  Magnetic resonance spectroscopy (MRS) is an important technique in biomedical
research and it has the unique capability to give a non-invasive access to the
biochemical content (metabolites) of scanned organs. In the literature, the
quantification (the extraction of the potential biomarkers from the MRS
signals) involves the resolution of an inverse problem based on a parametric
model of the metabolite signal. However, poor signal-to-noise ratio (SNR),
presence of the macromolecule signal or high correlation between metabolite
spectral patterns can cause high uncertainties for most of the metabolites,
which is one of the main reasons that prevents use of MRS in clinical routine.
In this paper, quantification of metabolites in MR Spectroscopic imaging using
deep learning is proposed. A regression framework based on the Convolutional
Neural Networks (CNN) is introduced for an accurate estimation of spectral
parameters. The proposed model learns the spectral features from a large-scale
simulated data set with different variations of human brain spectra and SNRs.
Experimental results demonstrate the accuracy of the proposed method, compared
to state of the art standard quantification method (QUEST), on concentration of
20 metabolites and the macromolecule.
