。
