Studholme C, Cardenas V, Maudsley A, Weiner M
Department of Radiology, VAMC (114Q), University of California-San Francisco, 4150 Clement Street, San Francisco, CA 94121, USA.
Neuroimage. 2003 Aug;19(4):1638-49. doi: 10.1016/s1053-8119(03)00183-6.
Deformation tensor morphometry makes use of the derivatives of spatial transformations between anatomies, to provide highly localized volumetric maps of relative anatomical size. The analysis of such maps, however, has the challenge of describing the data in a way that allows the spatial scale and extent of the local shape properties to match those induced by the disease process being studied. This study examines an approach to the spatial filtering of transformation Jacobian maps created in multisubject studies of brain anatomy, which constrains the filter neighborhood within common structural boundaries present in the spatially normalized image data. The filtering incorporates information derived from the spatial normalization process, using a statistical framework to introduce a measure of uncertainty in local regional intensity correspondence following spatial normalisation. The proposed filtering approach is compared to the use of spatially invariant Gaussian filtering in the analysis of Jacobian determinant maps of brain shape and shape change in Alzheimer's disease and normal aging. Results show significantly improved delineation of fine scale patterns of shape difference (in cross-sectional studies) and shape change (from multiple serial magnetic resonance imaging studies).
变形张量形态测量法利用不同解剖结构之间空间变换的导数，来提供相对解剖大小的高度局部化体积图。然而，对这些图的分析存在一个挑战，即要以一种能使局部形状属性的空间尺度和范围与所研究疾病过程所引发的尺度和范围相匹配的方式来描述数据。本研究探讨了一种对在多主体脑解剖学研究中创建的变换雅可比图进行空间滤波的方法，该方法将滤波邻域限制在空间归一化图像数据中存在的共同结构边界内。这种滤波纳入了从空间归一化过程中获得的信息，使用一个统计框架来引入空间归一化后局部区域强度对应关系中的不确定性度量。在对阿尔茨海默病和正常衰老过程中的脑形状及形状变化的雅可比行列式图进行分析时，将所提出的滤波方法与使用空间不变高斯滤波的方法进行了比较。结果表明，（在横断面研究中）形状差异的精细尺度模式以及（来自多个连续磁共振成像研究的）形状变化的描绘有了显著改善。
