Frequency-selective MRS data quantification with frequency prior knowledge.

Various signal processing techniques have been proposed to improve spectral estimation of closely spaced sinusoids in the presence of noise. This paper exploits frequency prior knowledge information to extract single peaks in magnetic resonance spectra, corresponding to metabolites of interest, by means of a highly selective finite impulse response filter. Thereafter the estimation of the parameters of the peaks is carried out using a singular-value-decomposition-based method known as HTLS. The new technique improves the performance of fully automated magnetic resonance spectroscopy data quantification when frequency prior knowledge is available.