ArticleIEEE transactions on bio-medical engineering2017
A Subspace Approach to Spectral Quantification for MR Spectroscopic Imaging.
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 30 papers.
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Who cites it
30 citing papers in PubMed.
- Physics-embedded CycleGAN for robust metabolite mapping in short-readout deuterium metabolic imaging.Magnetic resonance letters · 2026Article
- From Water Imaging to Tissue State Imaging: What Multiplexed MRI Adds to Quantitative MRI.Korean journal of radiology · 2026Article
- Multiplexed magnetic resonance imaging.Nature · 2026Article
- Motion Correction in High-Resolution 3D Brain MRSI Without Water and Lipid Suppression.Magnetic resonance in medicine · 2026Article
- Dual association patterns between microglial activation and neuronal health in Alzheimer's disease: a whole-brain MRSI/PET study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Iron Overload Mediates the Differential Cell Fate of Astrocytes from Neurons and Its Regulatory Mechanisms in Ischemic Stroke.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Study protocol for the Champaign-Urbana population study.Frontiers in neuroimaging · 2026Article
- High-Resolution Brain Metabolic Imaging at Ultrahigh Field Using Extended Spatiospectral Encoding and Subspace Modeling.IEEE transactions on bio-medical engineering · 2025Article
- Ultrafast J-resolved magnetic resonance spectroscopic imaging for high-resolution metabolic brain imaging.Nature biomedical engineering · 2025Article
- High-resolution, volumetric diffusion-weighted MR spectroscopic imaging of the brain.Magnetic resonance in medicine · 2025Article
- MR Spatiospectral Reconstruction Integrating Subspace Modeling and Self-Supervised Spatiotemporal Denoising.IEEE transactions on medical imaging · 2025Article
- Associations between Aβ deposition and neurometabolic alterations in Alzheimer's disease: Insights from hybrid 3D MRSI-PET imaging.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- 3D deuterium metabolic imaging (DMI) of the human liver at 7 T using low-rank and subspace model-based reconstruction.Magnetic resonance in medicine · 2025Article
- Accelerated 3D metabolite TMagnetic resonance in medicine · 2024Article
- Neurometabolic topography and associations with cognition in Alzheimer's disease: A whole-brain high-resolution 3D MRSI study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Multi-Parametric Molecular Imaging of the Brain Using Optimized Multi-TE Subspace MRSI.IEEE transactions on bio-medical engineering · 2024Article
- Simultaneous high-resolution whole-brain MR spectroscopy and [European journal of nuclear medicine and molecular imaging · 2024Article
- Mapping intracellular NAD content in entire human brain using phosphorus-31 MR spectroscopic imaging at 7 Tesla.Frontiers in neuroscience · 2024Article
- Joint spectral quantification of MR spectroscopic imaging using linear tangent space alignment-based manifold learning.Magnetic resonance in medicine · 2023Article
- High-Dimensional MR Spatiospectral Imaging by Integrating Physics-Based Modeling and Data-Driven Machine Learning: Current progress and future directions.IEEE signal processing magazine · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveTo provide a new approach to spectral quantification for magnetic resonance spectroscopic imaging (MRSI), incorporating both spatial and spectral priors.
methodsA novel signal model is proposed, which represents the spectral distributions of each molecule as a subspace and the entire spectrum as a union of subspaces. Based on this model, the spectral quantification can be solved in two steps: 1) subspace estimation based on the empirical distributions of the spectral parameters estimated using spectral priors; and 2) parameter estimation for the union-of-subspaces model incorporating spatial priors.
resultsThe proposed method has been evaluated using both simulated and experimental data, producing impressive results.
conclusionThe proposed union-of-subspaces representation of spatiospectral functions provides an effective computational framework for solving the MRSI spectral quantification problem with spatiospectral constraints. SIGNIFICANCE: The proposed approach transforms how the MRSI spectral quantification problem is solved and enables efficient and effective use of spatiospectral priors to improve parameter estimation. The resulting algorithm is expected to be useful for a wide range of quantitative metabolic imaging studies using MRSI.
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Registered trials
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