Signal-to-noise Ratio Enhancement of 31P Magnetic Resonance Spectroscopy using a Pre-trained Deep Learning Model

We demonstrate the feasibility of a novel denoising approach utilizing a pretrained deep learning model with multiscale local polynomial smoothing for single voxel 31P MRS data in the mice brain at 9.4T. We evaluated the low-rank denoising, one of the popular methods and the proposed method using LCModel to compare their performance. Both methods resulted in improved signal-to-noise ratio and decreased uncertainty (Cramer-Rao Lower Bounds). In this work, the suggested method outperformed in signal-to-noise ratio enhancement.