6533b826fe1ef96bd1283eb4
RESEARCH PRODUCT
Fast nosological imaging using canonical correlation analysis of brain data obtained by two-dimensional turbo spectroscopic imaging.
M. Carmen Martínez-bisbalBernardo CeldaTeresa LaudadioTeresa LaudadioSabine Van Huffelsubject
Diagnostic ImagingMagnetic Resonance SpectroscopyComputer scienceTurboNuclear magnetic resonanceRobustness (computer science)medicineHumansRadiology Nuclear Medicine and imagingComputer SimulationSpatial analysisSpectroscopyTissue segmentationbiologybusiness.industryBrainPattern recognitionbiology.organism_classificationmedicine.diseaseCorrelation analysisMr spectroscopic imagingMolecular MedicineArtificial intelligenceCanonical correlationbusinessGlioblastomaGlioblastomadescription
A new fast and accurate tissue typing technique has recently been successfully applied to prostate MR spectroscopic imaging (MRSI) data. This technique is based on canonical correlation analysis (CCA), a statistical method able to simultaneously exploit the spectral and spatial information characterizing the MRSI data. Here, the performance of CCA is further investigated by using brain data obtained by two-dimensional turbo spectroscopic imaging (2DTSI) from patients affected by glioblastoma. The purpose of this study is to investigate the applicability of CCA when typing tissues of heterogeneous tumors. The performance of CCA is also compared with that of ordinary correlation analysis on simulated as well as in vivo data. The results show that CCA outperforms ordinary correlation analysis in terms of robustness and accuracy. Copyright © 2007 John Wiley & Sons, Ltd.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2007-10-02 | NMR in biomedicine |