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Improving resolution in multidimensional NMR using random quadrature detection with compressed sensing reconstruction


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Publication Date: 2016-09-20

Journal Title: Journal of Biomolecular NMR

Publisher: Springer

Language: English

Type: Article

This Version: VoR

Metadata: Show full item record

Citation: Bostock, M. J., Holland, D. J., & Nietlispach, D. (2016). Improving resolution in multidimensional NMR using random quadrature detection with compressed sensing reconstruction. Journal of Biomolecular NMR https://doi.org/10.1007/s10858-016-0062-9

Description: This is the final version of the article. It first appeared from Springer via https://doi.org/10.1007/s10858-016-0062-9

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$_{RQD}$), high quality reconstructions are achieved. RQD is modular and combined with non-uniform sampling we show that this provides increased flexibility in designing sampling schedules leading to improved resolution with increasing benefits as dimensionality of experiments increases, with particular advantages for 4- and higher dimensional experiments.

Keywords: compressed sensing, non-uniform sampling, $\textit{l}$$_{1}$-norm minimisation, NMR spectroscopy, random quadrature detection (RQD), gradient selection, CS$_{RQD}$

Sponsorship: Part of this work was performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk/), provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England and funding from the Science and Technology Facilities Council.

Embargo Lift Date: 2100-01-01

Identifiers:

External DOI: https://doi.org/10.1007/s10858-016-0062-9

This record's URL: https://www.repository.cam.ac.uk/handle/1810/261041



Rights: Attribution 4.0 International

Licence URL: http://creativecommons.org/licenses/by/4.0/





Autor: Bostock, M. J.Holland, D. J.Nietlispach, D.

Fuente: https://www.repository.cam.ac.uk/handle/1810/261041



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