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Abstract #0439

Denoising of Hyperpolarized 13C MR Images of the Human Brain Using Patch-based Higher-order Singular Value Decomposition

Yaewon Kim1, Hsin-Yu Chen1, Adam W. Autry1, Javier Villanueva-Meyer1, Susan M. Chang2, Yan Li1, Peder E. Z. Larson1, Jeffrey R. Brender3, Murali C. Krishna3, Duan Xu1, Daniel B. Vigneron1,2, and Jeremy W. Gordon1
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States, 2Department of Neurological Surgery, University of California, San Francisco, CA, United States, 3Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States

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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