Fluid registration of diffusion tensor images using information theory

被引：81

作者：

Chiang, Ming-Chang ^{[1
]}

Leow, Alex D. ^{[1
]}

Klunder, Andrea D. ^{[1
]}

Dutton, Rebecca A. ^{[1
]}

Barysheva, Marina ^{[1
]}

Rose, Stephen E. ^{[2
]}

McMahon, Katie L. ^{[2
]}

de Zubicaray, Greig I. ^{[2
]}

Toga, Arthur W. ^{[1
]}

Thompson, Paul M. ^{[1
]}

机构：

[1] Univ Calif Los Angeles, Sch Med, Dept Neurol, Lab Neuro Imaging, Los Angeles, CA 90095 USA

[2] Univ Queensland, Ctr Magnet Resonance, Brisbane, Qld 4072, Australia

来源：

IEEE TRANSACTIONS ON MEDICAL IMAGING | 2008年 / 27卷 / 04期

关键词：

diffusion tensor imaging (DTI); fluid registration; high angular resolution diffusion imaging (HARDI); Kullback-Leibler divergence;

D O I：

10.1109/TMI.2007.907326

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：442 / 456

页数：15

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