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Independent component analysis for automated decomposition of in vivo magnetic resonance spectra
Author(s) -
Ladroue Christophe,
Howe Franklyn A.,
Griffiths John R.,
Tate A. Rosemary
Publication year - 2003
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.10595
Subject(s) - independent component analysis , pattern recognition (psychology) , artificial intelligence , computer science , set (abstract data type) , principal component analysis , component (thermodynamics) , biological system , blind signal separation , spectral line , decomposition , chemistry , physics , biology , computer network , channel (broadcasting) , organic chemistry , astronomy , thermodynamics , programming language
Abstract Fully automated methods for analyzing MR spectra would be of great benefit for clinical diagnosis, in particular for the extraction of relevant information from large databases for subsequent pattern recognition analysis. Independent component analysis (ICA) provides a means of decomposing signals into their constituent components. This work investigates the use of ICA for automatically extracting features from in vivo MR spectra. After its limits are assessed on artificial data, the method is applied to a set of brain tumor spectra. ICA automatically, and in an unsupervised fashion, decomposes the signals into interpretable components. Moreover, the spectral decomposition achieved by the ICA leads to the separation of some tissue types, which confirms the biochemical relevance of the components. Magn Reson Med 50:697–703, 2003. © 2003 Wiley‐Liss, Inc.