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Short echo time MR spectroscopy of brain tumors: Grading of cerebral gliomas by correlation analysis of normalized spectral amplitudes
Author(s) -
Weis Jan,
Ring Patrik,
Olofsson Tommie,
OrtizNieto Francisco,
Wikström Johan
Publication year - 2010
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.21991
Subject(s) - spectral line , grading (engineering) , nuclear magnetic resonance , correlation , correlation coefficient , glioma , amplitude , nuclear medicine , mathematics , physics , medicine , statistics , optics , biology , ecology , geometry , cancer research , astronomy
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. J. Magn. Reson. Imaging 2010;31:39–45. © 2009 Wiley‐Liss, Inc.