Keywords: Machine Learning/Artificial Intelligence, Brain, Convolutional Neural Networks, Multi-class Regression, Unsupervised LearningQuantification of metabolites in the human brain in vivo from magnetic resonance spectra (MRS) has many applications in medicine and psychology, but it remains a challenging task despite considerable research efforts. In this paper, we propose quantification of metabolites from MEGA-PRESS data using deep learning through an unsupervised learning approach. A regression framework based on the Convolutional Neural Networks (CNN) is introduced for estimation of spectral parameters including the relative concentrations of metabolites, line-broadening, and zero-order phase. The results show that the model is capable of reliably fitting in vivo data.
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