NMR lineshapes and lineshape fitting procedures

Higinbotham, J.; Marshall, Ian

doi:10.1016/s0066-4103(01)43009-2

Cited by 29 publications

(15 citation statements)

References 92 publications

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“…Automatic quantitation of signals from other NMR visible nuclei like 31 P, 13 C, 19 F, etc., is also possible. As an example, Fig.…”

Section: Quantitation Of Experimental Datamentioning

confidence: 99%

“…As a consequence, missing initial and/or final data points do not really hamper the quantitation: one can omit such points from the fit. 19 In the frequency-or transform-domain, the effect of missing data is spread over the entire spectrum and hence is more difficult to handle. A second useful feature of the time-domain is that it enables one to automatically process water 20 and background signals 21,22 with SVD-based methods.…”

Section: Introductionmentioning

confidence: 99%

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Time-domain semi-parametric estimation based on a metabolite basis set

Ratiney¹,

Sdika²,

Coenradie³

et al. 2005

NMR Biomed.

255

296

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A novel and fast time-domain quantitation algorithm--quantitation based on semi-parametric quantum estimation (QUEST)--invoking optimal prior knowledge is proposed and tested. This nonlinear least-squares algorithm fits a time-domain model function, made up from a basis set of quantum-mechanically simulated whole-metabolite signals, to low-SNR in vivo data. A basis set of in vitro measured signals can be used too. The simulated basis set was created with the software package NMR-SCOPE which can invoke various experimental protocols. Quantitation of 1H short echo-time signals is often hampered by a background signal originating mainly from macromolecules and lipids. Here, we propose and compare three novel semi-parametric approaches to handle such signals in terms of bias-variance trade-off. The performances of our methods are evaluated through extensive Monte-Carlo studies. Uncertainty caused by the background is accounted for in the Cramér-Rao lower bounds calculation. Valuable insight about quantitation precision is obtained from the correlation matrices. Quantitation with QUEST of 1H in vitro data, 1H in vivo short echo-time and 31P human brain signals at 1.5 T, as well as 1H spectroscopic imaging data of human brain at 1.5 T, is demonstrated.

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“…Automatic quantitation of signals from other NMR visible nuclei like 31 P, 13 C, 19 F, etc., is also possible. As an example, Fig.…”

Section: Quantitation Of Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Time-domain semi-parametric estimation based on a metabolite basis set

Ratiney¹,

Sdika²,

Coenradie³

et al. 2005

NMR Biomed.

255

296

View full text Add to dashboard Cite

show abstract

“…Specialized lineshape fitting routines are used to extract anisotropic parameters and obtain the principal components of tensors of interest, such as the fits shown in Figures 2.1, 2.2 and 2.3. [121][122][123] More details about quantitative processing of SSNMR data for drug development applications, including quantitative spectral analysis, deconvolution and nonlinear fitting of relaxation data, can be found elsewhere. 123 …”

Section: Special Processing Methodsmentioning

confidence: 99%

Chapter 2. Solid‐State NMR in Drug Discovery and Development

Vogt¹

2013

New Developments in NMR

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“…In this work, we are primarily interested in nuclear magnetic resonance (NMR) spectroscopy, where the signal parameters correspond to properties of the material under study, such as intra-molecular forces. Historically, the model most commonly considered is the so-called Lorentzian line shape [4], i.e., wherein the decay of the signal components are modeled as an exponential first-degree polynomial, although more detailed signal models are also common, such as the Voigt line shape, which use a second-order polynomial decay [5].…”

Section: Introductionmentioning

confidence: 99%

Mismatched Estimation of Polynomially Damped Signals

Elvander

Swärd

Jakobsson

2019

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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In this work, we consider the problem of estimating the parameters of polynomially damped sinusoidal signals, commonly encountered in, for instance, spectroscopy. Generally, finding the parameter values of such signals constitutes a highdimensional problem, often further complicated by not knowing the number of signal components or their specific signal structures. In order to alleviate the computational burden, we herein propose a mismatched estimation procedure using simplified, approximate signal models. Despite the approximation, we show that such a procedure is expected to yield predictable results, allowing for statistically and computationally efficient estimates of the signal parameters.Index Terms-Mismatched estimation, computational efficiency, NMR spectroscopy, Lorentzian and Voigt line shapes.

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NMR lineshapes and lineshape fitting procedures

Cited by 29 publications

References 92 publications

Time-domain semi-parametric estimation based on a metabolite basis set

Time-domain semi-parametric estimation based on a metabolite basis set

Chapter 2. Solid‐State NMR in Drug Discovery and Development

Mismatched Estimation of Polynomially Damped Signals

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