Spatial Normalization and Averaging of Diffusion Tensor MRI Data Sets

Abstract

Diffusion tensor magnetic resonance imaging (DT-MRI) is unique in providing information about both the structural integrity and the orientation of white matter fibers in vivo and, through "tractography", revealing the trajectories of white matter tracts. DT-MRI is therefore a promising technique for detecting differences in white matter architecture between different subject populations. However, while studies involving analyses of group averages of scalar quantities derived from DT-MRI data have been performed, as yet there have been no similar studies involving the whole tensor. Here we present the first step towards realizing such a study, i.e., the spatial normalization of whole tensor data sets. The approach is illustrated by spatial normalization of 10 DT-MRI data sets to a standard anatomical template. Both qualitative and quantitative approaches are described for assessing the results of spatial normalization. Techniques are then described for combining the spatially normalized data sets according to three definitions of average, i.e., the mean, median, and mode of a distribution of tensors. The current absence of, and hence need for, appropriate statistical tests for comparison of results derived from group-averaged DT-MRI data sets is then discussed. Finally, the feasibility of performing tractography on the group-averaged DT-MRI data set is investigated and the possibility and implications of generating a generic map of brain connectivity from a group of subjects is considered. © 2002 Elsevier Science (USA)

Spatial normalization of diffusion tensor MRI using multiple channels

“…Fiber bundle maps of the average brains show much smoother tracts than a fiber bundle map of a single brain. As was noted in Jones et al (2002), most of the short fibers in the peripheral region of brain, such as the arcuate fibers, do not appear in the average brains due to smoothing effects caused by registration errors, intersubject variability, or both.…”

“…In terms of the alignment of the major eigenvector of the tensor, nonlinear normalization showed better performance than linear normalization. As mentioned by several investigators (Alexander and Gee, 2000;Jones et al, 2002), due to noise effects, there may exist a possibility of misorientation of the diffusion tensors in nonlinear high-dimensional warping. However, this potential misorientation does not necessarily pose a larger problem than the misalignment obtained by an affine transformation.…”

“…For averaging tensors at each voxel from the normalized diffusion images, we used both the mean and median of the tensors, as suggested by Jones et al (2002). The mean and the median of a sample of tensors are derived from the definition of Frechet (1948), where the central location is defined to minimize the sum of distances in the domain of the samples.…”

“…In making an atlas, Jones et al (2002) evaluated several averaging methods such as mean, median, and mode. The median tensor was obtained in order to be robust to outliers by finding a tensor that minimizes the distance to the sample of tensors in an absolute distance sense.…”

“…Jones and colleagues (Jones et al, 2002) created a diffusion tensor average brain by normalizing tensors with a linear transformation derived from the individual fractional anisotropy images. Their work demonstrates the feasibility and the importance of an atlas template derived from the studied population.…”