Two-Stage Evolutionary Quantification of In Vivo MRS Metabolites

The main subject of this work is the in vivo quantification of the metabolites concentrations revealed in the magnetic resonance spectroscopy (MRS) spectra. For this purpose, a novel two-stage processing methodology, consisting of the denoising of the MRS signal and the quantification of the metabolites’ peaks using a genetic algorithm (GA), is proposed. The denoising stage tends to improve the quality of the acquired MRS signal in a way that makes the fitting procedure performed by the genetic algorithm (GA) more successful. Two different approaches for improving the MRS signal quality, the denoising via wavelet analysis and signal separation by singular value decomposition (SVD), under possible combinations are examined. The introduced quantification technique deals with metabolites’ peaks overlapping, a considerably difficult situation occurred in real conditions. Extensive experiments have proved the efficiency of the introduced methodology in artificial MRS data by establishing it as a generic metabolite quantification procedure.