Spatial Normalization of Diffusion Tensor MRI Using Multiple Channels

H. J. Park, M. Kubicki, M. E. Shenton, A. Guimond, R. W. McCarley, S. E. Maier, R. Kikinis, F. A. Jolesz, C.-F. Westin
Neuroimage
Volume 20, Number 4, Pages 1995-2009
2003

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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 DT-MRIs. 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.

Fiber bundle maps of averaged DT-MRIs. Averages of 15 DT-MRIs (n =15) are created by calculating the mean and the median of nonlinearly transformed images (WARPMEAN and WARPMED respectively). A comparative fiber bundle map of a single brain is also displayed.

Reference

Park HJ, Kubicki M, Shenton ME, Guimond A, McCarley RW, Maier SE, Kikinis R, Jolesz FA, Westin CF. Spatial normalization of diffusion tensor MRI using multiple channels. Neuroimage 2003;20(4):1995-2009.

Bibtex entry

@Article{parkNeuroimage03,
  author         = {H. J. Park and M. Kubicki and M. E. Shenton and A. Guimond 
                   and  R. W. McCarley and S. E. Maier and R. Kikinis and F. A.
                   Jolesz  and C.-F. Westin},                                  
  title          = {Spatial Normalization of Diffusion Tensor {MRI} Using      
                   Multiple  Channels},                                        
  journal        = {Neuroimage},                                               
  year           = {2003},                                                     
  volume         = {20},                                                       
  number         = {4},                                                        
  pages          = {1995--2009}
}

Grants

NIH P41-RR13218 (NAC), NIH K02-MH01110, NIH R01-MH50747, NIH R01-MH40799, NIH R01-NS39335, NIH R01-RR11747, NIH P41-RR13218 (NAC)

Research areas

DTMRI, Tensor

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