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A hybrid method of application of independent component analysis to in vivo 1 H MR spectra of childhood brain tumours
Author(s) -
Hao Jie,
Zou Xin,
Wilson Martin,
Davies Nigel P.,
Sun Yu,
C. Peet Andrew,
N. Arvanitis Theodoros
Publication year - 2012
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.1776
Subject(s) - independent component analysis , metabolite , principal component analysis , pattern recognition (psychology) , biological system , spectral line , in vivo , computer science , artificial intelligence , chemistry , physics , biology , biochemistry , astronomy , microbiology and biotechnology
Independent component analysis (ICA) can automatically extract individual metabolite, macromolecular and lipid (MMLip) components from a series of in vivo MR spectra. The traditional feature extraction (FE)‐based ICA approach is limited, in that a large sample size is required and a combination of metabolite and MMLip components can appear in the same independent component. The alternative ICA approach, based on blind source separation (BSS), is weak when dealing with overlapping peaks. Combining the advantages of both BSS and FE methods may lead to better results. Thus, we propose an ICA approach involving a hybrid of the BSS and FE techniques for the automated decomposition of a series of MR spectra. Experiments were performed on synthesised and patient in vivo childhood brain tumour MR spectra datasets. The hybrid ICA method showed an improvement in the decomposition ability compared with BSS‐ICA or FE‐ICA, with an increased correlation between the independent components and simulated metabolite and MMLip signals. Furthermore, we were able to automatically extract metabolites from the patient MR spectra dataset that were not in commonly used basis sets (e.g. guanidinoacetate). Copyright © 2011 John Wiley & Sons, Ltd.