Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15081
Title: Positive definiteness of Diffusion Kurtosis Imaging
Authors: Hu, S
Huang, ZH
Ni, HY
Qi, L 
Keywords: Conic linear programming
Diffusion kurtosis tensor
Positive definiteness
Issue Date: 2012
Source: Inverse problems and imaging, 2012, v. 6, no. 1, p. 57-75 How to cite?
Journal: Inverse Problems and Imaging 
Abstract: Difiusion Kurtosis Imaging (DKI) is a new Magnetic Resonance Imaging (MRI) model to characterize the non-Gaussian difiusion behavior in tissues. In reality, the term, in the extended Stejskal and Tanner equation of DKI should be positive for an appropriate range of b-values to make sense physically. The positive definiteness of the above term reects the signal attenuation in tissues during imaging. Hence, it is essential for the validation of DKI. In this paper, we analyze the positive definiteness of DKI. We first characterize the positive definiteness of DKI through the positive definiteness of a tensor constructed by difiusion tensor and difiusion kurtosis tensor. Then, a conic linear optimization method and its simplified version are proposed to handle the positive definiteness of DKI from the perspective of numerical computation. Some preliminary numerical tests on both synthetical and real data show that the method discussed in this paper is promising.
URI: http://hdl.handle.net/10397/15081
ISSN: 1930-8337
DOI: 10.3934/ipi.2012.6.57
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