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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 adoi:10.1109/tmi.2007.907326 pmid:18390342 pmcid:PMC2770435 fatcat:mqa7opljn5cv7kmcuvakdg4tji