Hyperpolarized 13C metabolic imaging of the human abdomen with spatiotemporal denoising

Abstract

AbstractPurposeImproving the quality and maintaining the fidelity of large coverage abdominal hyperpolarized (HP) 13C MRI studies with a patch based global–local higher‐order singular value decomposition (GL‐HOVSD) spatiotemporal denoising approach.MethodsDenoising performance was first evaluated using the simulated [1‐13C]pyruvate dynamics at different noise levels to determine optimal kglobal and klocal parameters. The GL‐HOSVD spatiotemporal denoising method with the optimized parameters was then applied to two HP [1‐13C]pyruvate EPI abdominal human cohorts (n = 7 healthy volunteers and n = 8 pancreatic cancer patients).ResultsThe parameterization of kglobal = 0.2 and klocal = 0.9 denoises abdominal HP data while retaining image fidelity when evaluated by RMSE. The kPX (conversion rate of pyruvate‐to‐metabolite, X = lactate or alanine) difference was shown to be <20% with respect to ground‐truth metabolic conversion rates when there is adequate SNR (SNRAUC > 5) for downstream metabolites. In both human cohorts, there was a greater than nine‐fold gain in peak [1‐13C]pyruvate, [1‐13C]lactate, and [1‐13C]alanine apparent SNRAUC. The improvement in metabolite SNR enabled a more robust quantification of kPL and kPA. After denoising, we observed a 2.1 ± 0.4 and 4.8 ± 2.5‐fold increase in the number of voxels reliably fit across abdominal FOVs for kPL and kPA quantification maps.ConclusionSpatiotemporal denoising greatly improves visualization of low SNR metabolites particularly [1‐13C]alanine and quantification of [1‐13C]pyruvate metabolism in large FOV HP 13C MRI studies of the human abdomen.