Abstract #4631

WALINET+: A water and lipid identification Neural Network for nuisance removal of water Unsuppressed Magnetic Resonance Spectroscopic Imaging.

Paul Weiser^1,2,3, Georg Langs³, Stanislav Motyka⁴, Wolfgang Bogner⁴, Sebastien Courvoisier^5,6, Gulnur Ungan^1,2, Malte Hoffmann^1,2, Antoine Klauser⁷, and Ovidiu Andronesi^1,2

¹Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA, Boston, MA, United States, ²Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston,MA, USA, Boston, MA, United States, ³Computational Imaging Research Lab - Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, Vienna, Austria, ⁴High Field MR Center - Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria, Vienna, Austria, ⁵Center for Biomedical Imaging (CIBM), Geneva, Switzerland, Geneva, Switzerland, ⁶Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland, Geneva, Switzerland, ⁷Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland, Lausanne, Switzerland

Synopsis

Keywords: Artifacts, Spectroscopy, Water and Lipid Removal, Deep Learning

Motivation: Magnetic resonance spectroscopic imaging (MRSI) enables the 3-dimensional visualization of metabolic concentrations in healthy subjects and patients. However, metabolic maps can be distorted due to artifacts originating from large water and lipid signals.

Goal(s): The removal of nuisance signal in water unsuppressed 7T MRSI

Approach: A Deep-Learning based nuisance identification neural network is trained to predict water and lipid signals, which are consequently subtracted from the original input.

Results: Simulated data reveal a thorough removal of nuisance signals. Metabolic maps show an agreement with water suppressed MRSI from the same subject.

Impact: WALINET+ is an initial attempt for a deep learning based removal of nuisance signals in water unsuppressed 7T MRSI, and has the potential of reducing acquisition times by several minutes.

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