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

Abstract

Purpose: Improving the quality and maintaining the fidelity of large coverage abdominal hyperpolarized (HP) ¹³ C MRI studies with a patch based global-local higher-order singular value decomposition (GL-HOVSD) spatiotemporal denoising approach.

Methods: Denoising performance was first evaluated using the simulated [1-¹³ C]pyruvate dynamics at different noise levels to determine optimal k_global and k_local parameters. The GL-HOSVD spatiotemporal denoising method with the optimized parameters was then applied to two HP [1-¹³ C]pyruvate EPI abdominal human cohorts (n = 7 healthy volunteers and n = 8 pancreatic cancer patients).

Results: The parameterization of k_global = 0.2 and k_local = 0.9 denoises abdominal HP data while retaining image fidelity when evaluated by RMSE. The k_PX (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 (SNR_AUC > 5) for downstream metabolites. In both human cohorts, there was a greater than nine-fold gain in peak [1-¹³ C]pyruvate, [1-¹³ C]lactate, and [1-¹³ C]alanine apparent SNR_AUC . The improvement in metabolite SNR enabled a more robust quantification of k_PL and k_PA . 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 k_PL and k_PA quantification maps.

Conclusion: Spatiotemporal denoising greatly improves visualization of low SNR metabolites particularly [1-¹³ C]alanine and quantification of [1-¹³ C]pyruvate metabolism in large FOV HP ¹³ C MRI studies of the human abdomen.

Keywords: abdominal imaging; alanine; carbon-13; higher-order singular value decomposition; hyperpolarization; image denoising; pancreas; pyruvate.