title: A hybrid method of application of independent component analysis to in vivo 1H MR spectra of childhood brain tumours.
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).
authors.affiliation: Biomedical Informatics, Signals and Systems Research Laboratory, School of Electronic, Electrical and Computer Engineering, University of Birmingham, Birmingham, UK.
authors.forename: Jie
authors.initials: J
authors.lastname: Hao
authors.forename: Xin
authors.initials: X
authors.lastname: Zou
authors.forename: Martin
authors.initials: M
authors.lastname: Wilson
authors.forename: Nigel P
authors.initials: NP
authors.lastname: Davies
authors.forename: Yu
authors.initials: Y
authors.lastname: Sun
authors.forename: Andrew C
authors.initials: AC
authors.lastname: Peet
authors.forename: Theodoros N
authors.initials: TN
authors.lastname: Arvanitis
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D002648:Child; D002675:Child, Preschool; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009369:Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor