Zhu Hongtu, Xu Dongrong, Raz Amir, Hao Xuejun, Zhang Heping, Kangarlu Alayar, Bansal Ravi, Peterson Bradley S
MRI Unit, Department of Psychiatry, Columbia University Medical Center, USA.
Magn Reson Imaging. 2006 Jun;24(5):569-82. doi: 10.1016/j.mri.2006.01.004. Epub 2006 Mar 20.
Tractography algorithms for diffusion tensor (DT) images consecutively connect directions of maximal diffusion across neighboring DTs in order to reconstruct the 3-dimensional trajectories of white matter tracts in vivo in the human brain. The performance of these algorithms, however, is strongly influenced by the amount of noise in the images and by the presence of degenerate tensors-- i.e., tensors in which the direction of maximal diffusion is poorly defined. We propose a simple procedure for the classification of tensor morphologies that uses test statistics based on invariant measures of DTs, such as fractional anisotropy, while accounting for the effects of noise on tensor estimates. Examining DT images from seven human subjects, we demonstrate that this procedure validly classifies DTs at each voxel into standard types (nondegenerate DTs, as well as degenerate oblate, prolate or isotropic DTs), and we provide preliminary estimates for the prevalence and spatial distribution of degenerate tensors in these brains. We also show that the P values for test statistics are more sensitive tools for classifying tensor morphologies than are invariant measures of anisotropy alone.
用于扩散张量（DT）图像的纤维束成像算法通过依次连接相邻DT中最大扩散方向，来重建人类大脑中活体白质纤维束的三维轨迹。然而，这些算法的性能受到图像噪声量以及退化张量（即最大扩散方向定义不明确的张量）的强烈影响。我们提出了一种简单的张量形态分类方法，该方法使用基于DT不变量测度（如分数各向异性）的检验统计量，同时考虑噪声对张量估计的影响。通过检查七名人类受试者的DT图像，我们证明该方法能够有效地将每个体素处的DT分类为标准类型（非退化DT以及退化的扁长、长球或各向同性DT），并提供了这些大脑中退化张量的发生率和空间分布的初步估计。我们还表明，与单独的各向异性不变量测度相比，检验统计量的P值是用于张量形态分类的更敏感工具。
