Purpose To assess volumetric proton MR spectroscopic imaging of the human brain on multi-vendor MRI instruments. Methods Echo-planar spectroscopic imaging (EPSI) was developed on instruments from three manufacturers, with matched specifications and acquisition protocols that accounted for differences in sampling performance, RF power, and data formats. Inter-site reproducibility was evaluated for signal-normalized maps of N-acetylaspartate (NAA), Creatine (Cre) and Choline using phantom and human subject measurements. Comparative analyses included metrics for spectral quality, spatial coverage, and mean values in atlas-registered brain regions. Results Inter-site differences for phantom measurements were under 1.7% for individual metabolites and 0.2% for ratio measurements. Spatial uniformity ranged from 79% to 91%. The human studies found differences of mean values in the temporal lobe, but good agreement in other white-matter regions, with maximum differences relative to their mean of under 3.2%. For NAA/Cre, the maximum difference was 1.8%. In grey-matter a significant difference was observed for frontal lobe NAA. Primary causes of inter-site differences were attributed to shim quality, B0 drift, and accuracy of RF excitation. Correlation coefficients for measurements at each site were over 0.60, indicating good reliability. Conclusion A volumetric intensity-normalized MRSI acquisition can be implemented in a comparable manner across multi-vendor MR instruments.

Background and Purpose Accurate grading of cerebral glioma using conventional structural imaging techniques remains challenging due to the relatively poor sensitivity and specificity of these methods. The purpose of this study was to evaluate the relative sensitivity and specificity of structural MRI and MR measurements of perfusion, diffusion, and spectroscopic parameters for glioma grading. A secondary objective was to evaluate a whole-brain MR spectroscopic imaging method for evaluation of brain tumors. Materials and Methods Fifty six patients with radiologically suspected untreated glioma were studied with T1- and T2-weighted MR imaging, DCE-MR imaging, DTI, and volumetric whole-brain MR spectroscopic imaging. ROC analysis was performed using the relative CBV, ADC, FA, and multiple spectroscopic parameters to determine optimum thresholds for tumor grading and to obtain the sensitivity, specificity, PPV, and NPV for identifying high-grade gliomas. Logistic regression was performed to analyze all the parameters together. Results The relative CBV individually classified glioma as low and high grade with a sensitivity and specificity of 100% and 88% respectively based on a threshold value of 3.34. On combining all parameters under consideration, the classification was achieved with 2% error and sensitivity and specificity of 100% and 96% respectively. Conclusion Individually, CBV measurement provides the greatest diagnostic performance for predicting glioma grade; however, the most accurate classification can be achieved by combining all of the imaging parameters. The whole-brain MR spectroscopic imaging method provided data from of a large fraction of the tumor volumes.

With signal-to-noise ratio enhancements on the order of 10,000-fold, hyperpolarized MR spectroscopic imaging (MRSI) of metabolically active substrates allows the study of both the injected substrate and downstream metabolic products in vivo. Although hyperpolarized [1-13C]-pyruvate, in particular, has been used to demonstrate metabolic activities in various animal models, robust quantitation and metabolic modeling remain important areas of investigation. Enzyme saturation effects are routinely seen with commonly used doses of hyperpolarized [1-13C]-pyruvate, however most metrics proposed to date, including metabolite ratios, time-to-peak of metabolic products, or single exchange rate constant fail to capture these saturation effects. In addition, the widely used small flip-angle excitation approach does not correctly model the inflow of fresh downstream metabolites generated proximal to the target slice, which is often a significant factor in vivo. In this work, we developed an efficient quantitation framework employing a spiral-based dynamic spectroscopic imaging approach. The approach overcomes the aforementioned limitations and demonstrates that the in vivo 13C labeling of lactate and alanine after a bolus injection of [1-13C]-pyruvate is well approximated by saturatable kinetics, which can be mathematically modeled using a Michaelis-Menten-like formulation with the resulting estimated apparent maximal reaction velocity Vmax and apparent Michaelis constant KM parameters being unbiased with respect to critical experimental parameters including the substrate dose, bolus shape, and duration. Although the proposed saturatable model has similar mathematical formulation to the original Michaelis-Menten kinetics, it is conceptually different. In this study, we focus on the 13C labeling of lactate and alanine and do not differentiate the labeling mechanism (net flux or isotopic exchange) or the respective contribution of various factors (organ perfusion rate, substrate transport kinetics, enzyme activities, and the size of the unlabeled lactate and alanine pools) to the labeling process.