WALINET: A water and lipid identification convolutional neural network for nuisance signal removal in MR spectroscopic imaging.

Journal: Magnetic resonance in medicine
PMID: 39737778

Abstract

PURPOSE: Proton magnetic resonance spectroscopic imaging ( -MRSI) provides noninvasive spectral-spatial mapping of metabolism. However, long-standing problems in whole-brain -MRSI are spectral overlap of metabolite peaks with large lipid signal from scalp, and overwhelming water signal that distorts spectra. Fast and effective methods are needed for high-resolution -MRSI to accurately remove lipid and water signals while preserving the metabolite signal. The potential of supervised neural networks for this task remains unexplored, despite their success for other MRSI processing.

Authors

  • Paul J Weiser
    Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Georg Langs
    Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab, Medical University of Vienna Vienna Austria.
  • Stanislav Motyka
    High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
  • Wolfgang Bogner
    High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
  • Sébastien Courvoisier
    Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
  • Malte Hoffmann
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Radiology Department, Boston, MA, USA.
  • Antoine Klauser
    Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
  • Ovidiu C Andronesi
    Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA.

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