Two-dimensional J-resolved proton MR spectroscopy and prior knowledge fitting (ProFit) in the frontal and parietal lobes of healthy volunteers: assessment of metabolite discrimination and general reproducibility.

Low spectral resolution and severe metabolite peak overlap are associated with proton (1H) MR spectroscopy (MRS) data acquired from human brain using clinical MR imaging systems. Spatially localized variants of two-dimensional (2D) 1H MRS methods circumvent these issues by encoding a second frequency dimension and separating all uncoupled and scalar spin-spin (J)-coupled metabolite resonances across a 2D spectral surface. Localized 2D 1H MRS measurement sequences are analogous to high-resolution methods used extensively for elucidating the molecular connectivity of organic molecules in vitro, with localized 2D J-resolved spectroscopy (1–3) and 2D correlated spectroscopy (COSY) (4) being examples of two techniques adapted for human investigations. Applications for localized 2D J-resolved 1H MRS in human brain have included the resolution of γ-amino butyric acid (GABA) from creatine (Cre) resonances in substance abuse disorders (5,6), the investigation of amino acid neurotransmitter metabolism in bipolar disorder (7) and pain (8), and the separation of lactic acid (Lac) from lipids in brain tumors (9). Localized 2D COSY 1H MRS has been shown to enhance the available neurochemical information in healthy human brain tissue (4) and glioblastomas (10). The implementation of localized 2D 1H MRS acquisition schemes onto clinical MRI/MRS systems is relatively straightforward, as is the reconstruction and visualization of the recorded 2D spectral data. However, quantification of 2D 1H MRS data remains particularly challenging, and several approaches having been adopted at different institutions. Earlier attempts for quantifying 2D J-resolved 1H MRS data were based on the extraction of a one-dimensional (1D) row where, for GABA, the extraction of a row positioned at 3.01 ppm / 7.45 Hz enables sufficient resolution from dominating Cre methyl resonance (3,11). The row extraction approach recently was improved to enable the automated 1D spectral fitting of all individual rows for multiple metabolites in a 2D J-resolved dataset (12). A popular method for quantifying 2D COSY 1H MRS data recorded in vivo involves the calculation of metabolite peak volume integrals and the reproducibility of that technique has been demonstrated at 1.5 Tesla (T) (13). A statistical peak-separation and object segmentation algorithm also has been proposed for the analysis of 2D COSY datasets (14). Schulte and Boesiger recently introduced a novel algorithm termed Prior Knowledge Fitting (ProFit), which represents a notable advance in the automated analysis of 2D 1H MRS data (15). The ProFit algorithm iteratively fits in vivo 2D MRS data using a linear combination of suitable 2D model spectra, and can be adapted for fitting any given type of 2D spectral acquisition. The original report demonstrated the robust fitting of twenty individual metabolites to 2D J-resolved 1H MRS spectra acquired from healthy human volunteers including GABA, glutamate (Glu), glutamine (Gln), glutathione (GSH), and glycine (Gly). Data recorded from a single subject and from multiple subjects was used to calculate intra- and inter-subject metabolite fit standard deviations, respectively, which demonstrated a high degree of reproducibility (15). Spectral fit reliability also was evaluated using the Cramer-Rao lower bound (CRLB) values determined for each metabolite concentration. CRLB values are evaluated following inversion of the noise-normalized Fisher information matrix (16), and typically are outputted as the estimated standard deviation expressed as a percentage of the estimated metabolite concentration. Therefore, CRLB values represent the theoretical lower level of variability attainable for a metabolite at a given signal-to-noise (SNR). Compared to the fitting of conventional 1D 1H MRS data, 2D J-resolved 1H MRS data analyzed using ProFit resulted in significantly reduced CRLB values for the vast majority of metabolite species, including GABA, Gln, and Glu. The reproducibility of metabolite concentration, coupled with the low CRLB values detected for the amino neurotransmitter species, makes these approaches especially suitable for neuropsychiatric applications. The off-diagonal elements of the inverted Fisher matrix also can be used to calculate the amplitude correlation coefficient for a pair of metabolite estimates, thus providing additional data reflecting the inter-dependence of the ProFit signal estimates (16). Note that as with CRLB values, the amplitude correlation coefficients are an estimate of the lowest amount of covariance between two fit parameters, thus providing a lower bound for the actual amount of observed covariance that cannot be readily extracted from in vivo data. Such measures could provide unique information regarding the relative discriminability of specific metabolite resonances such as Glu from Gln, or GABA from Cre, which are typical outcome measures in neuropsychiatric MRS studies. MRS discrimination of metabolite resonances is also expected to be region-specific, due to the differential static magnetic field homogeneity and signal linewidth that is realistically achievable across the human brain. The primary objective of this study was to interrogate the magnitude and variability of the between-metabolite correlation coefficients as calculated in human brain using 2D J-resolved 1H MRS and ProFit analysis. MRS data acquired from two distinct brain regions in healthy volunteers using a multi-scan protocol commonly used in serial studies focusing on metabolic changes underlying disease progression and/or the effects of exogenous agents. A second aim of this study was to use a ProFit-independent metabolite normalization scheme based on a CSF-corrected water reference signal to further assess the reproducibility of 2D 1H MRS and ProFit analysis, and those findings are briefly put into context with the precedent literature.