ArticleMagnetic resonance in medicine2021
Method for fast lipid reconstruction and removal processing in
Article in Magnetic resonance in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Reproducibility of Metabolic Mapping Using 2D Multi-Slice Short-TE and GABA-Edited Spin-Echo MRSI at 3T in a Single Protocol.NMR in biomedicine · 2026Article
- Brain Glutathione Levels Associate With Cognitive Performance in Older Adults.Journal of neurochemistry · 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
- Lipid removal in deuterium metabolic imaging (DMI) using spatial prior knowledge.Magnetic resonance (Gottingen, Germany) · 2024Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo develop a new rapid spatial filtering method for lipid removal, fast lipid reconstruction and removal processing (FLIP), which selectively isolates and removes interfering lipid signals from outside the brain in a full-FOV 2D MRSI and whole-brain 3D echo planar spectroscopic imaging (EPSI). THEORY AND
methodsFLIP uses regularized least-squares regression based on spatial prior information from MRI to selectively remove lipid signals originating from the scalp and measure the brain metabolite signals with minimum cross contamination. FLIP is a noniterative approach, thus allowing a rapid processing speed, and uses only spatial information without any spectral priors. The performance of FLIP was compared with the Papoulis-Gerchberg algorithm (PGA), Hankel singular value decomposition (HSVD), and fast image reconstruction with L2 regularization (L2).
resultsFLIP in both 2D and 3D MRSI resulted in consistent metabolite quantification in a greater number of voxels with less concentration variation than other algorithms, demonstrating effective and robust lipid-removal performance. The percentage of voxels that met quality criteria with FLIP, PGA, HSVD, and L2 processing were 90%, 57%, 29%, and 42% in 2D MRSI, and 80%, 75%, 76%, and 74% in 3D EPSI, respectively. The quantification results of full-FOV MRSI using FLIP were comparable to those of volume-localized MRSI, while allowing significantly increased spatial coverage. FLIP performed the fastest in 2D MRSI.
conclusionFLIP is a new lipid-removal algorithm that promises fast and effective lipid removal with improved volume coverage in MRSI.
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