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Abstract #4998

Characterization of brain metabolites using CEST and machine learning

Nirbhay Narayan Yadav1,2, Akansha Sehgal1,2, and Peter van Zijl1,2

1Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University, Baltimore, MD, United States, 2FM Kirby Research Center, The Kennedy Krieger Institute, Baltimore, MD, United States

In vivo CEST MRI data can include contributions from a vast array of metabolites, mobile proteins and peptides, and immobile macromolecules amongst others. Detecting which components are present in any given dataset is a major challenge. Here, as a first start to address the problem, we have used a machine learning approach to classify a CEST dataset acquired from brain metabolite phantoms. The classifier was successful in all cases and was shown to be robust to a moderate level of noise. The results demonstrate this is a promising technique that could potentially quantify molecular contributions in vivo.

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