Hyperpolarised [1-13C]pyruvate forms an effective probe of metabolism in vivo and has been used extensively to diagnose and prognosticate cancer. Commonly, [1-13C]pyruvate metabolism is quantified by either total metabolite-to-pyruvate integral ("AUC") ratios, or by fitting metabolic models by least-squares methods. Here, we use a modified Markov Chain Monte Carlo (MCMC) method with adaptive sampling and delayed rejection to fit models to hyperpolarised datasets of the healthy rat brain generated by a spectral-spatial EPI imaging sequence . The method is able to statistically discriminates between signal and noise, and returns quantitatively bounded maps of rate constants of interest, such as $$$k_{\text{Pyruvate}\rightarrow\text{Lactate}}$$$.
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