WALRUS: Robust and Transferable Deep Learning for Water and Lipid Removal in 1H-MRSI

Proton magnetic resonance spectroscopic imaging (1H-MRSI) enables non-invasive mapping of brain metabolism, but reliable quantification is challenged by residual water and extracranial lipid contamination. Recent work demonstrated that a deep learning model trained on simulated metabolite spectra combined with in vivo-derived water and lipid signals can outperform conventional nuisance-removal methods. However, this approach relied on heuristic simulation parameter distributions and a subject-specific L2-derived lipid estimate as an auxiliary network input, requiring subject-specific lipid mas

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