Motivation: In hyperpolarized 13C-pyruvate MRI, the pyruvate-to-lactate pharmacokinetic apparent rate constant, kPL, has previously shown to be an important clinical biomarker to quantify metabolic reprogramming(3-5). However, rate constant measurements cannot be compared to any ground-truth data and gathering large amounts of human imaging study data can be logistically prohibitive. Goal(s): To utilize patient-derived anatomical models and existing open-source pharmacokinetic models to create metabolic phantoms resembling in vivo datasets. Approach: A framework combining patient-derived anatomy from BrainWeb and the Cardiac Atlas Project with 1-compartment, 3-pool pharmacokinetic modeling was developed. Results: Simulated hyperpolarized whole-brain and whole-heart data were generated and closely matched in vivo datasets. Impact: Metabolic digital phantoms create ground truth data providing opportunities to accurately characterize the performance of new sampling and acquisition strategies, validate advanced data analysis techniques and create faithful synthetic data to supplement training datasets for machine learning applications.
Haller et al. (Tue,) studied this question.