Deep-ECCENTRIC: Deep Learning Reconstruction of whole-brain non-Cartesian Compressed-Sensing MR Spectroscopic Imaging

Motivation: Magnetic resonance spectroscopic imaging (MRSI) is a unique method for non-invasive mapping of brain neurochemistry. While the latest advancements in acquisition enable whole-brain high-resolution metabolic imaging, these methods have lengthy reconstruction times that limit the clinical use. Goal(s): To realize a fast end-to-end reconstruction pipeline for high-resolution whole-brain MRSI compatible with online processing and clinical use. Approach: We developed a rapid deep-learning reconstruction pipeline for 3D non-Cartesian Compressed-Sensing MRSI (ECCENTRIC). Results: Our approach reconstructs in a few minutes high-resolution ECCENTRIC (k,t) data. We demonstrate a 60-fold speed-up in reconstruction time, facilitating the use in clinical routine. Impact: We present Deep-ECCENTRIC: a deep-learning pipeline for end-to-end reconstruction of 3D non-Cartesian Compressed-Sensing MRSI. We showcase spatially precise reconstructions with high spectral consistency, at a 60-fold speed-up over conventional reconstructions, which facilitates the clinical use of fast high-resolution MRSI.