Hyperpolarized 13C metabolic imaging of the human abdomen with spatiotemporal denoising
Tanner M. Nickles, Yaewon Kim, Philip M. Lee, Hsin-Yu Chen, Michael Ohliger, Robert A. Bok, Zhen J. Wang, Peder E. Z. Larson, Daniel B. Vigneron, Jeremy W. Gordon
Abstract
Purpose
Improving 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.
Methods
Denoising performance was first evaluated using the simulated [1-13C]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 [1-13C]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 (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 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 [1-13C]alanine and quantification of [1-13C]pyruvate metabolism in large FOV HP 13C MRI studies of the human abdomen.