A hybrid method of application of independent component analysis to in vivo 1H MR spectra of childhood brain tumours.
NMR Biomed
; 25(4): 594-606, 2012 Apr.
Article
en En
| MEDLINE
| ID: mdl-21960131
ABSTRACT
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).
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Algoritmos
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Imagen por Resonancia Magnética
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Biomarcadores de Tumor
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Diagnóstico por Computador
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Neoplasias
Tipo de estudio:
Diagnostic_studies
/
Evaluation_studies
Límite:
Child
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Child, preschool
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Humans
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Male
Idioma:
En
Revista:
NMR Biomed
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
/
MEDICINA NUCLEAR
Año:
2012
Tipo del documento:
Article
País de afiliación:
Reino Unido