Fluid Registration of Diffusion Tensor Images Using Information Theory

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 large-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 ...

• Type: Journal
• Year: 2008
• Where: TMI
• Authors: Ming-Chang Chiang, Alex D. Leow, Andrea D. Klunder, Rebecca A. Dutton, Marina Barysheva, Stephen E. Rose, Katie McMahon, Greig I. de Zubicaray, Arthur W. Toga, Paul M. Thompson
• Keywords: Diffusion, Diffusion Tensors, Image, TMI 2008