ArticleJournal of biomolecular NMR2019
Using Deep Neural Networks to Reconstruct Non-uniformly Sampled NMR Spectra.
Article in Journal of biomolecular NMR, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- High-quality pure shift NMR spectra by deep learning using multi-spectral input and joint loss functions.Nature communications · 2026Article
- Fast three-dimensional NMR spectroscopy reconstruction based on Hankel matrix with two-directional sliding windows.Magnetic resonance letters · 2026Article
- Two-dimensional NMR from a single pulse: Reconstructing heteronuclear 2D spectra via off-resonance decoupling and deep neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Toward ultimate NMR resolution with deep learning.Science advances · 2026Article
- Deep Learning Assisted Proton Pure Shift NMR Spectroscopy.Journal of the American Chemical Society · 2026Article
- Simulating the extraction of signal parameters and spectrograms for non-stationary NMR signals.Magnetic resonance (Gottingen, Germany) · 2026Article
- Decoherence Principles and Algorithms for One-Dimensional Nonuniform Sampling Schedules for Multidimensional NMR.Analytical chemistry · 2025Article
- NMR in the Age of Modern Biomedical Research and Drug Discovery.Journal of molecular biology · 2025Review
- Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges.Chemical reviews · 2025Review
- Scalable deep learning reconstruction for accelerated multidimensional nuclear magnetic resonance spectroscopy of proteins.Science advances · 2025Article
- High-confidence reconstruction for Laplace inversion in NMR based on uncertainty-informed deep learning.Science advances · 2025Article
- Compressed Sensing Reconstruction with Zero-Shot Self-Supervised Learning for High-Resolution MRI of Human Embryos.Tomography (Ann Arbor, Mich.) · 2025Article
- ShimNet: A Neural Network for Postacquisition Improvement of NMR Spectra Distorted by Magnetic-Field Inhomogeneity.The journal of physical chemistry. B · 2025Article
- Article
- Deep learning network for NMR spectra reconstruction in time-frequency domain and quality assessment.Nature communications · 2025Article
- A combined NMR and deep neural network approach for enhancing the spectral resolution of aromatic side chains in proteins.Science advances · 2024Article
- Deep learning enabled ultra-high quality NMR chemical shift resolved spectra.Chemical science · 2024Article
- Beyond traditional magnetic resonance processing with artificial intelligence.Communications chemistry · 2024Article
- Fast Pure Shift NMR Spectroscopy Using Attention-Assisted Deep Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Using neural networks to obtain NMR spectra of both small and macromolecules from blood samples in a single experiment.Communications chemistry · 2024Article
Corrections and comments
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1 author.
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Abstract
Non-uniform and sparse sampling of multi-dimensional NMR spectra has over the last decade become an important tool to allow for fast acquisition of multi-dimensional NMR spectra with high resolution. The success of non-uniform sampling NMR hinge on both the development of algorithms to accurately reconstruct the sparsely sampled spectra and the design of sampling schedules that maximise the information contained in the sampled data. Traditionally, the reconstruction tools and algorithms have aimed at reconstructing the full spectrum and thus 'fill out the missing points' in the time-domain spectrum, although other techniques are based on multi-dimensional decomposition and extraction of multi-dimensional shapes. Also over the last decade, machine learning, deep neural networks, and artificial intelligence have seen new applications in an enormous range of sciences, including analysis of MRI spectra. As a proof-of-principle, it is shown here that simple deep neural networks can be trained to reconstruct sparsely sampled NMR spectra. For the reconstruction of two-dimensional NMR spectra, reconstruction using a deep neural network performs as well, if not better than, the currently and widely used techniques. It is therefore anticipated that deep neural networks provide a very valuable tool for the reconstruction of sparsely sampled NMR spectra in the future to come.
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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.