The Use of Continuous Regularization in the Automated Analysis of MRS Time-Domain Data

Totz, J.; Boogaart, Aad van den; Huffel, Sabine Van; Graveron‐Demilly, D.; Dologlou, Ioannis; Heidler, R.; Michel, D.

doi:10.1006/jmre.1996.1043

Cited by 13 publications

(5 citation statements)

References 7 publications

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“…By iterating this diagonalizationprojection-averaging procedure several times, we eventually get a K-rank Hankel matrix approximation to the original full rank noisy data matrix. Harmonic inversion methods are seen by us and others 18,19 to give significantly improved results when fed signals resulting from the Cadzow iterative scheme. Much of the past harmonic inversion work omitted this step.…”

Section: Windowingmentioning

confidence: 97%

Saving Measurement Time in ¹³C NMR Spectroscopy

Kunikeev

Taylor

2004

J. Phys. Chem. A

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An algorithm to produce acceptably accurate Fourier transform NMR spectra using many fewer transients than commonly obtained is introduced and applied to 13C chemical shift and one-dimensional INADEQUATE Lorentzian type spectra. The algorithm supplies criteria to recognize when to stop transient collection at a time too early for an acceptable spectrum to be produced by Fourier signal processing but not by harmonic inversion signal processing.

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Section: Windowingmentioning

confidence: 97%

Saving Measurement Time in ¹³C NMR Spectroscopy

Kunikeev

Taylor

2004

J. Phys. Chem. A

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“…Reference is a black box method with accompanying software that employs Cadzow’s preconditioning method and a Tikhanov type regularization called continuous regularization . Continuous regularization involves the calculation of polynomial coefficients in a way that minimizes a linear combination of the difference between the residual of the LP equation and the weighted norm of the solution vector whose elements are the LP coefficients.…”

Section: Relation To Other Workmentioning

confidence: 99%

“…Again, the method probably could not be used for the purposes of this paper. In fairness, the authors of ref never claimed it should be used for other than quantification at higher SNRs in MRS applications with signals obtained from the human brain.…”

Section: Relation To Other Workmentioning

confidence: 99%

Increasing Sensitivity in Determining Chemical Shifts in One Dimensional Lorentzian NMR Spectra

2013

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An algorithm is presented for one-dimensional NMR systems that employs nonlinear, non-Fourier methods to convert noisy time-dependent free induction decay (FID) data to a denoised frequency spectrum that gives reliable chemical shifts and coupling constants when the spectrum is Lorentzian. It is formulated in a way that increases frequency sensitivity and resolution and, for nuclei of low natural abundance, potentially avoids enrichment totally or in part. The algorithm should also be of use in analytical chemistry where enrichment is not possible. In effect, the useful limit of detection is significantly lowered. The algorithm uses new "phasing" and "feature stability upon accumulation" methods to reliably separate signal from noise at low signal-to-noise ratios where the Fourier spectrum requires many more transients to be definitive as to what is signal and what is noise. The long-standing problem of "false features" that plagued many prior attempts to employ nonlinear methods is thereby resolved for Lorentzian spectra. Examples are reported, and the limitations of the algorithm are discussed.

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“…Instead of truncating the SVD to rank K, a continuous regularization technique, called LPSVD(CR) 41 can be applied, an approach that leads to increased frequency resolution and the ability to automatically detect the number of significant peaks in the signal. 42,43 In EPLPSVD, 42,44 the Hankel structure of the H K matrix is restored by applying the Cadzow algorithm. In Chen et al 45 the principle of minimum variance is used to correct the K retained singular values prior to applying the Cadzow algorithm.…”

Section: Quantitation Algorithmsmentioning

confidence: 99%

MR spectroscopy quantitation: a review of time‐domain methods

et al. 2001

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In this article an overview of time-domain quantitation methods is given. Advantages of processing the data in the measurement domain are discussed. The basic underlying principles of the methods are outlined and from them the situations under which these algorithms perform well are derived. Also an overview of methods to preprocess the data is given. In that respect, signal-to-noise and/or resolution enhancement, the removal of unwanted components and corrections for model imperfections are discussed.

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The Use of Continuous Regularization in the Automated Analysis of MRS Time-Domain Data

Cited by 13 publications

References 7 publications

Saving Measurement Time in ¹³C NMR Spectroscopy

Saving Measurement Time in ¹³C NMR Spectroscopy

Increasing Sensitivity in Determining Chemical Shifts in One Dimensional Lorentzian NMR Spectra

MR spectroscopy quantitation: a review of time‐domain methods

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The Use of Continuous Regularization in the Automated Analysis of MRS Time-Domain Data

Cited by 13 publications

References 7 publications

Saving Measurement Time in 13C NMR Spectroscopy

Saving Measurement Time in 13C NMR Spectroscopy

Increasing Sensitivity in Determining Chemical Shifts in One Dimensional Lorentzian NMR Spectra

MR spectroscopy quantitation: a review of time‐domain methods

Contact Info

Product

Resources

About

Saving Measurement Time in ¹³C NMR Spectroscopy

Saving Measurement Time in ¹³C NMR Spectroscopy