Improved Baseline Recognition and Modeling of FT NMR Spectra

Golotvin, S. S.; Williams, Antony J.

doi:10.1006/jmre.2000.2121

Cited by 80 publications

(63 citation statements)

References 8 publications

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“…Baseline correction is done by constructing a baseline model and then subtracting the baseline model from the original spectrum. 21 The spectral artefacts, phaseand peak shifts and lineshape distortion, are corrected by a combination method of FIDDLE 22 and a method proposed by Witjes. 23 Careful pre-processing can reduce the influence of the artefacts on data analysis.…”

Section: Introductionmentioning

confidence: 99%

Real-life applications of the MULVADO software package for processing DOSY NMR data

Huo

Geurts²,

Brands³

et al. 2006

Magn. Reson. Chem.

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MULVADO is a newly developed software package for DOSY NMR data processing, based on multivariate curve resolution (MCR), one of the principal multivariate methods for processing DOSY data. This paper will evaluate this software package by using real-life data of materials used in the printing industry: two data sets from the same ink sample but of different quality. Also a sample of an organic photoconductor and a toner sample are analysed. Compared with the routine DOSY output from monoexponential fitting, one of the single channel algorithms in the commercial Bruker software, MULVADO provides several advantages. The key advantage of MCR is that it overcomes the fluctuation problem (non-consistent diffusion coefficient of the same component). The combination of non-linear regression (NLR) and MCR can yield more accurate resolution of a complex mixture. In addition, the data pre-processing techniques in MULVADO minimise the negative effects of experimental artefacts on the results of the data. In this paper, the challenges for analysing polymer samples and other more complex samples will also be discussed.

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Section: Introductionmentioning

confidence: 99%

Real-life applications of the MULVADO software package for processing DOSY NMR data

Huo

Geurts²,

Brands³

et al. 2006

Magn. Reson. Chem.

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show abstract

“…[22]. In our studies the sharp spectral peaks were identified and separated from baseline using signal processing method [23] as opposed to the simulated basis spectrum. In our procedure even unknown sharp peaks present in the spectrum would not be included in the baseline.…”

Section: Discussionmentioning

confidence: 99%

“…For better estimation of the baseline, any remaining narrow metabolite peaks were separated from the baseline in the baseline recognition step. This was achieved by utilizing the signal power criterion [23]. This involved computation ofσ sig , which is the mean of the variances of the signal in the NAA, Cr and Cho peaks.…”

Section: Data Processingmentioning

confidence: 99%

Fast quantification of proton magnetic resonance spectroscopic imaging with artificial neural networks

Bhat

Sajja

Narayana

2006

Journal of Magnetic Resonance

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Accurate quantification of the MRSI-observed regional distribution of metabolites involves relatively long processing times. This is particularly true in dealing with large amount of data that is typically acquired in multi-center clinical studies. To significantly shorten the processing time, an artificial neural network (ANN) based approach was explored for quantifying the phase corrected (as opposed to magnitude) spectra. Specifically, in these studies radial basis function neural network (RBFNN) was used. This method was tested on simulated and normal human brain data acquired at 3T. The N-acetyl aspartate (NAA)/ creatine (Cr), choline (Cho)/Cr, Glutamate + Glutamine (Glx)/Cr, and Myo-inositol (mI)/Cr ratios in normal subjects were compared with the line fitting (LF) technique and jMRUI-AMARES analysis, and published values. The average NAA/Cr, Cho/Cr, Glx/Cr and mI/Cr ratios in normal controls were found to be 1.58 ± 0.13, 0.9 ± 0.08, 0.7 ± 0.17 and 0.42 ± 0.07 respectively. The corresponding ratios using the LF and jMRUI-AMARES methods were 1.6 ± 0.11, 0.95 ± 0.08, 0.78 ± 0.18, 0.49 ± 0.1 and 1.61 ± 0.15, 0.78 ± 0.07, 0.61 ± 0.18, 0.42 ± 0.13 respectively. These results agree with those published in literature. Bland-Altman analysis indicated an excellent agreement and minimal bias between the results obtained with RBFNN and other methods. The computational time for the current method was 15 seconds compared to approximately 10 minutes for the LF-based analysis.

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“…2 is illustrated with complex white Gaussian noise of standard deviation (SD) σ = 25 added, and a 10-Hz exponential filter applied. The sampling frequency, f s is 3 kHz, and the fine peaks in the ATP multiplets are split by 16 Hz. The technique was tested on 100 different simulated signal-plus-noise realizations, for each of 20 SNR levels in the range 5 ≤ σ ≤ 100 with a step of 5, for a total of 2000 runs.…”

Section: Fitting Simulated Mrs Signalsmentioning

confidence: 99%

“…While new solutions are being investigated [16][17][18], phase and baseline corrections are difficult to automate and usually cannot match the performance of an experienced manual operator when artefacts are large. This is evidenced, for example, by a 50% increase in the standard deviation of normal human cardiac phosphocreatine (PCr) to adenosine triphosphate (ATP) ratios quantified in phosphorus ( 31 P) spectra by automated Marquardt frequency-domain fitting and by non-linear least squares fitting in the time-domain [19], as compared to manual frequency-domain fitting of data acquired with the identical protocol and equipment [20].…”

Section: Introductionmentioning

confidence: 99%

Quantifying in vivo MR spectra with circles

Gabr

Ouwerkerk

Bottomley

2006

Journal of Magnetic Resonance

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Accurate and robust quantification of in vivo magnetic resonance spectroscopy (MRS) data is essential to its application in research and medicine. The performance of existing analysis methods is problematic for in vivo studies where low signal-to-noise ratio, overlapping peaks and intense artefacts are endemic. Here, a new frequency-domain technique for MRS data analysis is introduced wherein the circular trajectories which result when spectral peaks are projected onto the complex plane, are fitted with active circle models. The use of active contour strategies naturally allows incorporation of prior knowledge as constraint energy terms. The problem of phasing spectra is eliminated, and baseline artefacts are dealt with using active contours-snakes. The stability and accuracy of the new technique, CFIT, is compared with a standard time-domain fitting tool, using simulated 31 P data with varying amounts of noise and 98 real human chest and heart 31 P MRS data sets. The real data were also analyzed by our standard frequency-domain absorption-mode technique. On the real data, CFIT demonstrated the least fitting failures of all methods and an accuracy similar to the latter method, with both these techniques outperforming the time-domain approach. Contrasting results from simulations argue that performance relative to Cramer-Rao Bounds may not be a suitable indicator of fitting performance with typical in vivo data such as these. We conclude that CFIT is a stable, accurate alternative to the best existing methods of fitting in vivo data.

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Improved Baseline Recognition and Modeling of FT NMR Spectra

Cited by 80 publications

References 8 publications

Real-life applications of the MULVADO software package for processing DOSY NMR data

Real-life applications of the MULVADO software package for processing DOSY NMR data

Fast quantification of proton magnetic resonance spectroscopic imaging with artificial neural networks

Quantifying in vivo MR spectra with circles

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