Group data analysis in brainstem neuroimaging is predicated on accurate co-registration of anatomy. As the brainstem is comprised of many functionally heterogeneous nuclei densely situated adjacent to one another, relatively small errors in co-registration can manifest in increased variance or decreased sensitivity (or significance) in detecting activations. We have devised a 2-stage automated, reference mask guided registration technique (Automated Brainstem Co-registration, or ABC) for improved brainstem co-registration. Our approach utilized a brainstem mask dataset to weight an automated co-registration cost function. Our method was validated through measurement of RMS error at 12 manually defined landmarks. These landmarks were also used as guides for a secondary manual co-registration option, intended for outlier individuals that may not adequately co-register with our automated method. Our methodology was tested on 10 healthy human subjects and compared to traditional co-registration techniques (Talairach transform and automated affine transform to the MNI-152 template). We found that ABC had a significantly lower mean RMS error (1.22 +/- 0.39 mm) than Talairach transform (2.88 +/- 1.22 mm, mu +/- sigma) and the global affine (3.26 +/- 0.81 mm) method. Improved accuracy was also found for our manual-landmark-guided option (1.51 +/- 0.43 mm). Visualizing individual brainstem borders demonstrated more consistent and uniform overlap for ABC compared to traditional global co-registration techniques. Improved robustness (lower susceptibility to outliers) was demonstrated with ABC through lower inter-subject RMS error variance compared with traditional co-registration methods. The use of easily available and validated tools (AFNI and FSL) for this method should ease adoption by other investigators interested in brainstem data group analysis.

译文

脑干神经影像学中的组数据分析基于解剖结构的准确共配准。由于脑干由彼此密集相邻的许多功能异质核组成,因此在共同配准中相对较小的误差可能表现为检测激活的方差增加或敏感性降低 (或显着性)。我们设计了一种2阶段自动参考面罩引导的注册技术 (自动脑干共同注册或ABC),以改善脑干共同注册。我们的方法利用脑干掩模数据集对自动共同注册成本函数进行加权。我们的方法通过测量12个手动定义的地标上的均方根误差进行了验证。这些地标也被用作辅助手动共同注册选项的指南,该选项适用于可能无法与我们的自动方法充分共同注册的离群值个人。我们的方法在10名健康人类受试者上进行了测试,并与传统的共配准技术 (Talairach变换和自动仿射变换到MNI-152模板) 进行了比较。我们发现ABC的平均均方根误差 (1.22 +/-0.39毫米) 明显低于Talairach变换 (2.88 +/-1.22毫米,mu +/- sigma) 和全局仿射 (3.26 +/-0.81毫米) 方法。我们的手动地标引导选项 (1.51 +/-0.43毫米) 也提高了准确性。与传统的全球共配准技术相比,可视化单个脑干边界显示出ABC更加一致和统一的重叠。与传统的共配准方法相比,ABC通过较低的受试者间均方根误差方差证明了更高的鲁棒性 (对异常值的敏感性较低)。对于这种方法,使用易于获得和验证的工具 (AFNI和FSL) 应该可以简化对脑干数据组分析感兴趣的其他研究人员的采用。

+1
+2
100研值 100研值 ¥99课程
检索文献一次
下载文献一次

去下载>

成功解锁2个技能,为你点赞

《SCI写作十大必备语法》
解决你的SCI语法难题!

技能熟练度+1

视频课《玩转文献检索》
让你成为检索达人!

恭喜完成新手挑战

手机微信扫一扫,添加好友领取

免费领《Endnote文献管理工具+教程》

微信扫码, 免费领取

手机登录

获取验证码
登录