Xu, T., Mayer, D., Gu, M., Yen, Y. F., Josan, S., Tropp, J., … & Spielman, D. (2011). Quantification of in vivo metabolic kinetics of hyperpolarized pyruvate in rat kidneys using dynamic 13C MRSI. NMR in Biomedicine, 24(8), 997-1005.
With signal-to-noise ratio enhancements on the order of 10,000-fold, hyperpolarized 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 quantification 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 and single exchange rate constants, 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 quantification framework employing a spiral-based dynamic spectroscopic imaging approach. The approach overcomes the aforementioned limitations and demonstrates that the in vivo13C 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 being unbiased with respect to critical experimental parameters, including the substrate dose, bolus shape and duration. Although the proposed saturatable model has a 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. Copyright © 2011 John Wiley & Sons, Ltd.