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Hierarchical non‐negative matrix factorization (hNMF): a tissue pattern differentiation method for glioblastoma multiforme diagnosis using MRSI
Author(s) -
Li Yuqian,
Sima Diana M.,
Cauter Sofie Van,
Croitor Sava Anca R.,
Himmelreich Uwe,
Pi Yiming,
Van Huffel Sabine
Publication year - 2013
Publication title -
nmr in biomedicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.278
H-Index - 114
eISSN - 1099-1492
pISSN - 0952-3480
DOI - 10.1002/nbm.2850
Subject(s) - non negative matrix factorization , matrix decomposition , glioblastoma , voxel , in vivo , computer science , matrix (chemical analysis) , pattern recognition (psychology) , artificial intelligence , medicine , cancer research , biology , chemistry , physics , eigenvalues and eigenvectors , microbiology and biotechnology , quantum mechanics , chromatography
MRSI has shown potential in the diagnosis and prognosis of glioblastoma multiforme (GBM) brain tumors, but its use is limited by difficult data interpretation. When the analyzed MRSI data present more than two tissue patterns, conventional non‐negative matrix factorization (NMF) implementation may lead to a non‐robust estimation. The aim of this article is to introduce an effective approach for the differentiation of GBM tissue patterns using MRSI data. A hierarchical non‐negative matrix factorization (hNMF) method that can blindly separate the most important spectral sources in short‐TE 1 H MRSI data is proposed. This algorithm consists of several levels of NMF, where only two tissue patterns are computed at each level. The method is demonstrated on both simulated and in vivo short‐ TE 1 H MRSI data in patients with GBM. For the in vivo study, the accuracy of the recovered spectral sources was validated using expert knowledge. Results show that hNMF is able to accurately estimate the three tissue patterns present in the tumoral and peritumoral area of a GBM, i.e. normal, tumor and necrosis, thus providing additional useful information that can help in the diagnosis of GBM. Moreover, the hNMF results can be displayed as easily interpretable maps showing the contribution of each tissue pattern to each voxel. Copyright © 2012 John Wiley & Sons, Ltd.