Background: Magnetic resonance spectroscopy (MRS) enables non-invasive
detection and measurement of biochemicals and metabolites. However, MRS has low
signal-to-noise ratio (SNR) when concentrations of metabolites are in the range
of the million molars. Standard approach of using a high number of signal
averaging (NSA) to achieve sufficient NSR comes at the cost of a long
acquisition time. Purpose: We propose to use deep-learning approaches to
denoise MRS data without increasing the NSA. Methods: The study was conducted
using data collected from the brain spectroscopy phantom and human subjects. We
utilized a stack auto-encoder (SAE) network to train deep learning models for
denoising low NSA data (NSA = 1, 2, 4, 8, and 16) randomly truncated from high
SNR data collected with high NSA (NSA=192) which were also used to obtain the
ground truth. We applied both self-supervised and fully-supervised training
approaches and compared their performance of denoising low NSA data based on
improved SNRs. Results: With the SAE model, the SNR of low NSA data (NSA = 1)
obtained from the phantom increased by 22.8% and the MSE decreased by 47.3%.
For low NSA images of the human parietal and temporal lobes, the SNR increased
by 43.8% and the MSE decreased by 68.8%. In all cases, the chemical shift of
NAA in the denoised spectra closely matched with the high SNR spectra,
suggesting no distortion to the spectra from denoising. Furthermore, the
denoising performance of the SAE model was more effective in denoising spectra
with higher noise levels. Conclusions: The reported SAE denoising method is a
model-free approach to enhance the SNR of low NSA MRS data. With the denoising
capability, it is possible to acquire MRS data with a few NSA, resulting in
shorter scan times while maintaining adequate spectroscopic information for
detecting and quantifying the metabolites of interest