  Objective: Magnetic Resonance Spectroscopy (MRS) is a noninvasive tool to
reveal metabolic information. One challenge of MRS is the relatively low
Signal-Noise Ratio (SNR) due to low concentrations of metabolites. To improve
the SNR, the most common approach is to average signals that are acquired in
multiple times. The data acquisition time, however, is increased by multiple
times accordingly, resulting in the scanned objects uncomfortable or even
unbearable. Methods: By exploring the multiple sampled data, a deep learning
denoising approach is proposed to learn a mapping from the low SNR signal to
the high SNR one. Results: Results on simulated and in vivo data show that the
proposed method significantly reduces the data acquisition time with slightly
compromised metabolic accuracy. Conclusion: A deep learning denoising method
was proposed to significantly shorten the time of data acquisition, while
maintaining signal accuracy and reliability. Significance: Provide a solution
of the fundamental low SNR problem in MRS with artificial intelligence.
