Kinetic Modeling of Hyperpolarized Carbon-13 Pyruvate Metabolism in the Human Brain

Kinetic modeling of the <italic>in vivo</italic> pyruvate-to-lactate conversion is crucial to investigating aberrant cancer metabolism that demonstrates Warburg effect modifications. Non-invasive detection of alterations to metabolic flux might offer prognostic value and improve the monitoring of response to treatment. In this clinical research project, hyperpolarized [1–¹³C] pyruvate was intravenously injected in a total of 10 brain tumor patients to measure its rate of conversion to lactate (<inline-formula> <tex-math notation="LaTeX">${k}_{{\textit {PL}}}$ </tex-math></inline-formula>) and bicarbonate (<inline-formula> <tex-math notation="LaTeX">${k}_{{\textit {PB}}}$ </tex-math></inline-formula>) via echo-planar imaging. Our aim was to investigate new methods to provide <inline-formula> <tex-math notation="LaTeX">${k}_{{\textit {PL}}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${k}_{{\textit {PB}}}$ </tex-math></inline-formula> maps with whole-brain coverage. The approach was data-driven and addressed two main issues: selecting the optimal model for fitting our data and determining an appropriate goodness-of-fit metric. The statistical analysis suggested that an input-less model had the best agreement with the data. It was also found that selecting voxels based on post-fitting error criteria provided improved precision and wider spatial coverage compared to using signal-to-noise cutoffs alone.