Abstract #0355

LIPCON: Lipid Identification with Convolutional Neural Network for MR Spectroscopic Imaging

Paul Weiser^1,2,3, Georg Langs³, Stanislav Motyka⁴, Bern Strasser⁴, Wolfgang Bogner⁴, Sébastien Courvoisier^5,6, Malte Hoffmann^1,2, Ovidiu Andronesi*^1,2, and Antoine Klauser*^1,5,7

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

Synopsis

Keywords: AI/ML Image Reconstruction, Spectroscopy, Lipid-Suppression, Brain, High-Field MR, Deep Learning

Motivation: Magnetic Resonance Spectroscopic Imaging (MRSI) offers non-invasive metabolic concentration mapping, aiding early pathology detection like brain tumors. However, extracranial lipid signals can compromise neurochemical data.

Goal(s): The potential of supervised neural networks remains unexplored, despite their success in other artifact removal and metabolite quantification tasks. We introduce a deep learning method for robust lipid removal.

Approach: Our approach is compared to a state-of-the-art L2-lipid-regularization using simulated and in-vivo whole-brain MRSI data.

Results: Our supervised deep learning method showed improved performance to the L2-lipid-regularization method by eliminating more lipid signal while preserving metabolic signals and spectral baseline.

Impact: The LIPCON method achieves accurate lipid suppression across whole-brain MRSI datasets without the need for parameter tuning and within a few seconds. This should mark a step in enhancing the reproducibility and efficiency of MRSI pipelines.

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