biostudies-literatureNickles TMNIBIB NIH HHSNIDDK NIH HHSNCI NIH HHSNational Institutes of HealthNIH HHS2153-2161https://www.ebi.ac.uk/biostudies/studies/S-EPMC10950515biostudies-literatureUnknown91(5)<h4>Purpose</h4>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.<h4>Methods</h4>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).<h4>Results</h4>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.<h4>Conclusion</h4>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.Magnetic resonance in medicineHyperpolarized &lt;sup&gt;13&lt;/sup&gt; C metabolic imaging of the human abdomen with spatiotemporal denoising.PMC10950515U01 EB026412P41 EB013598R01 CA249909R01 CA256740R01 DK115987R01DK115987P41EB013598U01EB026412Vigneron DBNickles TMGordon JWChen HYKim YOhliger MBok RAWang ZJLarson PEZLee PMfalseHyperpolarized &lt;sup&gt;13&lt;/sup&gt; C metabolic imaging of the human abdomen with spatiotemporal denoising.<h4>Purpose</h4>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.<h4>Methods</h4>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).<h4>Results</h4>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.<h4>Conclusion</h4>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.2024-01-01T00:00:00Z2024 May2025-07-12T03:05:51.03Z2025-07-12T03:05:51.03ZS-EPMC109505153819331010.1002/mrm.29985