Wand: Wavelet Analysis-based Neural Decomposition of MRS Signals for Artifact Removal

Onderzoeksoutput: WerkdocumentPreprintProfessioneel

6 Downloads (Pure)

Samenvatting

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 through numerical evaluations using simulated spectra. Furthermore, WAND's artifact removal capabilities significantly enhance the quantification accuracy of linear combination model fitting. The method's robustness is further validated using data from the 2016 MRS Fitting Challenge and in-vivo experiments.
Originele taal-2Engels
UitgeverarXiv.org
Aantal pagina's23
Volume2410.10427
DOI's
StatusGepubliceerd - 14 okt. 2024

Bibliografische nota

Submitted to NMR in Biomedicine

Trefwoorden

  • eess.SP

Vingerafdruk

Duik in de onderzoeksthema's van 'Wand: Wavelet Analysis-based Neural Decomposition of MRS Signals for Artifact Removal'. Samen vormen ze een unieke vingerafdruk.

Citeer dit