Title
More accurate estimation of diffusion tensor parameters using diffusion kurtosis imagingMore accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging
Author
Faculty/Department
Faculty of Sciences. Physics
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Research group
Vision lab
Bio-Imaging lab
Publication type
article
Publication
Orlando, Fla,
Subject
Physics
Human medicine
Source (journal)
Magnetic resonance in medicine. - Orlando, Fla
Volume/pages
65(2011):1, p. 138-145
ISSN
0740-3194
ISI
000285963500015
Carrier
E
Target language
Dutch (dut)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
With diffusion tensor imaging, the diffusion of water molecules through brain structures is quantified by parameters, which are estimated assuming monoexponential diffusion-weighted signal attenuation. The estimated diffusion parameters, however, depend on the diffusion weighting strength, the b-value, which hampers the interpretation and comparison of various diffusion tensor imaging studies. In this study, a likelihood ratio test is used to show that the diffusion kurtosis imaging model provides a more accurate parameterization of both the Gaussian and non-Gaussian diffusion component compared with diffusion tensor imaging. As a result, the diffusion kurtosis imaging model provides a b-value-independent estimation of the widely used diffusion tensor parameters as demonstrated with diffusion-weighted rat data, which was acquired with eight different b-values, uniformly distributed in a range of [0,2800 sec/mm2]. In addition, the diffusion parameter values are significantly increased in comparison to the values estimated with the diffusion tensor imaging model in all major rat brain structures. As incorrectly assuming additive Gaussian noise on the diffusion-weighted data will result in an overestimated degree of non-Gaussian diffusion and a b-value-dependent underestimation of diffusivity measures, a Rician noise model was used in this study.
E-info
https://repository.uantwerpen.be/docman/iruaauth/9179c8/e528935bc30.pdf
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