RESUMO
PURPOSE: To examine preprocessing methods affecting the potential use of Magnetic Resonance Spectroscopy (MRS) as a noninvasive modality for detection and characterization of brain lesions and for directing therapy progress. MATERIALS AND METHODS: Two reference point re-calibration with linear interpolation (to compensate for magnetic field nonhomogeneity), weighting of spectra (to emphasize consistent peaks and depress chemical noise), and modeling based on chemical shift locations of 97 biomarkers were investigated. Results for 139 categorized scans were assessed by comparing Leave-One-Out (LOO) cross-validation and external validation. RESULTS: For distinction of nine brain tissue categories, use of re-calibration, variance weighting, and biomarker modeling improved LOO classification of MRS spectra from 31% to 95%. External validation of the two best nine-category models on 47 unknown samples gave 96% or 100% accuracy, respectively, compared with pathological diagnosis. CONCLUSION: Preprocessing of MRS spectra can significantly improve their diagnostic utility for automated consultation of pattern recognition models. Use of several techniques in combination greatly increases available proton MRS information content. Accurate assignment of unknowns among nine tissue classes represents a significant improvement, for a much more demanding task, than has been previously reported.