Dunås Tora, Wåhlin Anders, Ambarki Khalid, Zarrinkoob Laleh, Malm Jan, Eklund Anders
Department of Radiation Sciences, Umeå University, S-901 87, Umeå, Sweden.
Umeå Center for Functional Brain Imaging, Umeå University, S-901 87, Umeå, Sweden.
Neuroinformatics. 2017 Jan;15(1):101-110. doi: 10.1007/s12021-016-9320-y.
Improved whole brain angiographic and velocity-sensitive MRI is pushing the boundaries of noninvasively obtained cerebral vascular flow information. The complexity of the information contained in such datasets calls for automated algorithms and pipelines, thus reducing the need of manual analyses by trained radiologists. The objective of this work was to lay the foundation for such automated pipelining by constructing and evaluating a probabilistic atlas describing the shape and location of the major cerebral arteries. Specifically, we investigated how the implementation of a non-linear normalization into Montreal Neurological Institute (MNI) space improved the alignment of individual arterial branches. In a population-based cohort of 167 subjects, age 64-68 years, we performed 4D flow MRI with whole brain volumetric coverage, yielding both angiographic and anatomical data. For each subject, sixteen cerebral arteries were manually labeled to construct the atlas. Angiographic data were normalized to MNI space using both rigid-body and non-linear transformations obtained from anatomical images. The alignment of arterial branches was significantly improved by the non-linear normalization (p < 0.001). Validation of the atlas was based on its applicability in automatic arterial labeling. A leave-one-out validation scheme revealed a labeling accuracy of 96 %. Arterial labeling was also performed in a separate clinical sample (n = 10) with an accuracy of 92.5 %. In conclusion, using non-linear spatial normalization we constructed an artery-specific probabilistic atlas, useful for cerebral arterial labeling.
改进后的全脑血管造影和速度敏感磁共振成像（MRI）正在拓展无创获取脑血管血流信息的边界。此类数据集中所包含信息的复杂性需要自动化算法和流程，从而减少训练有素的放射科医生进行手动分析的需求。这项工作的目的是通过构建和评估一个描述大脑主要动脉形状和位置的概率图谱，为这种自动化流程奠定基础。具体而言，我们研究了在蒙特利尔神经病学研究所（MNI）空间中实施非线性归一化如何改善个体动脉分支的对齐。在一个基于人群的队列中，有167名年龄在64 - 68岁的受试者，我们进行了全脑容积覆盖的4D流动MRI，生成了血管造影和解剖学数据。对于每个受试者，手动标记了16条脑动脉以构建图谱。使用从解剖图像获得的刚体和非线性变换将血管造影数据归一化到MNI空间。非线性归一化显著改善了动脉分支的对齐（p < 0.001）。图谱的验证基于其在自动动脉标记中的适用性。留一法验证方案显示标记准确率为96%。在一个单独的临床样本（n = 10）中进行的动脉标记准确率为92.5%。总之，使用非线性空间归一化，我们构建了一个特定于动脉的概率图谱，可用于脑动脉标记。
