ArticleMagnetic resonance in medicine2019
Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting.
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 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
- Phase Correction of MR Spectroscopic Imaging Data Using Model-Based Signal Estimation and Extrapolation.IEEE transactions on bio-medical engineering · 2026Article
- Region-specific brain metabolite reference estimates and exploratory age-related changes in cognitively intact older adults: a preliminary deep-learningFrontiers in aging neuroscience · 2026Article
- Accelerated water residual removal in MRS: Exploring deep learning versus fitting-based approaches.Magnetic resonance in medicine · 2026Article
- Deep Learning-aidedMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- MRS-Sim: Open-Source Framework for Simulating In Vivo-Like Magnetic Resonance Spectra.NMR in biomedicine · 2025Article
- Deep neural network modeling for brain tumor classification using magnetic resonance spectroscopic imaging.PLOS digital health · 2025Article
- Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging.NeuroImage · 2025Article
- WALINET: A water and lipid identification convolutional neural network for nuisance signal removal inMagnetic resonance in medicine · 2025Article
- NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High-Resolution Short-Echo-Time MR Spectroscopy Datasets.Radiology. Artificial intelligence · 2025Article
- Article
- Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy.Magma (New York, N.Y.) · 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
- Joint spectral quantification of MR spectroscopic imaging using linear tangent space alignment-based manifold learning.Magnetic resonance in medicine · 2023Article
- Model-informed unsupervised deep learning approaches to frequency and phase correction of MRS signals.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
- Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response.International journal of molecular sciences · 2023Review
- Application of aImaging neuroscience (Cambridge, Mass.) · 2023Article
- Deep Learning and Its Application to Function Approximation for MR in Medicine: An Overview.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeMRSI has shown great promise in the detection and monitoring of neurologic pathologies such as tumor. A necessary component of data processing includes the quantitation of each metabolite, typically done through fitting a model of the spectrum to the data. For high-resolution volumetric MRSI of the brain, which may have ~10,000 spectra, significant processing time is required for spectral analysis and generation of metabolite maps.
methodsA novel unsupervised deep learning architecture that combines a convolutional neural network with a priori models of the spectrum is presented. This architecture, a convolutional encoder-model decoder (CEMD), combines the strengths of adaptive and unbiased convolutional networks with models of magnetic resonance and is readily interpretable.
resultsThe CEMD architecture performs accurate spectral fitting for volumetric MRSI in patients with glioblastoma, provides whole-brain fitting in 1 min on a standard computer, and handles a variety of spectral artifacts.
conclusionA new architecture combining physics domain knowledge with convolutional neural networks has been developed and is able to perform rapid spectral fitting of whole-brain data. Rapid processing is a critical step toward routine clinical practice.
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