Log-Domain Diffeomorphic Registration of Diffusion Tensor Images

Diffusion tensor imaging provides information about deep white matter anatomy that structural magnetic resonance images typically fail to resolve. Non-linear registration of diffusion tensor images, for which a few methods already exist, allows us to capture the deformations of these structures that would otherwise go unobserved. Here, we build on an existing method for diffeomorphic registration of diffusion tensor images, so that it fully incorporates the useful log-domain parameterization of diffeomorphisms. Initially, this allows us to easily include a registration symmetry constraint that is highly desirable for pair-wise registration. More importantly, the parameterization allows simple and proper calculation of statistics on the transformations obtained. We show that the symmetric log-domain method exhibits the most preferable trade-off between image correspondence and deformation smoothness on real data and also achieves the best recovery of synthetic warps.