Accelerating 2D NMR relaxation dispersion experiments using iterated maps

Rovny, Jared; Blum, Robert L.; Loria, J. Patrick; Barrett, Sean

doi:10.1007/s10858-019-00263-3

Cited by 5 publications

(3 citation statements)

References 54 publications

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“…Such analyses result in highly accurate intensities. [22][23][24][25] Recently, NUS has been combined with accordion spectroscopy where the signals in the indirect dimension of a two-dimensional (2D) NMR spectrum are modulated with the relaxation parameter of interest and may be extracted by measuring only two sparsely sampled 2D spectra. 26 Despite the many advantages of NUS, according to Web of Science, the key publications 6,7,[27][28][29] for the five software packages for NUS processing (MddNMR, hmsIST, Cambridge CS, NESTA-NMR, and SMILE), which we consider in this review, have together in total been cited 68 and 78 times per year in 2018 and 2019 compared with more than 600 annual citations of the popular nmrPipe 30 software package for traditional data processing of multidimensional NMR data.…”

Section: Why Use Nonuniform Sampling?mentioning

confidence: 99%

“…The frequencies in a series of spectra may also be used for global optimization across the different relaxation delays comprising a pseudodimension. Such analyses result in highly accurate intensities 22–25 . Recently, NUS has been combined with accordion spectroscopy where the signals in the indirect dimension of a two‐dimensional (2D) NMR spectrum are modulated with the relaxation parameter of interest and may be extracted by measuring only two sparsely sampled 2D spectra 26 …”

Section: Why Use Nonuniform Sampling?mentioning

confidence: 99%

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Software for reconstruction of nonuniformly sampled NMR data

Pedersen

Prestel

Teilum

2020

Magnetic Reson in Chemistry

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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.

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Section: Why Use Nonuniform Sampling?mentioning

confidence: 99%

Section: Why Use Nonuniform Sampling?mentioning

confidence: 99%

Software for reconstruction of nonuniformly sampled NMR data

Pedersen

Prestel

Teilum

2020

Magnetic Reson in Chemistry

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“…In this Letter, we question the possibility of denoising these data through low-rank decomposition methods: the modulation of the signal intensity as a result of the pulse sequence is expected to be captured as the most significant variation across a series of spectra. [3,4] It is worth mentioning that the joint processing of series of signals has been used in other contexts in NMR for sensitivity boosts and better determination of spectral parameters (see for instance previous works [5][6][7][8][9][10][11] ). We also discuss which acquisition protocol can be applied to obtain data of a sufficient quality to be used for quantitative purposes.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity considerations on denoising series of spectra by singular value decomposition

Bruno

Fiorucci

Ravera

2023

Magnetic Reson in Chemistry

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When acquiring series of spectra ( T1,0.1emT2, CP buildup curves, etc.) on samples with poor SNR, we are usually faced with choosing between taking a few points with a large number of scans to maximize the SNR or more points with a smaller number of scans to maximize the information content. In this Letter, we show how low‐rank decomposition can be used to denoise a series of spectra, reducing the trade‐off between the number of scans and the number of experiments.

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