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.

使用 Vision Transformer (ViT) 通过光谱图像重建 / 去噪 GABA 编辑的磁共振波谱 (MRS) 的方法，可以从一个常规获取到的四分之一数量的瞬态数据中获取准确的波谱信息，并且在实验中表现出优于其他模型的性能。