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RESEARCH PRODUCT
Quantifying brain tumor tissue abundance in HR-MAS spectra using non-negative blind source separation techniques
Anca Croitor SavaVicente EsteveM. Carmen Martínez-bisbalUwe HimmelreichDiana M. SimaSabine Van HuffelBernardo CeldaJorge Calvarsubject
Convex analysisApplied MathematicsAnalytical chemistryGlial tumorIndependent component analysisBlind signal separation030218 nuclear medicine & medical imagingAnalytical ChemistryMatrix decomposition03 medical and health sciences0302 clinical medicineDimension (vector space)Magic angle spinningSource separationBiological system030217 neurology & neurosurgeryMathematicsdescription
Given high-resolution magic angle spinning (HR-MAS) spectra from several glial tumor subjects, our goal is to differentiate between tumor tissue types by separating the different sources that contribute to the profile of each spectrum. Blind source separation techniques are applied for obtaining characteristic profiles for necrosis, highly cellular tumor and border tumor tissue and providing the contribution (abundance) of each of these tumor tissue types to the profile of each spectrum. The problem is formulated as a non-negative source separation problem. Non-negative matrix factorization, convex analysis of non-negative sources and non-negative independent component analysis methods are considered. The results are in agreement with the pathology obtained by the histopathological examination that succeeded the HR-MAS measurements. Furthermore, an analysis to verify to which extent the dimension of the input space, the input features and the number of sources to be extracted from the HR-MAS data could influence the performance of the source separation is presented. Copyright © 2012 John Wiley & Sons, Ltd.
year | journal | country | edition | language |
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2012-06-19 | Journal of Chemometrics |