WALINET: A water and lipid identification convolutional neural network for nuisance signal removal in 1H$$ {}^1\mathrm{H} $$ MR spectroscopic imaging
WALINET has superior performance for nuisance signal removal and metabolite quantification on whole-brain <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msup><mml:mo> </mml:mo> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msup> <mml:mi>H</mml:mi></mml:mrow> <mml:annotation>$$ {}^1\mathrm{H} $$</mml:annotation></mml:semantics> </mml:math> -MRSI compared with conventional state-of-the-art techniques. This represents a new application of deep learning for MRSI processing, with potential for automated high-throughput workflow.
Authors
- Wolfgang Bogner (ORCID: https://orcid.org/0000-0002-0130-3463)
- Stanislav Motyka (ORCID: https://orcid.org/0000-0002-6314-316X)
- Paul Weiser (ORCID: https://orcid.org/0009-0004-2503-5696)
- Sébastien Courvoisier (ORCID: https://orcid.org/0000-0001-9309-4154)
- Ovidiu C. Andronesi (ORCID: https://orcid.org/0000-0002-7412-0641)
- Georg Langs
- Antoine Klauser (ORCID: https://orcid.org/0000-0003-3019-9914)
- Malte Hoffmann (ORCID: https://orcid.org/0000-0002-5511-0739)
Institutions
- University of Geneva (CH)
- Harvard University (US)
- Massachusetts General Hospital (US)
- Athinoula A. Martinos Center for Biomedical Imaging (US)
- Centre d'Imagerie BioMedicale (CH)
- Siemens (Switzerland) (CH)
- Medical University of Vienna (AT)
Publication Details
- Journal
- Magnetic Resonance in Medicine
- Published
- 2024-12-31
- DOI
- https://doi.org/10.1002/mrm.30402
- Citations
- 4
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.62
Funders
- Austrian Science Fund
- Centre d'Imagerie BioMédicale
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital