Log-Domain Diffeomorphic Registration of Diffusion Tensor Images

Andrew Sweet (1) , Xavier Pennec (1)

1 ASCLEPIOS - Analysis and Simulation of Biomedical 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.

Mots clés: diffusion tensor imaging; computational anatomy; diffeomorphic registration; exponential map

Domaines: Imagerie médicale; Modélisation et simulation; Imagerie; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Andrew Sweet, Xavier Pennec. Log-Domain Diffeomorphic Registration of Diffusion Tensor Images. WBIR 2010 - International Workshop on Biomedical Image Registration , Jul 2010, Lübeck, Germany. pp.198-209, ⟨10.1007/978-3-642-14366-3_18⟩. ⟨inria-00616163⟩