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Abstract #1298

Quantification of Non-Water-Suppressed Proton Spectroscopy using Deep Neural Networks

Marcia Sahaya Louis1, Eduardo Coello2, Huijun Liao2, Ajay Joshi3, and Alexander Lin2
1ECE, Boston University, Boston, MA, United States, 2Radiology, Brigham and Women's hospital, Boston, MA, United States, 3Boston University, Boston, MA, United States

Water is present in the brain tissue at a concentration that is at least four orders of magnitude higher than metabolites of interest. As a result, it is necessary to suppress the water resonance so that the brain metabolites of interest can be better visualized and quantified. This work presents a neural network model for extracting the metabolites spectrum from non-water-suppressed proton magnetic resonance spectra. The autoencoder model learns a vector field for mapping the water signal to a lower-dimensional manifold and accurately reconstructs the metabolite spectra as compared to water-suppressed spectra from the same subject.

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