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Quantification of MRS data in the frequency domain using a wavelet filter, an approximated Voigt lineshape model and prior knowledge
KTH, School of Technology and Health (STH), Centres, Centre for Technology in Medicine and Health, CTMH.
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2006 (English)In: NMR in Biomedicine, ISSN 0952-3480, E-ISSN 1099-1492, Vol. 19, no 5, 617-626 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2006. Vol. 19, no 5, 617-626 p.
Keyword [en]
Baseline, Lineshape, MRS, Spectroscopy, Voigt, Wavelet, Baseline component, Voigt lineshape, Wavelet filter, Computer simulation, Curve fitting, Frequency domain analysis, Least squares approximations, Monte Carlo methods, Wavelet transforms, Magnetic resonance spectroscopy, article, controlled study, data analysis, diagnostic accuracy, filter, frequency analysis, human, human experiment, Monte Carlo method, normal human, nuclear magnetic resonance spectroscopy, priority journal, proton nuclear magnetic resonance, radiological parameters, reference value, reliability, signal noise ratio, statistical model, volunteer, Humans, Image Enhancement, Mathematics, Models, Theoretical, Signal Processing, Computer-Assisted
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:kth:diva-156000DOI: 10.1002/nbm.1060ISI: 000239936800013Scopus ID: 2-s2.0-33747363982OAI: oai:DiVA.org:kth-156000DiVA: diva2:765583
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QC 20141124

Available from: 2014-11-24 Created: 2014-11-17 Last updated: 2017-12-05Bibliographically approved

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