ArticleIEEE transactions on bio-medical engineering2022
SNR Enhancement for Multi-TE MRSI Using Joint Low-Dimensional Model and Spatial Constraints.
Article in IEEE transactions on bio-medical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 9 citations in OpenAlex.
- Physics-embedded CycleGAN for robust metabolite mapping in short-readout deuterium metabolic imaging.Magnetic resonance letters · 2026Article
- From equilibrium to dynamic metabolic imaging: the evolving role of magnetic resonance spectroscopic imaging in neurodegenerative disorders.Journal of translational medicine · 2026Review
- 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
- Joint learning of nonlinear representation and projection for fast constrained MRSI reconstruction.Magnetic resonance in medicine · 2025Article
- Simultaneous Concentration and TNMR in biomedicine · 2025Article
- Improving Brain Metabolite Detection with a Combined Low-Rank Approximation and Denoising Diffusion Probabilistic Model Approach.Bioengineering (Basel, Switzerland) · 2024Article
- 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
- 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
3 authors at 1 institution in 1 country.
Funding
Abstract
We present a novel method to enhance the SNR for multi-TE MR spectroscopic imaging (MRSI) data by integrating learned nonlinear low-dimensional model and spatial constraints. A deep complex convolutional autoencoder (DCCAE) was developed to learn a nonlinear low-dimensional representation of the high-dimensional multi-TE
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What OpenQuestion holds
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.