MacKenzie-Graham Allan, Boline Jyl, Toga Arthur W
Laboratory of Neuro Imaging, Department of Neurology, University of California, Los Angeles, CA, USA.
Methods Mol Biol. 2007;401:183-94. doi: 10.1007/978-1-59745-520-6_11.
Quantifying the effect of a genetic manipulation or disease is a complicated process in a population of animals. Probabilistic brain atlases can capture population variability and be used to quantify those variations in anatomy as measured by structural imaging. Minimum deformation atlases (MDAs), a subclass of probabilistic atlases, are intensity-based averages of a collection of scans in a common space unbiased by selection of a single target image. Here, we describe a method for generating an MDA from a set of magnetic resonance microscopy images. First, the images are segmented to remove any non-brain tissue and bias field corrected to remove field inhomogeneities. The corrected images are then linearly aligned to a representative scan, the geometric mean of all the transformations is calculated, and a minimum deformation target (MDT) is produced by averaging the volumes in this new space. The brains are then non-linearly aligned to the MDT to produce the MDA. Finally, the images are linearly aligned to the MDA using a full-affine transformation to spatially and intensity normalize them, removing global differences in size, shape, and position but retaining anatomically significant differences.
在动物群体中，量化基因操作或疾病的影响是一个复杂的过程。概率性脑图谱可以捕捉群体变异性，并用于量化通过结构成像测量的解剖学变异。最小变形图谱（MDA）是概率性图谱的一个子类，它是在公共空间中一组扫描的基于强度的平均值，不受单个目标图像选择的影响。在这里，我们描述了一种从一组磁共振显微镜图像生成MDA的方法。首先，对图像进行分割以去除任何非脑组织，并进行偏置场校正以去除场不均匀性。然后将校正后的图像线性对齐到代表性扫描，计算所有变换的几何平均值，并通过在这个新空间中平均体积来生成最小变形目标（MDT）。然后将大脑非线性对齐到MDT以生成MDA。最后，使用全仿射变换将图像线性对齐到MDA，以在空间和强度上对它们进行归一化，消除大小、形状和位置上的全局差异，但保留解剖学上的显著差异。
