Water modeled signal removal and data quantification in localized MR spectroscopy using a time-scale postacquistion method

We have previously shown the continuous wavelet transform (CWT), a signal-p rocessing tool, which is based upon an iterative algorithm using a lorentzi an signal model, to be useful as a postacquisition water suppression techni que. To further exploit this tool we show its usefulness in accurately quan tifying the signal metabolites after water removal. However, due to the sta tic held inhomogeneities, eddy currents, and "radiation damping," the water signal and the metabolites may no longer have a lorentzian lineshape. Ther efore, another signal model must be used. As the CWT is a flexible method, we have developed a new algorithm using a gaussian model and found that it fits the signal components, especially the water resonance, better than the lorentzian model in most cases. A new framework, which uses the two models , is proposed. The framework iteratively extracts each resonance, starting by the water peak; from the raw signal and adjusts its envelope to both the lorentzian and the gaussian models. The model giving the best fit is selec ted. As a consequence, the small signals originating from metabolites when selecting, removing, and quantifying the dominant water resonance from the raw time domain signal are preserved and an accurate estimation of their co ncentrations is obtained. This is demonstrated by analyzing (H-1) magnetic resonance spectroscopy unsuppressed water data collected from a phantom wit h known concentrations at two different field strengths and data collected from normal volunteers using two different localization methods. (C) 2001 A cademic Press.