WAND: Wavelet Analysis-Based Neural Decomposition of MRS Signals for Artifact Removal
Accurate metabolite quantification in MR spectroscopy (MRS) is challenged by low signal-to-noise ratio, overlapping metabolites, and unknown, unparameterized baseline artifacts that obscure low-concentration metabolites. This paper introduces wavelet analysis-based neural decomposition (WAND), a data-driven method that decomposes MRS signals into metabolite-specific signals, baseline, and artifacts, exploiting how cleanly these components separate in the wavelet domain. A U-Net is trained to predict masks over wavelet coefficients from the continuous wavelet transform, isolating each component before inverse-transforming back to separated signals; the artifact mask is obtained by inverting the sum of all known signal masks, letting WAND capture and remove even artifacts it was never explicitly shown. WAND's accurate decomposition is demonstrated across both simulated and in vivo data.
Abstract
Accurate quantification of metabolites in magnetic resonance spectroscopy (MRS) is challenged by low signal-to-noise ratio (SNR), overlapping metabolites, and various artifacts. Particularly, unknown and unparameterized baseline effects obscure the quantification of low-concentration metabolites, limiting MRS reliability. This paper introduces wavelet analysis-based neural decomposition (WAND), a novel data-driven method designed to decompose MRS signals into their constituent components: metabolite-specific signals, baseline, and artifacts. WAND takes advantage of the enhanced separability of these components within the wavelet domain.
The method employs a neural network, specifically a U-Net architecture, trained to predict masks for wavelet coefficients obtained through the continuous wavelet transform. These masks effectively isolate desired signal components in the wavelet domain, which are then inverse-transformed to obtain separated signals. Notably, an artifact mask is created by inverting the sum of all known signal masks, enabling WAND to capture and remove even unpredictable artifacts. The effectiveness of WAND in achieving accurate decomposition is demonstrated across simulated and in vivo data.