Incorporating in vivo and ex vivo NMR sources of information for modeling robust brain tumor classifiers

The purpose of this paper is to investigate the potential and limitations of using multimodal sources of information coming from in vivo NMR and ex vivo NMR data for detecting brain tumors. Supervised pattern recognition methods, whose performance directly depends on the prior available observations used in building them, are proposed. We show that high resolution magic angle spinning (HR-MAS) data act as complementary information for classifying magnetic resonance spectroscopic imaging (MRSI) data. In particularly, when considering rare brain tumors, since it is unlikely to acquire sufficient cases to define their metabolite profiles using only in vivo NMR information, HR-MAS can support the classification procedure. We describe different approaches to combine HRMAS data with in vivo MRSI and magnetic resonance imaging (MRI) data and investigate which parameters influence the classification results by means of extensive simulations and in vivo studies.