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. 2006 Jul 5:2006:67.
doi: 10.1109/CVPRW.2006.65.

Diffeomorphic Matching of Diffusion Tensor Images

Yan Cao  1 , Michael I Miller, Susumu Mori, Raimond L Winslow, Laurent Younes
Affiliations

Diffeomorphic Matching of Diffusion Tensor Images

Yan Cao et al. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. .

Abstract

This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of tensor fields on the image volume, resulting in optimizing for geodesics on the space of diffeomorphisms connecting two diffusion tensor images. A coarse to fine multi-resolution and multi-kernel-width scheme is detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI brain and images.

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Figures

Figure 1
Figure 1
3D tensor matching of two normal human brains. Left column shows the tensor distribution of slice 30 before matching; Right column shows the tensor distribution of slice 30 after matching. Template and target are superimposed with blue color the template and red color the target. First row shows the whole slice. Second row and third row shows the enlargement of region A and region B respectively.
Figure 2
Figure 2
Comparison of the deformed template and the target for different LDDMM matching schemes. Top left panel shows the histogram of the tensor difference (Frobenius norm) at each voxel. Top right panel shows the histogram of the FA difference at each voxel. Bottom left panel shows the mean diffusion tensor difference (Frobenius norm) between corresponding voxels as function of FA value. Bottom right panel shows the mean difference between corresponding principal eigenvectors as function of FA value.
Figure 3
Figure 3
Comparison of the deformations result from LDDMM vector matching and tensor matching schemes. Top panel shows the FA weighted color-coded orientation map of the target. Second row shows the deformations from vector matching, third row shows the deformation from tensor matching. First column shows the the determinant of the Jacobian matrix.. Second column shows the rotation part of the Jacobian matrix, the rotation angle in degree. Third column shows the normalized difference of the eigenvalues of the Jacobian matrix. Tensor matching scheme improves the matching quality in areas with low FA values.
Figure 4
Figure 4
Left column shows the histogram of 3 tensor eigenvalues for several normal human brain DT-MRI datasets, right column shows the histogram of 3 tensor eigenvalues for several normal canine heart DT-MRI datasets. Same color means the same dataset. This figure shows sometimes it necessary to normalize the eigenvalues of the tensors before matching.
See this image and copyright information in PMC

References

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