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

Abstract

Purpose: Accurate quantification of metabolism in hyperpolarized (HP) ¹³C MRI is essential for clinical applications. However, kinetic model parameters are often confounded by uncertainties in radiofrequency flip angles and other model parameters.

Methods: A data-driven kinetic fitting approach for HP ¹³C-pyruvate MRI was proposed that compensates for uncertainties in the B₁ ⁺ field. We hypothesized that introducing a scaling factor to the flip angle to minimize fit residuals would allow more accurate determination of the pyruvate-to-lactate conversion rate (k_PL). Numerical simulations were performed under different conditions (flip angle, k_PL, and T₁ relaxation), with further testing using HP ¹³C-pyruvate MRI of rat liver and kidneys.

Results: Simulations showed that the proposed method reduced k_PL error from 60% to 1% when the prescribed and actual flip angles differed by 60%. The method also showed robustness to T₁ uncertainties, achieving median k_PL errors within ±3% even when the assumed T₁ was incorrect by up to a factor of 2. In rat studies, better-quality fitting for lactate signals (a 1.4-fold decrease in root mean square error [RMSE] for lactate fit) and tighter k_PL distributions (an average of 3.1-fold decrease in k_PL standard deviation) were achieved using the proposed method compared with when no correction was applied.

Conclusion: The proposed data-driven kinetic fitting approach provided a method to accurately quantify HP ¹³C-pyruvate metabolism in the presence of B₁ ⁺ inhomogeneity. This model may also be used to correct for other error sources, such as T₁ relaxation and flow, and may prove to be clinically valuable in improving tumor staging or assessing treatment response.