Title
Optimal experimental design for diffusion kurtosis imaging Optimal experimental design for diffusion kurtosis imaging
Author
Faculty/Department
Faculty of Sciences. Physics
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Publication type
article
Publication
New York, N.Y. ,
Subject
Physics
Source (journal)
IEEE transactions on medical imaging / Institute of Electrical and Electronics Engineers [New York, N.Y.] - New York, N.Y.
Volume/pages
29(2010) :3 , p. 819-829
ISSN
0278-0062
ISI
000276308800020
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Diffusion kurtosis imaging (DKI) is a new magnetic resonance imaging (MRI) model that describes the non-Gaussian diffusion behavior in tissues. It has recently been shown that DKI parameters, such as the radial or axial kurtosis, are more sensitive to brain physiology changes than the well-known diffusion tensor imaging (DTI) parameters in several white and gray matter structures. In order to estimate either DTI or DKI parameters with maximum precision, the diffusion weighting gradient settings that are applied during the acquisition need to be optimized. Indeed, it has been shown previously that optimizing the set of diffusion weighting gradient settings can have a significant effect on the precision with which DTI parameters can be estimated. In this paper, we focus on the optimization of DKI gradients settings. Commonly, DKI data are acquired using a standard set of diffusion weighting gradients with fixed directions and with regularly spaced gradient strengths. In this paper, we show that such gradient settings are suboptimal with respect to the precision with which DKI parameters can be estimated. Furthermore, the gradient directions and the strengths of the diffusion-weighted MR images are optimized by minimizing the Crame¿r-Rao lower bound of DKI parameters. The impact of the optimized gradient settings is evaluated, both on simulated as well as experimentally recorded datasets. It is shown that the precision with which the kurtosis parameters can be estimated, increases substantially by optimizing the gradient settings.
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