Spatial normalization of diffusion tensor MRI using multiple channels

Abstract

Diffusion Tensor MRI (DT-MRI) can provide important in vivo information for the detection of brain abnormalities in diseases characterized by compromised neural connectivity. To quantify diffusion tensor abnormalities based on voxel-based statistical analysis, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration algorithm based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration. Registration was performed on 16 DT-MRI data sets using different combinations of the channels, including a channel of T2-weighted intensity, a channel of the fractional anisotropy, a channel of the difference of the first and second eigenvalues, two channels of the fractional anisotropy and the trace of tensor, three channels of the eigenvalues of the tensor, and the six channel tensor components. To evaluate the registration of tensor data, we defined two similarity measures, i.e., the endpoint divergence and the mean square error, which we applied to the fiber bundles of target images and registered images at the same seed points in white matter segmentation. We also evaluated the tensor registration by examining the voxel-by-voxel alignment of tensors in a sample of 15 normalized DTMRIs. In all evaluations, nonlinear warping using six independent tensor components as input channels showed the best performance in effectively normalizing the tract morphology and tensor orientation. We also present a nonlinear method for creating a group diffusion tensor atlas using the average tensor field and the average deformation field, which we believe is a better approach than a strict linear one for representing both tensor distribution and morphological distribution of the population.

A review of diffusion tensor imaging studies in schizophrenia

“…Since T2 structural images do not include information relevant to the orientation and direction of the fiber tracts, the registration of these types of images to the atlas template usually performs poorly, with significant residual miss-registration error due to differences in fiber tracts location, shape and size, as well as anatomical variability that can not be captured with information provided by structural images. For this reason, voxelbased quantification of diffusion tensor images requires more sophisticated spatial normalization that takes advantage of the directional information provided by the full tensor (Park et al, 2003).…”

“…6 provides an example of work that will likely assist us in evaluating fiber bundles in patients with schizophrenia. Here, automated fiber tracking procedure (described in detail in Park et al, 2003) has been used to create major white matter fiber tracts. In brief, all white matter voxels have been used as seeding points, and then fiber bundles were created by following the largest eigenvectors of the diffusion tensor using the Runge-Kutta algorithm (Basser et al, 2000).…”

Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts

“…1 shows the distance map and label map constructed from a sample 2-D curve The label map partitions the space into regions that each correspond to a point of the center. Now, for every curve, r i = {r ij } in the space, the distance to the center μ k can be measured simply as: (4) and by projecting it onto the label map, its point correspondence to the center is readily achieved.…”

“…Such methods are sensitive to the accuracy of specifying the ROIs and are prone to user errors. Others have performed a voxel-based analysis of a registered DTI dataset, which requires non-linear warping of the tensor field [2], which in turn needs re-orientation of the tensors [3,4].…”

Multimodal Image Registration by Information Fusion at Feature Level

“…It is also efficient, as registrations on all modalities are produced at the same time. In [1], Park et al proposed a deformable multimodal image registration method using multichannel demons [2], in which T2 and DTI were registered as a vector image. In this straightforward method, although every image channel is incorporated, they are assigned equal importance in the image matching.…”