Authors:
Yuqian Li
1
;
Diana M. Sima
2
;
Sofie Van Cauter
3
;
Uwe Himmelreich
4
;
Yiming Pi
5
and
Sabine Van Huffel
2
Affiliations:
1
University of Electronic Science and Technology of China, Katholieke Universiteit Leuven and IBBT-K.U.Leuven, China
;
2
Katholieke Universiteit Leuven and IBBT-K.U.Leuven, Belgium
;
3
University Hospitals of Leuven and Katholieke Universiteit Leuven, Belgium
;
4
Katholieke Universiteit Leuven, Belgium
;
5
University of Electronic Science and Technology of China, China
Keyword(s):
Non-negative Matrix Factorization (NMF), Blind Source Separation (BSS), Magnetic Resonance Spectroscopic Imaging (MRSI), Brain Glioma, Glioblastoma Multiforme (GBM).
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Detection, Acquisition, Analysis and Processing
Abstract:
The purpose of this paper is to introduce a hierarchical Non-negative Matrix Factorization (NMF) approach, customized for the problem of blindly separating brain glioma tumor tissue types using short-echo time proton magnetic resonance spectroscopic imaging (1H MRSI) signals. The proposed algorithm consists of two levels of NMF, where two constituent spectra are computed in each level. The first level is able to correctly detect the spectral profile corresponding to the most predominant tissue type, i.e., normal tissue, while the second level is optimized in order to detect two ‘abnormal’ spectral profiles so that the 3 recovered spectral profiles are least correlated with each other. The two-level decomposition is followed by the reestimation of the overall spatial distribution of each tissue type via standard Non-negative Least Square (NNLS). This method is demonstrated on in vivo short-TE 1H MRSI brain data of a glioblastoma multiforme patient and a grade II-III glioma patient. Th
e results show the possibility of differentiating normal tissue, tumor tissue and necrotic tissue in the form of recovered tissue-specific spectra with accurate spatial distributions.
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