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Abstract #0905

PET by MRI: Glucose Imaging by 13C MRS by Noise Suppression through Tensor Decomposition Rank Reduction

Jeffrey R Brender1, Shun Kishimoto1, Hellmut Merkle2, Galen Reed3, Ralph E Hurd4, Albert P Chen5, Jan Henrik Ardenkjaer-Larsen6, Jeeva Munasinghe2, Keita Saito1, Tomohiro Seki1, Nobu Oshima1, Kazu Yamamoto1, James Mitchell1, and Murali C. Krishna1

1Radiation Biology, NIH/NCI, Washington DC, DC, United States, 2NIH/NINDS, Bethesda, MD, United States, 3GE HealthCare, Dallas, TX, United States, 4Applied Science Laboratory, GE HealthCare, Menlo Park, CA, United States, 5GE HealthCare, Toronto, ON, Canada, 6Technical University of Denmark, Lyngby, Denmark

We show here a new method of imaging glucose metabolism in vivo by 13C MRS without DNP that relies on a simple but efficient postprocessing procedure using the higher dimensional analogue of SVD, tensor decomposition. Using this procedure, without sacrificing accuracy, we achieve an order of magnitude increase in SNR in both DNP and non-hyperpolarized single voxel experiments. In CSI imaging experiments, we see an approximately 30 fold increase in SNR, enough that the glucose to lactate conversions can be imaged with a time resolution of 12 seconds and an overall spatial resolution that compares favorably to 18F-FDG PET.

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