Ghosting artifacts in clinical MR spectroscopy are problematic since they superimpose with metabolites and lead to inaccurate quantification. Here, we make use of “Deep Learning” (DL) methods to remove ghosting artifacts in MR spectra of human brain. The DL method was trained on a huge database of simulated spectra with and without ghosting artifacts, which represent complex variants of ghost-ridden spectra, transformed to time-frequency spectrograms. The trained model was tested on simulated and in-vivo spectra. The preliminary results for ghost removal show potential in simulated and in-vivo spectra, but need further refinement and quantitative testing.