Short echo time MR spectroscopy of brain tumors: grading of cerebral gliomas by correlation analysis of normalized spectral amplitudes.

PURPOSE: To process single voxel spectra of low- and high-grade gliomas. To propose correlation analysis of the scatter plots of normalized spectral amplitudes as a pattern recognition tool for the classification (grading) of brain tumors. To propose a spectrum processing approach that improves the differentiation of proton spectra with dominating macromolecule and lipid peaks. MATERIALS AND METHODS: LCModel was used to process spectra. Mean metabolite concentrations and mean normalized spectra were obtained for normal white matter and for gliomas. The mean spectra of macromolecules and lipids (ML) in the range 1.4-0.9 ppm, and mean difference spectra (DS) without ML and lactate were computed. Correlation analysis of the scatter plot of the patient and mean normalized spectral amplitudes and dispersion of the scatter plot points were used for classification and grading of tumors. RESULTS: It was found advantageous to perform the classifications using DS spectra. The shape of ML spectrum and concentration of tCr seem to be a good markers for glioma grade. CONCLUSION: Combining a qualitative comparison of the patient and mean DS spectra of the tumors using correlation analysis of normalized spectra amplitudes with a quantitative comparison of metabolite concentrations is a powerful tool in studying brain lesions.