Abstract
Kinetic modeling of the in vivo 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-(13)C] pyruvate was intravenously injected in a total of 10 brain tumor patients to measure its rate of conversion to lactate ( k(PL) ) and bicarbonate ( k(PB) ) via echo-planar imaging. Our aim was to investigate new methods to provide k(PL) and k(PB) 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.