Group-wise diffeomorphic diffusion tensor image registration

We propose an unbiased group-wise diffeomorphic registration technique to normalize a group of diffusion tensor (DT) images. Our method uses an implicit reference group-wise registration framework to avoid bias caused by reference selection. Log-Euclidean metrics on diffusion tensors are used for the tensor interpolation and computation of the similarity cost functions. The overall energy function is constructed by a diffeomorphic demons approach. The tensor reorientation is performed and implicitly optimized during the registration procedure. The performance of the proposed method is compared with reference-based diffusion tensor imaging (DTI) registration methods. The registered DTI images have smaller shape differences in terms of reduced variance of the fractional anisotropy maps and more consistent tensor orientations. We demonstrate that fiber tract atlas construction can benefit from the group-wise registration by producing fiber bundles with higher overlaps. © 2010 Springer-Verlag.link_to_subscribed_fulltex