Kinetic Modelling and Inference of Hyperpolarized 13C Molecules in Cancer Metabolism

TL;DR

This paper presents a kinetic modeling approach for hyperpolarized 13C-MRI data in cancer, using a two-site exchange model and MCMC inference to quantify tumor metabolism and heterogeneity.

Contribution

It introduces a novel application of kinetic modeling and inference techniques, including MCMC, to hyperpolarized 13C-MRI data for cancer metabolism analysis.

Findings

Quantitative metabolic parameters were reliably estimated.

The modeling revealed intratumour heterogeneity.

New fitting methods improved inference accuracy.

Abstract

Hyperpolarized 13C-MRI allows real time observation of metabolism in vivo. Imaging sequences have been developed to follow the metabolism of [1-13C] pyruvate and extract reaction kinetics, which can show tumour treatment response. We applied the fitting model and algorithm for the imaging data of mice tumour models and determined error estimates for the parameters of interest. Data was least-squares fitted onto a two-site exchange model in MATLAB, followed by statistic computation to assess model performance. Inference through the application of MCMC was also performed. The modelling and inference process extracted quantitative information satisfactorily and reproducibly, demonstrating metabolic activity and intratumour heterogeneity. Finally, novel fitting methods were evaluated and further recommendations were made.