| A hybrid method of application of independent component analysis to in vivo (1) H MR spectra of childhood brain tumours. | |
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MedLine Citation:
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PMID: 21960131 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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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. |
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Authors:
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Jie Hao; Xin Zou; Martin Wilson; Nigel P Davies; Yu Sun; Andrew C Peet; Theodoros N Arvanitis |
Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-9-30 |
Journal Detail:
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Title: NMR in biomedicine Volume: - ISSN: 1099-1492 ISO Abbreviation: - Publication Date: 2011 Sep |
Date Detail:
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Created Date: 2011-9-30 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8915233 Medline TA: NMR Biomed Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Copyright Information:
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Copyright © 2011 John Wiley & Sons, Ltd. |
Affiliation:
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Biomedical Informatics, Signals and Systems Research Laboratory, School of Electronic, Electrical and Computer Engineering, University of Birmingham, Birmingham, UK. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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