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Software for reconstruction of nonuniformly sampled NMR data
Author(s) -
Pedersen Christian Parsbæk,
Prestel Andreas,
Teilum Kaare
Publication year - 2021
Publication title -
magnetic resonance in chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.483
H-Index - 72
eISSN - 1097-458X
pISSN - 0749-1581
DOI - 10.1002/mrc.5060
Subject(s) - sampling (signal processing) , software , fourier transform , nmr spectra database , spectral line , two dimensional nuclear magnetic resonance spectroscopy , heteronuclear single quantum coherence spectroscopy , algorithm , nonuniform sampling , chemistry , computer science , nuclear magnetic resonance , physics , mathematics , computer vision , filter (signal processing) , mathematical analysis , astronomy , quantization (signal processing) , programming language
Abstract Nonuniform sampling (NUS) of multidimensional NMR experiments is a powerful tool to obtain high‐resolution spectra with less instrument time. With NUS, only a subset of the points needed for conventional Fourier transformation is recorded, and sophisticated algorithms are needed to reconstruct the missing data points. During the last decade, several software packages implementing the reconstruction algorithms have emerged and been refined and now result in spectra of almost similar quality as spectra from conventionally recorded and processed data. However, from the number of literature references to the reconstruction methods, many more multidimensional NMR spectra could presumably be recorded with NUS. To help researchers considering to start using NUS for their NMR experiments, we here review 13 different reconstruction methods found in five software packages (CambridgeCS, hmsIST, MddNMR, NESTA‐NMR, and SMILE). We have compared how the methods run with the provided example scripts for reconstructing a nonuniform sampled three‐dimensional 15 N–NOESY–HSQC at sampling densities from 5% to 50%. Overall, the spectra are all of similar quality above 20% sampling density. Thus, without any particular knowledge about the details of the reconstruction algorithms, significant reduction in the experiment time can be achieved. Below 20% sampling density, the intensities of particular weak peaks start being affected. MddNMR's IST with virtual echo and the SMILE algorithms still reproduced the spectra with the highest accuracy of peak intensities.