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

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.