Spatial normalization of diffusion tensor MRI using multiple channels

H. C. 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 1995-2000
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 rst 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 dened two similarity measures, i.e., the endpoint divergence and the mean square error, which we applied to the ber 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 eld and the average deformation eld, which we believe is a better approach than a strict linear one for representing both tensor distribution and morphological distribution of the population.


Reference

Park HC, 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):1995-2000.

Grants

KOSEF, NARSAD, NIH RO1 MH50747, NIH/NIMH 2K02 MH01110, NIH/NIMH 2RO1 MH40799, NIH RO1 NS39335, MES, RWM, NIH RO1 RR11747, NIH P41 RR13218

Research area

dti
© 2013 Psychiatry Neuroimaging Laboratory | Last updated 04.15.2013