ArticleJournal of biomolecular NMR2017
Pitfalls in compressed sensing reconstruction and how to avoid them.
Article in Journal of biomolecular NMR, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- The perspective of artificial intelligence for NMR.Magnetic resonance letters · 2027Review
- Fast three-dimensional NMR spectroscopy reconstruction based on Hankel matrix with two-directional sliding windows.Magnetic resonance letters · 2026Article
- Physics-informed deep learning enables fast ultrahigh-resolution nuclear magnetic resonance spectroscopy.Communications chemistry · 2026Article
- Decoherence Principles and Algorithms for One-Dimensional Nonuniform Sampling Schedules for Multidimensional NMR.Analytical chemistry · 2025Article
- Scalable deep learning reconstruction for accelerated multidimensional nuclear magnetic resonance spectroscopy of proteins.Science advances · 2025Article
- Evaluating metrics of spectral quality in nonuniform sampling.Journal of magnetic resonance open · 2025Article
- Limited-Angle Tomography Using a Neural Network as the Objective Function.International journal of biomedical research & practice · 2025Article
- Fast Pure Shift NMR Spectroscopy Using Attention-Assisted Deep Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Non-uniform sampling of similar NMR spectra and its application to studies of the interaction between alpha-synuclein and liposomes.Journal of biomolecular NMR · 2023Article
- Integrated MRI-guided radiotherapy - opportunities and challenges.Nature reviews. Clinical oncology · 2022Review
- Enhanced Nuclear Magnetic Resonance Spectroscopy with Isotropic Mixing as a Pseudodimension.Analytical chemistry · 2022Article
- Clustered sparsity and Poisson-gap sampling.Journal of biomolecular NMR · 2021Article
- Enhancing Compression Level for More Efficient Compressed Sensing and Other Lessons from NMR Spectroscopy.Sensors (Basel, Switzerland) · 2020Article
- Reaching the sparse-sampling limit for reconstructing a single peak in a 2D NMR spectrum using iterated maps.Journal of biomolecular NMR · 2019Article
- Accelerating 2D NMR relaxation dispersion experiments using iterated maps.Journal of biomolecular NMR · 2019Article
- Article
- Monitoring Hydrogenation Reactions using Benchtop 2D NMR with Extraordinary Sensitivity and Spectral Resolution.ChemistryOpen · 2019Article
- Systematic Evaluation of Non-Uniform Sampling Parameters in the Targeted Analysis of Urine Metabolites byScientific reports · 2018Article
- Absolute Minimal Sampling of Homonuclear 2D NMR TOCSY Spectra for High-Throughput Applications of Complex Mixtures.Angewandte Chemie (International ed. in English) · 2017Article
- Non-uniform sampling in biomolecular NMR.Journal of biomolecular NMR · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multidimensional NMR can provide unmatched spectral resolution, which is crucial when dealing with samples of biological macromolecules. The resolution, however, comes at the high price of long experimental time. Non-uniform sampling (NUS) of the evolution time domain allows to suppress this limitation by sampling only a small fraction of the data, but requires sophisticated algorithms to reconstruct omitted data points. A significant group of such algorithms known as compressed sensing (CS) is based on the assumption of sparsity of a reconstructed spectrum. Several papers on the application of CS in multidimensional NMR have been published in the last years, and the developed methods have been implemented in most spectral processing software. However, the publications rarely show the cases when NUS reconstruction does not work perfectly or explain how to solve the problem. On the other hand, every-day users of NUS develop their rules-of-thumb, which help to set up the processing in an optimal way, but often without a deeper insight. In this paper, we discuss several sources of problems faced in CS reconstructions: low sampling level, missassumption of spectral sparsity, wrong stopping criterion and attempts to extrapolate the signal too much. As an appendix, we provide MATLAB codes of several CS algorithms used in NMR. We hope that this work will explain the mechanism of NUS reconstructions and help readers to set up acquisition and processing parameters. Also, we believe that it might be helpful for algorithm developers.
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