Fluid registration of diffusion tensor images using information theory
Fluid registration of diffusion tensor images using information theory
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DOI：
10.1109/tmi.2007.907326
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发表时间：
2008-04-01
影响因子：
10.6
通讯作者：
Thompson, Paul M.
中科院分区：
文献类型：
--
作者：
Chiang, Ming-Chang;Leow, Alex D.;Thompson, Paul M.
We apply an information -theoretic cost metric, the symmetrized Kullback-Leibler (sKL) divergence, or J-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 J-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.
