ArticleIEEE transactions on medical imaging2021
Separation of Metabolites and Macromolecules for Short-TE
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Spectral Representation of Neurochemicals With Phase, Frequency Offset, and Lineshape Invariance: Application to JPRESS for In Vivo Concentration and TMagnetic resonance in medicine · 2026Article
- Comparative evaluation of a deep learning method QNet and LCModel for MRS quantification on the cloud computing platform CloudBrain-MRS.BMC medical imaging · 2026Article
- Motion Correction in High-Resolution 3D Brain MRSI Without Water and Lipid Suppression.Magnetic resonance in medicine · 2026Article
- Joint learning of nonlinear representation and projection for fast constrained MRSI reconstruction.Magnetic resonance in medicine · 2025Article
- High-resolutionNMR in biomedicine · 2024Article
- Multi-Parametric Molecular Imaging of the Brain Using Optimized Multi-TE Subspace MRSI.IEEE transactions on bio-medical engineering · 2024Article
- Quantification of spatially localized MRS by a novel deep learning approach without spectral fitting.Magnetic resonance in medicine · 2023Article
- Latent signal models: Learning compact representations of signal evolution for improved time-resolved, multi-contrast MRI.Magnetic resonance in medicine · 2023Article
- 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
- Variational Manifold Learning From Incomplete Data: Application to Multislice Dynamic MRI.IEEE transactions on medical imaging · 2022Article
- SNR Enhancement for Multi-TE MRSI Using Joint Low-Dimensional Model and Spatial Constraints.IEEE transactions on bio-medical engineering · 2022Article
- High-resolution, 3D multi-TEMagnetic resonance in medicine · 2022Article
- Machine Learning-Enabled High-Resolution Dynamic Deuterium MR Spectroscopic Imaging.IEEE transactions on medical imaging · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Short-echo-time (TE) proton magnetic resonance spectroscopic imaging (MRSI) allows for simultaneously mapping a number of molecules in the brain, and has been recognized as an important tool for studying in vivo biochemistry in various neuroscience and disease applications. However, separation of the metabolite and macromolecule (MM) signals present in the short-TE data with significant spectral overlaps remains a major technical challenge. This work introduces a new approach to solve this problem by integrating imaging physics and representation learning. Specifically, a mixed unsupervised and supervised learning-based strategy was developed to learn the metabolite and MM-specific low-dimensional representations using deep autoencoders. A constrained reconstruction formulation is proposed to integrate the MRSI spatiospectral encoding model and the learned representations as effective constraints for signal separation. An efficient algorithm was developed to solve the resulting optimization problem with provable convergence. Simulation and experimental results have been obtained to demonstrate the component-specific representation power of the learned models and the capability of the proposed method in separating metabolite and MM signals for practical short-TE [Formula: see text]-MRSI data.
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