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Computational and Mathematical Methods in Medicine
Volume 2012, Article ID 820847, 7 pages
http://dx.doi.org/10.1155/2012/820847
Research Article

Parametric Mapping of Brain Tissues from Diffusion Kurtosis Tensor

1Department of Biomedical Engineering, Tianjin University, Tianjin 300072, China
2Department of Radiology, Tianjin First Center Hospital, Tianjin 300192, China

Received 9 May 2012; Revised 21 July 2012; Accepted 24 July 2012

Academic Editor: Huafeng Liu

Copyright © 2012 Yuanyuan Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Diffusion kurtosis imaging (DKI) is a new diffusion magnetic resonance imaging (MRI) technique to go beyond the shortages of conventional diffusion tensor imaging (DTI) from the assumption that water diffuse in biological tissue is Gaussian. Kurtosis is used to measure the deviation of water diffusion from Gaussian model, which is called non-Gaussian, in DKI. However, the high-order kurtosis tensor in the model brings great difficulties in feature extraction. In this study, parameters like fractional anisotropy of kurtosis eigenvalues (FAek) and mean values of kurtosis eigenvalues (Mek) were proposed, and regional analysis was performed for 4 different tissues: corpus callosum, crossing fibers, thalamus, and cerebral cortex, compared with other parameters. Scatterplot analysis and Gaussian mixture decomposition of different parametric maps are used for tissues identification. Diffusion kurtosis information extracted from kurtosis tensor presented a more detailed classification of tissues actually as well as clinical significance, and the FAek of 𝐷-eigenvalues showed good sensitivity of tissues complexity which is important for further study of DKI.