ArticleIEEE transactions on medical imaging2021
Machine Learning-Enabled High-Resolution Dynamic Deuterium MR Spectroscopic Imaging.
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 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Advances and prospects in deuterium metabolic imaging (DMI): a systematic review of in vivo studies.European radiology experimental · 2024Pooled it
- Unraveling the role of deuterium in cancer: mechanisms, detection techniques, and therapeutic potential.Molecular diversity · 2026Review
- Denoising of ASL Data Using Deep Learning Priors Generated From Distribution Remapping.Magnetic resonance in medicine · 2026Article
- Assessing the treatment of pancreatic ductal adenocarcinoma by deuterium metabolic imaging: a preclinical study.Magma (New York, N.Y.) · 2026Article
- Advanced methods in deuterium metabolic imaging.Magma (New York, N.Y.) · 2026Review
- Multiplexed magnetic resonance imaging.Nature · 2026Article
- Phase Correction of MR Spectroscopic Imaging Data Using Model-Based Signal Estimation and Extrapolation.IEEE transactions on bio-medical engineering · 2026Article
- Advancing Deuterium MRI to Track Human Cerebral Glucose Metabolism at 7 T: A Comparison of Glucose-dNMR in biomedicine · 2025Article
- Comparison of Low-Rank Denoising Methods for Dynamic Deuterium MRSI at 7 T.NMR in biomedicine · 2025Article
- MR Spatiospectral Reconstruction Integrating Subspace Modeling and Self-Supervised Spatiotemporal Denoising.IEEE transactions on medical imaging · 2025Article
- Quantitative mapping of key glucose metabolic rates in the human brain using dynamic deuterium magnetic resonance spectroscopic imaging.PNAS nexus · 2025Article
- Concentric Ring Trajectory Sampling With k-Space Reordering Enables Assessment of Tissue-Specific TNMR in biomedicine · 2025Article
- Joint learning of nonlinear representation and projection for fast constrained MRSI reconstruction.Magnetic resonance in medicine · 2025Article
- Simultaneous assessment of cerebral glucose and oxygen metabolism and perfusion in rats using interleaved deuterium (NMR in biomedicine · 2025Article
- Multi-Parametric Molecular Imaging of the Brain Using Optimized Multi-TE Subspace MRSI.IEEE transactions on bio-medical engineering · 2024Article
- Whole-brain deuterium metabolic imaging via concentric ring trajectory readout enables assessment of regional variations in neuronal glucose metabolism.Human brain mapping · 2024Article
- QuantitativeMagnetic resonance in medicine · 2024Article
- Mapping intracellular NAD content in entire human brain using phosphorus-31 MR spectroscopic imaging at 7 Tesla.Frontiers in neuroscience · 2024Article
- Dual-frequency resonant coil design for low-γ X-nuclear and proton magnetic resonance imaging at ultrahigh fields.NMR in biomedicine · 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
10 authors at 2 institutions in 1 country.
Funding
Abstract
Deuterium magnetic resonance spectroscopic imaging (DMRSI) has recently been recognized as a potentially powerful tool for noninvasive imaging of brain energy metabolism and tumor. However, the low sensitivity of DMRSI has significantly limited its utility for both research and clinical applications. This work presents a novel machine learning-based method to address this limitation. The proposed method synergistically integrates physics-based subspace modeling and data-driven deep learning for effective denoising, making high-resolution dynamic DMRSI possible. Specifically, a novel subspace model was used to represent the dynamic DMRSI signals; deep neural networks were trained to capture the low-dimensional manifolds of the spectral and temporal distributions of practical dynamic DMRSI data. The learned subspace and manifold structures were integrated via a regularization formulation to remove measurement noise. Theoretical analysis, computer simulations, and in vivo experiments have been conducted to demonstrate the denoising efficacy of the proposed method which enabled high-resolution imaging capability. The translational potential was demonstrated in tumor-bearing rats, where the Warburg effect associated with cancer metabolism and tumor heterogeneity were successfully captured. The new method may not only provide an effective tool to enhance the sensitivity of DMRSI for basic research and clinical applications but also provide a framework for denoising other spatiospectral data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.