  Magnetic resonance spectroscopic imaging (SI) is a unique imaging technique
that provides biochemical information from in vivo tissues. The 1H spectra
acquired from several spatial regions are quantified to yield metabolite
concentrations reflective of tissue metabolism. However, since these
metabolites are found in tissues at very low concentrations, SI is often
acquired with limited spatial resolution. In this work we test the hypothesis
that deep learning is able to upscale low resolution SI, together with the
T1-weighted (T1w) image, to reconstruct high resolution SI. We report a novel
densely connected Unet (D-Unet) architecture capable of producing
super-resolution spectroscopic images. The inputs for the D-UNet are the T1w
image and the low resolution SI image while the output is the high resolution
SI. The results of the D-UNet are compared both qualitatively and
quantitatively to simulated and in vivo high resolution SI. It is found that
this deep learning approach can produce high quality spectroscopic images and
reconstruct entire 1H spectra from low resolution acquisitions, which can
greatly advance the current SI workflow.
