Quantification of MRS data in the frequency domain using a wavelet filter, an approximated Voigt lineshape model and prior knowledge

P. Gillies, Ian Marshall, M. Asplund, P. Winkler, J. Higinbotham

Research output: Contribution to journalArticle

Abstract

Quantification of MRS spectra is a challenging problem when a large baseline is present along with a low signal to noise ratio. This work investigates a robust fitting technique that yields accurate peak areas under these conditions. Using simulated long echo time 1H MRS spectra with low signal to noise ratio and a large baseline component, both the accuracy and reliability of the fit in the frequency domain were greatly improved by reducing the number of fitted parameters and making full use of all the known information concerning the Voigt lineshape. Using an appropriate first order approximation to a popular approximation of the Voigt lineshape, a significant improvement in the estimate of the area of a known spectral peak was obtained with a corresponding reduction in the residual. Furthermore, this improved parameter choice resulted in a large reduction in the number of iterations of the least-squares fitting routine. On the other hand, making use of the known centre frequency differences of the component resonances gave negligible improvement. A wavelet filter was used to remove the baseline component. In addition to performing a Monte Carlo study, these fitting techniques were also applied to a set of 10 spectra acquired from healthy human volunteers.Again, the same reduced parameter model gave the lowest value for X2 in each case. Copyright © 2006 John Wiley & Sons, Ltd.

Original languageEnglish
Pages (from-to)617-626
Number of pages10
JournalNMR in Biomedicine
Volume19
Issue number5
DOIs
Publication statusPublished - Aug 2006

Fingerprint

wavelet
filter
signal-to-noise ratio
parameter

Keywords

  • Baseline
  • Lineshape
  • MRS
  • Spectroscopy
  • Voigt
  • Wavelet

Cite this

Gillies, P. ; Marshall, Ian ; Asplund, M. ; Winkler, P. ; Higinbotham, J. / Quantification of MRS data in the frequency domain using a wavelet filter, an approximated Voigt lineshape model and prior knowledge. In: NMR in Biomedicine. 2006 ; Vol. 19, No. 5. pp. 617-626.
@article{709a5b621a4048048af2769846f6123e,
title = "Quantification of MRS data in the frequency domain using a wavelet filter, an approximated Voigt lineshape model and prior knowledge",
abstract = "Quantification of MRS spectra is a challenging problem when a large baseline is present along with a low signal to noise ratio. This work investigates a robust fitting technique that yields accurate peak areas under these conditions. Using simulated long echo time 1H MRS spectra with low signal to noise ratio and a large baseline component, both the accuracy and reliability of the fit in the frequency domain were greatly improved by reducing the number of fitted parameters and making full use of all the known information concerning the Voigt lineshape. Using an appropriate first order approximation to a popular approximation of the Voigt lineshape, a significant improvement in the estimate of the area of a known spectral peak was obtained with a corresponding reduction in the residual. Furthermore, this improved parameter choice resulted in a large reduction in the number of iterations of the least-squares fitting routine. On the other hand, making use of the known centre frequency differences of the component resonances gave negligible improvement. A wavelet filter was used to remove the baseline component. In addition to performing a Monte Carlo study, these fitting techniques were also applied to a set of 10 spectra acquired from healthy human volunteers.Again, the same reduced parameter model gave the lowest value for X2 in each case. Copyright {\circledC} 2006 John Wiley & Sons, Ltd.",
keywords = "Baseline, Lineshape, MRS, Spectroscopy, Voigt, Wavelet",
author = "P. Gillies and Ian Marshall and M. Asplund and P. Winkler and J. Higinbotham",
year = "2006",
month = "8",
doi = "10.1002/nbm.1060",
language = "English",
volume = "19",
pages = "617--626",
journal = "NMR in Biomedicine",
issn = "0952-3480",
publisher = "John Wiley and Sons Ltd",
number = "5",

}

Quantification of MRS data in the frequency domain using a wavelet filter, an approximated Voigt lineshape model and prior knowledge. / Gillies, P.; Marshall, Ian; Asplund, M.; Winkler, P.; Higinbotham, J.

In: NMR in Biomedicine, Vol. 19, No. 5, 08.2006, p. 617-626.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Quantification of MRS data in the frequency domain using a wavelet filter, an approximated Voigt lineshape model and prior knowledge

AU - Gillies, P.

AU - Marshall, Ian

AU - Asplund, M.

AU - Winkler, P.

AU - Higinbotham, J.

PY - 2006/8

Y1 - 2006/8

N2 - Quantification of MRS spectra is a challenging problem when a large baseline is present along with a low signal to noise ratio. This work investigates a robust fitting technique that yields accurate peak areas under these conditions. Using simulated long echo time 1H MRS spectra with low signal to noise ratio and a large baseline component, both the accuracy and reliability of the fit in the frequency domain were greatly improved by reducing the number of fitted parameters and making full use of all the known information concerning the Voigt lineshape. Using an appropriate first order approximation to a popular approximation of the Voigt lineshape, a significant improvement in the estimate of the area of a known spectral peak was obtained with a corresponding reduction in the residual. Furthermore, this improved parameter choice resulted in a large reduction in the number of iterations of the least-squares fitting routine. On the other hand, making use of the known centre frequency differences of the component resonances gave negligible improvement. A wavelet filter was used to remove the baseline component. In addition to performing a Monte Carlo study, these fitting techniques were also applied to a set of 10 spectra acquired from healthy human volunteers.Again, the same reduced parameter model gave the lowest value for X2 in each case. Copyright © 2006 John Wiley & Sons, Ltd.

AB - Quantification of MRS spectra is a challenging problem when a large baseline is present along with a low signal to noise ratio. This work investigates a robust fitting technique that yields accurate peak areas under these conditions. Using simulated long echo time 1H MRS spectra with low signal to noise ratio and a large baseline component, both the accuracy and reliability of the fit in the frequency domain were greatly improved by reducing the number of fitted parameters and making full use of all the known information concerning the Voigt lineshape. Using an appropriate first order approximation to a popular approximation of the Voigt lineshape, a significant improvement in the estimate of the area of a known spectral peak was obtained with a corresponding reduction in the residual. Furthermore, this improved parameter choice resulted in a large reduction in the number of iterations of the least-squares fitting routine. On the other hand, making use of the known centre frequency differences of the component resonances gave negligible improvement. A wavelet filter was used to remove the baseline component. In addition to performing a Monte Carlo study, these fitting techniques were also applied to a set of 10 spectra acquired from healthy human volunteers.Again, the same reduced parameter model gave the lowest value for X2 in each case. Copyright © 2006 John Wiley & Sons, Ltd.

KW - Baseline

KW - Lineshape

KW - MRS

KW - Spectroscopy

KW - Voigt

KW - Wavelet

U2 - 10.1002/nbm.1060

DO - 10.1002/nbm.1060

M3 - Article

VL - 19

SP - 617

EP - 626

JO - NMR in Biomedicine

JF - NMR in Biomedicine

SN - 0952-3480

IS - 5

ER -