Apparent rate constant mapping using hyperpolarized [1–¹³C]pyruvate

Abstract

Hyperpolarization of [1-13C]pyruvate in solution allows real-time measurement of uptake and metabolism using MR spectroscopic methods. After injection and perfusion, pyruvate is taken up by the cells and enzymatically metabolized into downstream metabolites such as lactate, alanine, and bicarbonate. In this work, we present comprehensive methods for the quantification and interpretation of hyperpolarized 13C metabolite signals. First, a time-domain spectral fitting method is described for the decomposition of FID signals into their metabolic constituents. For this purpose, the required chemical shift frequencies are automatically estimated using a matching pursuit algorithm. Second, a time-discretized formulation of the two-site exchange kinetic model is used to quantify metabolite signal dynamics by two characteristic rate constants in the form of (i) an apparent build-up rate (quantifying the build-up of downstream metabolites from the pyruvate substrate) and (ii) an effective decay rate (summarizing signal depletion due to repetitive excitation, T1-relaxation and backward conversion). The presented spectral and kinetic quantification were experimentally verified in vitro and in vivo using hyperpolarized [1-13C]pyruvate. Using temporally resolved IDEAL spiral CSI, spatially resolved apparent rate constant maps are also extracted. In comparison to single metabolite images, apparent build-up rate constant maps provide improved contrast by emphasizing metabolically active tissues (e.g. tumors) and suppression of high perfusion regions with low conversion (e.g. blood vessels). Apparent build-up rate constant mapping provides a novel quantitative image contrast for the characterization of metabolic activity. Its possible implementation as a quantitative standard will be subject to further studies.

Assessing Prostate Cancer Aggressiveness with Hyperpolarized Dual-Agent 3D Dynamic Imaging of Metabolism and Perfusion

“…Our approach also did not use information from the AIF. There is potential to improve the kinetic modeling of metabolism and perfusion with acquisitions capturing the bolus input signal, incorporating the AIF, and by using advanced modeling methods such as those presented in Kazan et al (48), Khegai et al (49), and Bankson et al (50). …”

Investigation of analysis methods for hyperpolarized 13C‐pyruvate metabolic MRI in prostate cancer patients

“…We compared the following metabolism quantification strategies in the framework of the above tissue model. Inputless k P L fitting.This fitting approach, inspired by Khegai et al, only fits the lactate magnetization and not the pyruvate magnetization, where the measured pyruvate magnetization is used as the input for the kinetic model at each time point.The estimated lactate magnetization measurement at each time point, n , is fit based only on the measured pyruvate magnetization at the adjacent time points, , and the estimated lactate magnetization at the previous time point by solving the two‐site model in differential Equations and : where and …”

“…This fitting approach, inspired by Khegai et al, only fits the lactate magnetization and not the pyruvate magnetization, where the measured pyruvate magnetization is used as the input for the kinetic model at each time point.…”

Influence of parameter accuracy on pharmacokinetic analysis of hyperpolarized pyruvate

“…The HP lactate signal normalized to the total metabolically active HP signal (pyruvate plus lactate) is a simple semi-quantitative method (22,23), but this approach does not account for signal arising from within vasculature or the effects of perfusion on apparent chemical exchange. A model with two-site chemical exchange has been used to quantify the apparent rate constant ( k PL ) for conversion of HP pyruvate into lactate (22,24,25). In addition, pharmacokinetic (PK) models that incorporate multiple spatial pools have been proposed (15,26).…”