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BioMed Research International
Volume 2014 (2014), Article ID 762126, 10 pages
http://dx.doi.org/10.1155/2014/762126
Research Article

Data Analysis and Tissue Type Assignment for Glioblastoma Multiforme

1School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2Department of Radiology and Department of Imaging and Pathology, University Hospitals of Leuven, 3001 Leuven, Belgium

Received 18 November 2013; Revised 13 January 2014; Accepted 23 January 2014; Published 3 March 2014

Academic Editor: Bairong Shen

Copyright © 2014 Yuqian Li 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

Glioblastoma multiforme (GBM) is characterized by high infiltration. The interpretation of MRSI data, especially for GBMs, is still challenging. Unsupervised methods based on NMF by Li et al. (2013, NMR in Biomedicine) and Li et al. (2013, IEEE Transactions on Biomedical Engineering) have been proposed for glioma recognition, but the tissue types is still not well interpreted. As an extension of the previous work, a tissue type assignment method is proposed for GBMs based on the analysis of MRSI data and tissue distribution information. The tissue type assignment method uses the values from the distribution maps of all three tissue types to interpret all the information in one new map and color encodes each voxel to indicate the tissue type. Experiments carried out on in vivo MRSI data show the feasibility of the proposed method. This method provides an efficient way for GBM tissue type assignment and helps to display information of MRSI data in a way that is easy to interpret.