Fluid Registration of Diffusion Tensor Images Using Information Theory

Ming-Chang Chiang, A.D. Leow, A.D. Klunder, R.A. Dutton, M. Barysheva, S.E. Rose, K.L. McMahon, G.I. de Zubicaray, A.W. Toga, P.M. Thompson
2008 IEEE Transactions on Medical Imaging  
We apply an information-theoretic cost metric, the symmetrized Kullback-Leibler (sKL) divergence, or -divergence, to fluid registration of diffusion tensor images. The difference between diffusion tensors is quantified based on the sKL-divergence of their associated probability density functions (PDFs). Three-dimensional DTI data from 34 subjects were fluidly registered to an optimized target image. To allow large image deformations but preserve image topology, we regularized the flow with a
more » ... ge-deformation diffeomorphic mapping based on the kinematics of a Navier-Stokes fluid. A driving force was developed to minimize the -divergence between the deforming source and target diffusion functions, while reorienting the flowing tensors to preserve fiber topography. In initial experiments, we showed that the sKL-divergence based on full diffusion PDFs is adaptable to higher-order diffusion models, such as high angular resolution diffusion imaging (HARDI). The sKL-divergence was sensitive to subtle differences between two diffusivity profiles, showing promise for nonlinear registration applications and multisubject statistical analysis of HARDI data. Index Terms-Diffusion tensor imaging (DTI), fluid registration, high angular resolution diffusion imaging (HARDI), Kullback-Leibler divergence.
doi:10.1109/tmi.2007.907326 pmid:18390342 pmcid:PMC2770435 fatcat:mqa7opljn5cv7kmcuvakdg4tji