ArticleIEEE transactions on bio-medical engineering2017
Spectral Quantification for High-Resolution MR Spectroscopic Imaging With Spatiospectral Constraints.
Article in IEEE transactions on bio-medical engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Physics-embedded CycleGAN for robust metabolite mapping in short-readout deuterium metabolic imaging.Magnetic resonance letters · 2026Article
- Motion Correction in High-Resolution 3D Brain MRSI Without Water and Lipid Suppression.Magnetic resonance in medicine · 2026Article
- High-Resolution Brain Metabolic Imaging at Ultrahigh Field Using Extended Spatiospectral Encoding and Subspace Modeling.IEEE transactions on bio-medical engineering · 2025Article
- Joint spectral quantification of MR spectroscopic imaging using linear tangent space alignment-based manifold learning.Magnetic resonance in medicine · 2023Article
- A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images.Journal of imaging · 2022Article
- High-resolution, 3D multi-TEMagnetic resonance in medicine · 2022Article
- Accelerated J-resolvedMagnetic resonance in medicine · 2021Article
- A Subspace Approach to Spectral Quantification for MR Spectroscopic Imaging.IEEE transactions on bio-medical engineering · 2017Article
- Imaging based magnetic resonance spectroscopy (MRS) localization for quantitative neurochemical analysis and cerebral metabolism studies.Analytical biochemistry · 2017Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveTo obtain reliable spectral estimation from magnetic resonance spectroscopic imaging (MRSI) data.
methodsThe proposed method takes advantage of prior knowledge: 1) along the spectral dimension in the form of spectral bases, and 2) along the spatial dimensions in the form of spatial regularizations (e.g., smoothness or transform sparsity) and jointly estimates parameters from all the voxels.
resultsSimulation and in vivo studies have been performed to demonstrate the performance of the proposed method. A Cramér-Rao-bound-based analysis is also provided.
conclusionIncorporation of both spatial and spectral constraints can significantly improve spectral quantification of MRSI data. SIGNIFICANCE: The proposed method is expected to be useful for various quantitative MRSI studies.
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Registered trials
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