  Purpose: To investigate the use of a Vision Transformer (ViT) to
reconstruct/denoise GABA-edited magnetic resonance spectroscopy (MRS) from a
quarter of the typically acquired number of transients using spectrograms.
  Theory and Methods: A quarter of the typically acquired number of transients
collected in GABA-edited MRS scans are pre-processed and converted to a
spectrogram image representation using the Short-Time Fourier Transform (STFT).
The image representation of the data allows the adaptation of a pre-trained ViT
for reconstructing GABA-edited MRS spectra (Spectro-ViT). The Spectro-ViT is
fine-tuned and then tested using \textit{in vivo} GABA-edited MRS data. The
Spectro-ViT performance is compared against other models in the literature
using spectral quality metrics and estimated metabolite concentration values.
  Results: The Spectro-ViT model significantly outperformed all other models in
four out of five quantitative metrics (mean squared error, shape score,
GABA+/water fit error, and full width at half maximum). The metabolite
concentrations estimated (GABA+/water, GABA+/Cr, and Glx/water) were consistent
with the metabolite concentrations estimated using typical GABA-edited MRS
scans reconstructed with the full amount of typically collected transients.
  Conclusion: The proposed Spectro-ViT model achieved state-of-the-art results
in reconstructing GABA-edited MRS, and the results indicate these scans could
be up to four times faster.
