Abstract #3003

# Bounded rate constant estimation in hyperpolarised  [1-13C]pyruvate experiments by a Delayed-rejection Adaptive Metropolis Markov-Chain Monte Carlo (DAM-MCMC) Method

Jack Julian James Jenkins Miller1,2,3, Angus Zoen Lau1,4, and Damian John Tyler1,2

1Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, United Kingdom, 2Oxford Centre for Clinical Magnetic Resonance Research, University of Oxford, Oxford, United Kingdom, 3Department of Physics, University of Oxford, Oxford, United Kingdom, 4Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada

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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