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Abstract #5645

Spectral Model Dependent Quantification of Triglyceride Composition using Chemical Shift Encoded Magnetic Resonance Imaging

Gregory Simchick1,2, Amelia Yin3,4, Hang Yin3,4, and Qun Zhao1,2

1Physics and Astronomy, University of Georgia, Athens, GA, United States, 2Bio-Imaging Research Center, University of Georgia, Athens, GA, United States, 3Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States, 4Center for Molecular Medicine, University of Georgia, Athens, GA, United States

Dynamic processes such as brown adipose tissue (BAT) activation and white adipose tissue (WAT) beiging have been shown to change triglyceride composition. Therefore, accurate spatial quantification of triglyceride composition is important for the monitoring of these processes. Presented here is an evaluation of the performance of various fat spectral models on the quantification of triglyceride composition using chemical shift encoded magnetic resonance imaging (CSE-MRI). Variations as large as 45% and less than 2.82% are observed in the average estimations of triglyceride composition and proton density fat fraction (PDFF), respectively. Estimations obtained using a material specific model correlate better with spectroscopy estimations than other examined models.

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