Department of Physics, University of Antwerp, Wilrijk, Belgium.
Hum Brain Mapp. 2010 Jan;31(1):98-114. doi: 10.1002/hbm.20848.
Voxel-based analysis (VBA) methods are increasingly being used to compare diffusion tensor image (DTI) properties across different populations of subjects. Although VBA has many advantages, its results are highly dependent on several parameter settings, such as those from the coregistration technique applied to align the data, the smoothing kernel, the statistics, and the post-hoc analyses. In particular, to increase the signal-to-noise ratio and to mitigate the adverse effect of residual image misalignments, DTI data are often smoothed before VBA with an isotropic Gaussian kernel with a full width half maximum up to 16 x 16 x 16 mm(3). However, using isotropic smoothing kernels can significantly partial volume or voxel averaging artifacts, adversely affecting the true diffusion properties of the underlying fiber tissue. In this work, we compared VBA results between the isotropic and an anisotropic Gaussian filtering method using a simulated framework. Our results clearly demonstrate an increased sensitivity and specificity of detecting a predefined simulated pathology when the anisotropic smoothing kernel was used.
体素基分析（VBA）方法越来越多地被用于比较不同受试者群体的扩散张量图像（DTI）特性。尽管 VBA 有许多优点，但它的结果高度依赖于几个参数设置，例如应用于对齐数据的配准技术、平滑核、统计数据和事后分析。特别是为了提高信噪比并减轻残余图像配准不良的不利影响，DTI 数据通常在进行 VBA 之前使用各向同性高斯核进行平滑处理，全宽半最大值高达 16 x 16 x 16 mm(3)。然而，使用各向同性平滑核会显著产生部分体积或体素平均伪影，从而对基础纤维组织的真实扩散特性产生不利影响。在这项工作中，我们使用模拟框架比较了各向同性和各向异性高斯滤波方法的 VBA 结果。我们的结果清楚地表明，当使用各向异性平滑核时，检测预定义模拟病变的灵敏度和特异性显著提高。
