Spatial normalization of diffusion tensor MRI using multiple channels
Spatial normalization of diffusion tensor MRI using multiple channels
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DOI：
10.1016/j.neuroimage.2003.08.008
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发表时间：
2003-12-01
期刊：
影响因子：
5.7
通讯作者：
Westin, CF
中科院分区：
文献类型：
--
作者：
Park, HJ;Kubicki, M;Westin, CF
弥散张量MRI （DT-MRI）可以为检测以神经连接受损为特征的疾病中的大脑异常提供重要的体内信息。为了量化基于体素统计分析的扩散张量异常，需要空间归一化以最小化所研究脑结构之间的解剖差异。在本文中，我们使用了一种基于demons算法的多输入通道配准算法，并根据用于配准的输入信息评估了扩散张量图像的空间归一化。使用不同通道组合对16个DT-MRI数据集进行配准，包括一个t2加权强度通道、一个分数各向异性通道、一个第一和第二特征值之差通道、两个分数各向异性通道和张量轨迹通道、三个张量特征值通道和六个通道张量分量通道。为了评价张量数据的配准效果，我们定义了端点散度和均方误差两种相似性度量，分别应用于目标图像的纤维束和白质分割中相同种子点的配准图像。我们还通过在15个标准化的dt - mri样本中检查张量的逐体素对齐来评估张量配准。在所有评估中，使用六个独立张量分量作为输入通道的非线性翘曲在有效规范化束形态和张量方向方面表现最好。我们还提出了一种使用平均张量场和平均变形场创建群扩散张量图谱的非线性方法，我们认为这是一种比严格的线性方法更好的方法来表示种群的张量分布和形态分布。(C) 2003 Elsevier Inc.版权所有。
Diffusion Tensor MRI (DT-MRI) can provide important in vivo information for the detection of brain abnormalities in diseases characterized by compromised neural connectivity. To quantify diffusion tensor abnormalities based on voxel-based statistical analysis, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration algorithm based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration. Registration was performed on 16 DT-MRI data sets using different combinations of the channels, including a channel of T2-weighted intensity, a channel of the fractional anisotropy, a channel of the difference of the first and second eigenvalues, two channels of the fractional anisotropy and the trace of tensor, three channels of the eigenvalues of the tensor, and the six channel tenser components. To evaluate the registration of tensor data, we defined two similarity measures, i.e., the endpoint divergence and the mean square error, which we applied to the fiber bundles of target images and registered images at the same seed points in white matter segmentation. We also evaluated the tensor registration by examining the voxel-by-voxel alignment of tensors in a sample of 15 normalized DT-MRIs. In all evaluations, nonlinear warping using six independent tensor components as input channels showed the best performance in effectively normalizing the tract morphology and tensor orientation. We also present a nonlinear method for creating a group diffusion tensor atlas using the average tensor field and the average deformation field, which we believe is a better approach than a strict linear one for representing both tensor distribution and morphological distribution of the population. (C) 2003 Elsevier Inc. All rights reserved.