Once an MRS dataset has been acquired, several important steps must be taken to obtain the desired metabolite concentration measures. First, the data must be preprocessed to prepare them for analysis. Next, the intensity of the metabolite signal(s) of interest must be estimated. Finally, the measured metabolite signal intensities must be converted into scaled concentration units employing a quantitative reference signal to allow meaningful interpretation. In this paper, we review these three main steps in the post-acquisition workflow of a single-voxel MRS experiment (preprocessing, analysis and quantification) and provide recommendations for best practices at each step. Abbreviations: 1 H, proton; 13 C, carbon-13; B 0 , main magnetic field; B 1 , RF field; Cr, creatine; CRMVB, Cramér-Rao minimum variance bound; CSF, cerebrospinal fluid; d GM , water density of grey matter; d WM , water density of white matter; ERETIC, Electric Reference to Access in vivo Concentrations; f CSF , volume fraction of cerebrospinal fluid inside the MRS voxel; fCSF H2O , water mole fraction in cerebrospinal fluid; fGM, volume fraction of gray matter inside the MRS voxel; fGM H2O , water mole fraction in gray matter; FFT, fast Fourier transform; FID, free induction decay; FQN, fit quality number; FWHM, full width at half maximum; f WM , volume fraction of white matter inside the MRS voxel; fWM H2O , water mole fraction in white matter; GM, grey matter; GPC, glycerophosphocholine; [H 2 O] molal , water concentration in moles of water per kilogram of tissue water = 55.49 moles/kg; [H 2 O] molar , water concentration in moles of water per liter of tissue water; HERMES, Hadamard encoding and reconstruction of MEGA-edited spectroscopy; MEGA-PRESS, Mescher-Garwood point resolved spectroscopy; [M] GM /[M] WM , assumed ratio of grey matter to white matter metabolite concentrations; MM, macromolecules; [M]molal, metabolite concentration in moles of metabolite per kilogram of tissue water; [M]molar, metabolite concentration in moles of metabolite per liter of tissue water; MRSI, magnetic resonance spectroscopic imaging; NAA, N-acetylaspartate; NAAG, N-acetylaspartylglutamate; N M , number of protons contributing to metabolite signal; N P , number of points in FID/spectrum; N pc , number of phase encoding steps in one phase cycle; N RF , number of RF channels; N tra , number of transients;PCh, phosphocholine; PCr, phosphocreatine; RH2O CSF , relaxation scaling factor for water in cerebrospinal fluid; RH2O GM , relaxation scaling factor for water in grey matter; RH2O WM , relaxation scaling factor for water in white matter; RM, relaxation scaling factor for tissue metabolite signal; RM GM , relaxation scaling factor for metabolite in grey matter; RM WM , relaxation scaling factor for metabolite in white matter; S H2O , water signal intensity; SH2O obs , observed water signal intensity in the presence of relaxation; S M , metabolite signal intensity; SM obs , observed metabolite signal intensity in the presence of relaxation; SNR, signal-to-noise r...

Purpose Frequency and phase drifts are a common problem in the acquisition of in vivo magnetic resonance spectroscopy (MRS) data. If not accounted for, frequency and phase drifts will result in artifactual broadening of spectral peaks, distortion of spectral lineshapes, and a reduction in signal-to-noise ratio (SNR). We present herein a new method for estimating and correcting frequency and phase drifts in in vivo MRS data. Methods We used a simple method of fitting each spectral average to a reference scan (often the first average in the series) in the time domain through adjustment of frequency and phase terms. Due to the similarity with image registration, this method is referred to as “spectral registration.” Using simulated data with known frequency and phase drifts, the performance of spectral registration was compared with two existing methods at various SNR levels. Results Spectral registration performed well in comparison with the other methods tested in terms of both frequency and phase drift estimation. Conclusions Spectral registration provides an effective method for frequency and phase drift correction. It does not involve the collection of navigator echoes, and does not rely on any specific resonances, such as residual water or creatine, making it highly versatile.