Denoising Magnetic Resonance Spectroscopy (MRS) Data Using Stacked Autoencoder for Improving Signal-to-Noise Ratio and Speed of MRS

• Published in: arXiv.org
• Main Authors: Wang, Jing, Ji, Bing, Yang, Lei, Liu, Tian, Mao, Hui, Yang, Xiaofeng
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 29.03.2023
• Subjects: Background noise; Chemical equilibrium; Coders; Deep learning; Magnetic resonance spectroscopy; Metabolites; Noise levels; Noise reduction; Signal averaging; Signal to noise ratio; Spectra; Spectrum analysis
• ISSN: 2331-8422

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 si...