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Abstract #2896

An Untargeted Metabolomics Approach to MRS in the Human Brain: A Comparison between LCModel  and MRS-based Classifier-Development System

Srijyotsna Volety1, Elizabeth Seaquist2, Gulin Oz3, and Uzay Emir1,4
1Health Sciences, Purdue University, West Lafayette, IN, United States, 2Department of Medicine, Medical School, University of Minnesota, Minneapolis, MN, United States, 3Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States, 4Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States

MRS quantification tools such as LCModel require the use of prior knowledge and can be time-consuming. Therefore, we propose an untargeted metabolomics approach to in vivo 1H-MRS using pattern recognition and machine learning to analyze spectral features. The ability of our approach to measure changes in brain glucose while blood glucose levels were increased was studied using high quality spectra and reliable data acquisition methods. Results showed similar time-course glucose signals and sensitivity to changes in glucose concentrations for both LCModel and our pattern recognition analysis. Thus, demonstrating that untargeted metabolomics techniques can be used for in vivo MRS quantification.

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