Abstract #2836

Assessment of Higher-Order SVD Rank Reduction Denoising on Dynamic Hyperpolarized [13C]pyruvate Metabolic Imaging Data on Patients with Glioma

Sana Vaziri¹, Adam Autry¹, Yaewon Kim¹, Hsin-Yu Chen¹, Jeremy W Gordon¹, Marisa LaFontaine¹, Jasmine Graham¹, Janine Lupo¹, Jennifer Clarke², Javier Villanueva-Meyer¹, Nancy Ann Oberheim Bush³, Duan Xu¹, Susan M Chang², Peder EZ Larson¹, Daniel B Vigneron^1,4, and Yan Li¹

¹Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, ²Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States, ³Department of Neurology, University of California, San Francisco, San Francisco, CA, United States, ⁴Department of Bioengineering and Therapeutic Science, University of California, San Francisco, San Francisco, CA, United States

Real-time monitoring of enzymatic conversion of [1-¹³C]pyruvate to [1-¹³C]lactate and [1-¹³C]bicarbonate in the brain can be performed using dynamic hyperpolarized ¹³C metabolic imaging. However, signal-to-noise ratios of bicarbonate are significantly lower than lactate and pyruvate, making it difficult to assess metabolic flux, particularly in lesions. Denoising techniques employing rank reduction via the multidimensional extension of singular value decomposition have recently been introduced for conventional MR images, diffusion-weighted images and HP-¹³C metabolic imaging. Here, we investigate the use of two higher-order singular value decomposition denoising techniques on dynamic hyperpolarized ¹³C metabolic images acquired from patients with glioma.

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