ArticleMagnetic resonance in medicine2019
Water removal in MR spectroscopic imaging with L2 regularization.
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Simultaneous Detection of GABA and Glycine Using MEGA-PRESS With TE Optimization at 3T.Magnetic resonance in medicine · 2026Article
- A Digital Phantom for MR Spectroscopy Data Simulation.Magnetic resonance in medicine · 2026Article
- A data-driven algorithm to determineMagnetic resonance in medicine · 2026Article
- Accelerated water residual removal in MRS: Exploring deep learning versus fitting-based approaches.Magnetic resonance in medicine · 2026Article
- WALINET: A water and lipid identification convolutional neural network for nuisance signal removal inMagnetic resonance in medicine · 2025Article
- Rosette Spectroscopic Imaging for Whole-Brain Slab Metabolite Mapping at 7T: Acceleration Potential and Reproducibility.Human brain mapping · 2025Article
- GABA-edited MEGA-PRESS at 3 T: Does a measured macromolecule background improve linear combination modeling?Magnetic resonance in medicine · 2024Article
- Application of aImaging neuroscience (Cambridge, Mass.) · 2023Article
- Magnetic resonance spectroscopic imaging of downfield proton resonances in the human brain at 3 T.Magnetic resonance in medicine · 2022Article
- Accelerated J-resolvedMagnetic resonance in medicine · 2021Article
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Authors and funding
5 authors.
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Abstract
purposeAn L2-regularization based postprocessing method is proposed and tested for removal of residual or unsuppressed water signals in proton MR spectroscopic imaging (MRSI) data recorded from the human brain at 3T.
methodsWater signals are removed by implementation of the L2 regularization using a synthesized water-basis matrix that is orthogonal to metabolite signals of interest in the spectral dimension. Simulated spectra with variable water amplitude and in vivo brain MRSI datasets were used to demonstrate the proposed method. Results were compared with two commonly-used postprocessing methods for removing water signals.
resultsThe L2 method yielded metabolite signals that were close to true values for the simulated spectra. Residual/unsuppressed water signals in human brain short- and long-echo time MRSI datasets were efficiently removed by the proposed method allowing good quality metabolite maps to be reconstructed with minimized contamination from water signals. Significant differences of the creatine signal were observed between brain long-echo time MRSI without and with water saturation, attributable to the previously described magnetization transfer effect.
conclusionsWith usage of a synthesized water matrix generated based on reasonable prior knowledge about water and metabolite resonances, the L2 method is shown to be an effective way to remove water signals from MRSI of the human brain.
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