Quantifying metabolism in hyperpolarized (HP) 13C MRI can be challenging because of low signal-to-noise ratio for downstream metabolites. To overcome this limitation, we investigated a new patch-based singular value decomposition method to denoise dynamic imaging data and tested it in numerical simulations and on 6 HP [1-13C]pyruvate EPI human brain datasets. The sensitivity enhancement provided by denoising significantly improved quantification of metabolite dynamics. With denoising, [1-13C]pyruvate and its metabolites [1-13C]lactate and [13C]bicarbonate had ≥5-fold sensitivity gain, improving the number of quantifiable voxels for mapping pyruvate-to-bicarbonate conversion rates (kPB) by 2-fold, and providing whole-brain coverage for mapping pyruvate-to-lactate conversion rates (kPL).
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