Abstract
To assess the feasibility of using a hybrid Maximum-Entropy/Nonlinear Least Squares (MEM/NLS) method for analyzing the kinetics of hyperpolarized dynamic data with minimum a priori knowledge. A continuous distribution of rates obtained through the Laplace inversion of the data is used as a constraint on the NLS fitting to derive a discrete spectrum of rates. Performance of the MEM/NLS algorithm was assessed through Monte Carlo simulations and validated by fitting the longitudinal relaxation time curves of hyperpolarized [1-(13) C] pyruvate acquired at 9.4 Tesla and at three different flip angles. The method was further used to assess the kinetics of hyperpolarized pyruvate-lactate exchange acquired in vitro in whole blood and to re-analyze the previously published in vitro reaction of hyperpolarized (15) N choline with choline kinase. The MEM/NLS method was found to be adequate for the kinetic characterization of hyperpolarized in vitro time-series. Additional insights were obtained from experimental data in blood as well as from previously published (15) N choline experimental data. The proposed method informs on the compartmental model that best approximate the biological system observed using hyperpolarized (13) C MR especially when the metabolic pathway assessed is complex or a new hyperpolarized probe is used.
Highlights
Hyperpolarized metabolic imaging has enabled significant enhancements of the magnetic resonance (MR) signals of 13C in a range of small molecules allowing the distinction and imaging of an injected molecule from its downstream metabolites
This approach was originally proposed for the analysis of protein folding by Steinbach and colleagues and to spectral-based algorithms allows the derivation of a spectrum of kinetic rates that characterize the maximum entropy method (MEM)/nonlinear least square (NLS) Method for Kinetics of Hyperpolarized Data biological phenomena described in the experimental data with minimum a priori assumptions (9)
This work is the proof of concept analysis of the feasibility of using the hybrid MEM/NLS method proposed by Steinbach J.P. and colleagues for the kinetic analysis of hyperpolarized in vitro data with minimum a priori assumption of the metabolic conversion studied
Summary
Hyperpolarized metabolic imaging has enabled significant enhancements of the magnetic resonance (MR) signals of 13C in a range of small molecules allowing the distinction and imaging of an injected molecule from its downstream metabolites. A common approach to fit hyperpolarized data uses mathematical models characterized by several compartments that interconvert at characteristic rates This method has been used to quantify the rate of conversion of hyperpolarized pyruvate to lactate in vitro in EL-4 mouse lymphoma cells (1) and in T47D human breast cells (5), as well as to characterize pyruvate metabolism in pig hearts in vivo (6). In this work we explore the possibility of using a hybrid maximum entropy/nonlinear least squares method (MEM/NLS) for the kinetic characterization of dynamic hyperpolarized 13C data This approach was originally proposed for the analysis of protein folding by Steinbach and colleagues and to spectral-based algorithms allows the derivation of a spectrum of kinetic rates that characterize the MEM/NLS Method for Kinetics of Hyperpolarized Data biological phenomena described in the experimental data with minimum a priori assumptions (9)
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