Infant brain probability templates for MRI segmentation and normalization.
Infant brain probability templates for MRI segmentation and normalization.
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DOI：
10.1016/j.neuroimage.2008.07.060
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发表时间：
2008-12
期刊：
影响因子：
5.7
通讯作者：
Gaser, Christian
中科院分区：
文献类型：
--
作者：
Altaye, Mekibib;Holland, Scott K.;Wilke, Marko;Gaser, Christian
由于婴儿输入数据和参考数据之间的发育差异，基于成人或儿科参考数据的婴儿脑MRI数据的空间归一化和分割可能是不合适的。在这项研究中，我们构建了婴儿模板和先验脑组织概率地图的基础上，从76个婴儿的年龄范围从9个月到15个月的MR脑图像数据。我们采用了两种处理策略来构建婴儿模板和先验数据：一个处理与一个不使用先验数据的分割步骤。使用我们构建的模板，成人模板和新的婴儿模板之间的比较。组织分布的差异是明显的婴儿和成人之间的模板，特别是在灰质（GM）的地图。婴儿先验信息将脑组织分类为GM的概率比成人数据高，代价是白色物质（WM），其在与成人数据相比时以较低的概率呈现。这种差异在额叶和扣带回更为明显。当婴儿数据与儿科（5至18岁）模板进行比较时，也观察到类似的差异。在此采用的婴儿T1 W脑图像的两遍分割方法为婴儿脑图像中的GM、WM和CSF提供了高质量的组织概率图。这些模板可用作分割和归一化的先验概率分布;这是提高这些程序在特殊人群中的准确性的关键。
Spatial normalization and segmentation of infant brain MRI data based on adult or pediatric reference data may not be appropriate due to the developmental differences between the infant input data and the reference data. In this study we have constructed infant templates and a priori brain tissue probability maps based on the MR brain image data from 76 infants ranging in age from 9 to 15 months. We employed two processing strategies to construct the infant template and a priori data: one processed with and one without using a priori data in the segmentation step. Using the templates we constructed, comparisons between the adult templates and the new infant templates are presented. Tissue distribution differences are apparent between the infant and adult template, particularly in the gray matter (GM) maps. The infant a priori information classifies brain tissue as GM with higher probability than adult data, at the cost of white matter (WM), which presents with lower probability when compared to adult data. The differences are more pronounced in the frontal regions and in the cingulate gyrus. Similar differences are also observed when the infant data is compared to a pediatric (age 5 to 18) template. The two-pass segmentation approach taken here for infant T1W brain images has provided high-quality tissue probability maps for GM, WM, and CSF, in infant brain images. These templates may be used as prior probability distributions for segmentation and normalization; a key to improving the accuracy of these procedures in special populations.
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