ArticleJournal of biomolecular NMR2017
Improving resolution in multidimensional NMR using random quadrature detection with compressed sensing reconstruction.
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. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Beyond traditional magnetic resonance processing with artificial intelligence.Communications chemistry · 2024Article
- nus-tool: A unified program for generating and analyzing sample schedules for nonuniformly sampled NMR experiments.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2023Article
- Emerging solution NMR methods to illuminate the structural and dynamic properties of proteins.Current opinion in structural biology · 2019Review
- Time-domain signal modelling in multidimensional NMR experiments for estimation of relaxation parameters.Journal of biomolecular NMR · 2019Article
- Beyond Fourier.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2017Article
- Non-uniform sampling in biomolecular NMR.Journal of biomolecular NMR · 2017Article
- Modern Technologies of Solution Nuclear Magnetic Resonance Spectroscopy for Three-dimensional Structure Determination of Proteins Open Avenues for Life Scientists.Computational and structural biotechnology journal · 2017Review
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Authors and funding
3 authors.
Funding
Abstract
NMR spectroscopy is central to atomic resolution studies in biology and chemistry. Key to this approach are multidimensional experiments. Obtaining such experiments with sufficient resolution, however, is a slow process, in part since each time increment in every indirect dimension needs to be recorded twice, in quadrature. We introduce a modified compressed sensing (CS) algorithm enabling reconstruction of data acquired with random acquisition of quadrature components in gradient-selection NMR. We name this approach random quadrature detection (RQD). Gradient-selection experiments are essential to the success of modern NMR and with RQD, a 50 % reduction in the number of data points per indirect dimension is possible, by only acquiring one quadrature component per time point. Using our algorithm (CS
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