Defining the sampling space in multidimensional NMR experiments: What should the maximum sampling time be?

Vosegaard, Thomas; Nielsen, Niels Chr.

doi:10.1016/j.jmr.2009.04.007

Cited by 8 publications

(16 citation statements)

References 21 publications

(29 reference statements)

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“…In this work, Equation is demonstrated by successively processing longer evolution times of a simulated data set containing a synthetic peak and Gaussian noise in Figure . Several insights and discussion points follow, which have been presented by many …”

Section: The Pastmentioning

confidence: 94%

“…For example, the availability of gradients led to drastically shortened phase cycles in nD‐NMR, enabling experimentalists to extend indirect evolution periods. Furthermore, important classes of experimentation reach long evolution times such as in biosolids, which have very short T 2 's, or natural products NMR, which often requires exceptional resolution for structure elucidation. Equation does not include the effects of digital manipulations such as apodization, deconvolution, linear prediction, etc., which have been taken up by others . It is helpful to recognize that Equation describes the intrinsic SNR (iSNR) that is calculated from the exact data stored in the receiver, whereas the apparent SNR is calculated from data that have been digitally manipulated after they were stored .…”

Section: The Pastmentioning

confidence: 99%

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The past, present, and future of 1.26T₂

Rovnyak

2018

Concepts Magnetic Resonance

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This mini‐review considers the scientific and historical development of the constant 1.26T2, which represents the acquisition time for which the signal‐to‐noise ratio of a decaying exponential (with time constant T2) is a maximum in the presence of thermal noise. While first reported in 1977, interest in this result greatly increased after about the year 2000, when it began to influence thinking in nonuniform sampling, sensitivity, and pulse sequence design. Overall, 1.26T2 has become a lens through which to view the evolution of NMR data acquisition and processing. An enduring lesson of the 1.26T2 story is the value of describing and analyzing the properties of magnetic resonance signals in the time domain prior to any further spectral analysis and processing, a concept which is at the core of many modern analytic techniques.

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Section: The Pastmentioning

confidence: 94%

Section: The Pastmentioning

confidence: 99%

The past, present, and future of 1.26T₂

Rovnyak

2018

Concepts Magnetic Resonance

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“…The maximum t1 time was then estimated according to literature with a designed indirect dimension linewidths,[5] and the number of t1 points was determined from the maximum t1 time and the designed frequency range for the indirect dimension. The maximum t1 time was fixed for all the acquisition profiles.…”

Section: Methodsmentioning

confidence: 99%

“…The linewidths increment was calculated by comparing the FWHM of the selected cross peaks (specified in the experimental section) in the corresponding spectrum to the calculated linewidths according to the literature method. [5] The signal enhancement was determined by comparing the cross peak volumes obtained by linear or Gaussian acquisition profiles to the ones by the regular profile. In plots A and C, the spectra with “square acquisition profile” were processed with 100 Hz Gaussian line broadening in both direct and indirect dimension.…”

Section: Figurementioning

confidence: 99%

Signal enhancement for the sensitivity-limited solid state NMR experiments using a continuous, non-uniform acquisition scheme

Wang

2011

Journal of Magnetic Resonance

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We describe a sampling scheme for the two-dimensional (2D) solid state NMR experiments, which can be readily applied to the sensitivity-limited samples. The sampling scheme utilizes continuous, non-uniform sampling profile for the indirect dimension, i.e. the acquisition number decreases as a function of the evolution time (t1) in the indirect dimension. For a beta amyloid (Aβ) fibril sample, we observed overall 40 ~ 50% signal enhancement by measuring the cross peak volume, while the cross peak linewidths remained comparable to the linewidths obtained by regular sampling and processing strategies. Both the linear and Gaussian decay functions for the acquisition numbers result in similar percentage of increment in signal. In addition, we demonstrated that this sampling approach can be applied with different dipolar recoupling approaches such as radiofrequency assisted diffusion (RAD) and finite-pulse radio-frequency-driven recoupling (fpRFDR). This sampling scheme is especially suitable for the sensitivity-limited samples which require long signal averaging for each t1 point, for instance the biological membrane proteins where only a small fraction of the sample is isotopically labeled.

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“…Radial sampling presents relatively small leakage artifacts [3] and Poisson disk sampling is observed to provide a large low-artifact area in the signal vicinity [4]. The maximum sampling time for multi-dimensional NMR experiments was analyzed by Vosegaard and co-workers [5]. Besides the sampling patterns, some reconstruction algorithms have been employed to improve spectral quality, including maximum entropy [6,7], iterative CLEAN algorithm [8] and Bayesian reconstruction [9].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Self-Sparse 2D NMR Spectra from Undersampled Data in the Indirect Dimension

Guo

Cao

et al. 2011

Sensors

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Reducing the acquisition time for two-dimensional nuclear magnetic resonance (2D NMR) spectra is important. One way to achieve this goal is reducing the acquired data. In this paper, within the framework of compressed sensing, we proposed to undersample the data in the indirect dimension for a type of self-sparse 2D NMR spectra, that is, only a few meaningful spectral peaks occupy partial locations, while the rest of locations have very small or even no peaks. The spectrum is reconstructed by enforcing its sparsity in an identity matrix domain with ℓp (p = 0.5) norm optimization algorithm. Both theoretical analysis and simulation results show that the proposed method can reduce the reconstruction errors compared with the wavelet-based ℓ1 norm optimization.

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Defining the sampling space in multidimensional NMR experiments: What should the maximum sampling time be?

Cited by 8 publications

References 21 publications

The past, present, and future of 1.26T₂

The past, present, and future of 1.26T₂

Signal enhancement for the sensitivity-limited solid state NMR experiments using a continuous, non-uniform acquisition scheme

Reconstruction of Self-Sparse 2D NMR Spectra from Undersampled Data in the Indirect Dimension

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Defining the sampling space in multidimensional NMR experiments: What should the maximum sampling time be?

Cited by 8 publications

References 21 publications

The past, present, and future of 1.26T2

The past, present, and future of 1.26T2

Signal enhancement for the sensitivity-limited solid state NMR experiments using a continuous, non-uniform acquisition scheme

Reconstruction of Self-Sparse 2D NMR Spectra from Undersampled Data in the Indirect Dimension

Contact Info

Product

Resources

About

The past, present, and future of 1.26T₂

The past, present, and future of 1.26T₂