A data-driven approach for improved quantification of in-vivo metabolic conversion rate of hyperpolarized [1-13C]pyruvate

Motivation: Accurate quantification of metabolism in hyperpolarized (HP) 13C MRI is essential for clinical application. However, kinetic model parameters such as flip angle are often uncertain, leading to model mismatch. Goal(s): We aimed to develop a data-driven approach to quantify the HP pyruvate-to-lactate conversion rate (kPL) for with improved accuracy. Approach: To validate our method, we conducted numerical simulations and animal studies with HP 13C-pyruvate and applied this method to human abdominal data. Results: Our findings revealed that the data-driven kinetic fitting method can improve model-data fits by correcting flip angle and relaxation errors, resulting in more accurate kPL determination. Impact: Using the new data-driven kinetic fitting method to quantify HP 13C-pyruvate metabolism, accurate kPL can be obtained even in the presence of B1 inhomogeneity. This improvement may prove to be clinically valuable in improving tumor staging or assessing treatment-response.