Statistical analyses of brain surfaces using Gaussian random fields on 2-D manifolds
Statistical analyses of brain surfaces using Gaussian random fields on 2-D manifolds
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DOI：
10.1109/tmi.2006.884187
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发表时间：
2007-01-01
影响因子：
10.6
通讯作者：
Peterson, Bradley S.
中科院分区：
文献类型：
--
作者：
Bansal, Ravi;Staib, Lawrence H.;Peterson, Bradley S.
由于健康或疾病中大脑的生长或退化不仅影响大脑皮质和皮质下区域的体积，而且新的图像处理技术能够以惊人的精度检测形状或局部体积的微小和高度局部化的扰动，因此对大脑及其亚区的形态计量分析的兴趣最近增强。然而，大脑区域形状的适当统计表示对于检测、定位和解释其表面轮廓中的可变性以及识别在个体和个体群体中产生这种可变性的潜在组织的体积差异是必不可少的。我们对大脑区域形状的统计表示是由该区域的参考区域和整个区域表面定义的高斯随机场(GRF)定义的。我们首先从健康个体的一组分割的脑图像中选择一个参考区。然后，砂粒被估计为参考区域表面上的点与已经与参考区域共同配准的大脑图像中的对应区域上的对应点之间的带符号欧几里得距离。这些表面上的点之间的对应关系是通过使用流体动力学原理将大脑的每个区域变形到参考区域的坐标空间来定义的。然后，每个对象的扭曲的、共同配准的区域被解除扭曲到其自然空间，同时将当对象的表面和参考区域紧密地共同配准时建立的对应点的地图带入该空间。所提出的表面轮廓形状的统计描述除了关于区域或其GRF的形状的光滑性之外，不做任何假设。该描述还允许检测和定位在精细和粗略尺度上跨对象组的表面形状的统计上的显著差异。我们通过应用这些统计方法来研究大样本正常受试者和注意力缺陷/多动障碍(ADHD)受试者杏仁核和海马体形状的差异，从而证明了这些统计方法的有效性。
Interest in the morphometric analysis of the brain and its subregions has recently intensified because growth or degeneration of the brain in health or illness affects not only the volume but also the shape of cortical and subcortical brain regions, and new image processing techniques permit detection of small and highly localized perturbations in shape or localized volume, with remarkable precision. An appropriate statistical representation of the shape of a brain region is essential, however, for detecting, localizing, and interpreting variability in its surface contour and for identifying differences in volume of the underlying tissue that produce that variability across individuals and groups of individuals. Our statistical representation of the shape of a brain region is defined by a reference region for that region and by a Gaussian random field (GRF) that is defined across the entire surface of the region. We first select a reference region from a set of segmented brain images of healthy individuals. The GRIT is then estimated as the signed Euclidean distances between points on the surface of the reference region and the corresponding points on the corresponding region in images of brains that have been coregistered to the reference. Correspondences between points on these surfaces are defined through deformations of each region of a brain into the coordinate space of the reference region using the principles of fluid dynamics. The warped, coregistered region of each subject is then unwarped into its native space, simultaneously bringing into that space the map of corresponding points that was established when the surfaces of the subject and reference regions were tightly coregistered. The proposed statistical description of the shape of surface contours makes no assumptions, other than smoothness, about the shape of the region or its GRF. The description also allows for the detection and localization of statistically significant differences in the shapes of the surfaces across groups of subjects at both a fine and coarse scale. We demonstrate the effectiveness of these statistical methods by applying them to study differences in shape of the amygdala and hippocampus in a large sample of normal subjects and in subjects with attention deficit/hyperactivity disorder (ADHD).