Fluid registration of diffusion tensor images using information theory

Chiang, Ming-Chang, Leow, Alex D., Klunder, Andrea D., Dutton, Rebecca A., Barysheva, Marina, Rose, Stephen E., McMahon, Katie L., de Zubicaray, Greig I., Toga, Arthur W. and Thompson, Paul M. (2008) Fluid registration of diffusion tensor images using information theory. IEEE Transactions on Medical Imaging, 27 4: 442-456. doi:10.1109/TMI.2007.907326

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads

Author Chiang, Ming-Chang
Leow, Alex D.
Klunder, Andrea D.
Dutton, Rebecca A.
Barysheva, Marina
Rose, Stephen E.
McMahon, Katie L.
de Zubicaray, Greig I.
Toga, Arthur W.
Thompson, Paul M.
Title Fluid registration of diffusion tensor images using information theory
Journal name IEEE Transactions on Medical Imaging   Check publisher's open access policy
ISSN 0278-0062
Publication date 2008-04-01
Year available 2008
Sub-type Article (original research)
DOI 10.1109/TMI.2007.907326
Open Access Status DOI
Volume 27
Issue 4
Start page 442
End page 456
Total pages 15
Editor M. Vannier
Place of publication Piscataway NY, USA
Publisher IEEE
Language eng
Subject 291501 Clinical Engineering
730104 Nervous system and disorders
Abstract 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.
Keyword diffusion tensor imaging (DTI)
fluid registration
high angular resolution diffusion imaging (HARDI)
Kullback-Leibler divergence
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID R21 RR019771-02
Institutional Status UQ

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 70 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 79 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 06 May 2008, 19:53:49 EST by Lesley-Jayne Jerrard on behalf of Centre For Magnetic Resonance