Data-Driven MRS Signal Decomposition Using Wavelet Analysis

Abstract

Motivation: Magnetic resonance spectroscopy (MRS) is currently limited by noise, low spatial resolution, and artifacts that compromise the accuracy of metabolite quantification. Goal(s): This work aims to enhance MRS signal quality without compromising signal integrity, employing wavelet analysis for robust signal decomposition. Approach: A novel method utilizing wavelet analysis and a U-Net architecture creates masks that segment scalograms, effectively isolating individual metabolites in MRS signals. Results: The method has shown in simulations the ability to distinctly separate and characterize metabolite signals, offering a promising direction for refining MRS data analysis. Impact: Provides a data-driven method for MRS signal decomposition based on wavelet analysis that shows success in extracting metabolite and baseline information. It holds the potential for accurate characterization of nuisance signals, which could lead to improved MRS fitting.