Deep learning water-unsuppressed MRSI at ultra-high field for simultaneous quantitative metabolic, susceptibility and myelin water imaging
Paul J. Weiser, Jiye Kim, Jongho Lee, Amirmohammad Shamaei, Gulnur Ungan, Malte Hoffmann, Antoine Klauser, Berkin Bilgic, and Ovidiu C. Andronesi

TL;DR
This paper introduces a deep learning pipeline for ultra-high field water-unsuppressed MRSI that enables simultaneous imaging of metabolites, susceptibility, and myelin water, improving accuracy and efficiency without water suppression.
Contribution
The authors developed WALINET+ and a physics-informed metabolite fitting network to enhance water-unsuppressed MRSI at 7T, enabling simultaneous quantitative imaging with high accuracy.
Findings
WALINET+ achieved less than 2% NRMSE in simulations.
Metabolite SNR was higher in wu-MRSI compared to ws-MRSI.
QSM and MWF measurements showed good agreement with standard methods.
Abstract
Purpose: Magnetic Resonance Spectroscopic Imaging (MRSI) maps endogenous brain metabolism while suppressing the overwhelming water signal. Water-unsuppressed MRSI (wu-MRSI) allows simultaneous imaging of water and metabolites, but large water sidebands cause challenges for metabolic fitting. We developed an end-to-end deep-learning pipeline to overcome these challenges at ultra-high field. Methods:Fast high-resolution wu-MRSI was acquired at 7T with non-cartesian ECCENTRIC sampling and ultra-short echo time. A water and lipid removal network (WALINET+) was developed to remove lipids, water signal, and sidebands. MRSI reconstruction was performed by DeepER and a physics-informed network for metabolite fitting. Water signal was used for absolute metabolite quantification, quantitative susceptibility mapping (QSM), and myelin water fraction imaging (MWF). Results: WALINET+ provided the…
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Taxonomy
TopicsAdvanced MRI Techniques and Applications · NMR spectroscopy and applications · Metabolomics and Mass Spectrometry Studies
