A comparison of denoising methods in dynamic MRS using pseudo-synthetic data

PurposeMR spectroscopy of dynamic systems is limited by low signal to noise. Denoising along a series of acquired spectra exploits their temporal correlation to improve the quality of individual spectra, and reduce errors in fitting metabolite peaks. In this study we compare the performance of several denoising methods. MethodsSix different denoising methods were considered: SIFT (Spectral Improvement by Fourier Thresholding), HSVD (Hankel Singular Value Decomposition), spline, wavelet, sliding window and sliding Gaussian. Pseudo-synthetic data was constructed to mimic 31Phosphorus spectra from exercising muscle. For each method the optimal tuning parameters were determined for SNRs of 2, 5, 10 and 20 using a Monte Carlo approach. Denoised data from each method was then fitted using the AMARES algorithm and the results compared to the pseudo-synthetic ground truth. ResultsAll six methods produced improvements in both fitting accuracy and agreement with the ground truth, compared to unprocessed noisy data. The least effective methods, SIFT and HSVD, achieved around 10-20% reduction in RMS error, while the most effective, Spline, reduced RMS error by 70%. The improvement from denoising was typically greater for lower SNR data. ConclusionsIndirect time domain denoising of dynamic MR spectroscopy data can substantially improve subsequent metabolite fitting. Spline-based denoising was found to be the most flexible and effective technique.