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- Title
Quantification of MRS data in the frequency domain using a wavelet filter, an approximated Voigt lineshape model and prior knowledge.
- Authors
Gillies, P.; Marshall, I.; Asplund, M.; Winkler, P.; Higinbotham, J.
- 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 χ2 in each case. Copyright © 2006 John Wiley & Sons, Ltd.
- Publication
NMR in Biomedicine, 2006, Vol 19, Issue 5, p617
- ISSN
0952-3480
- Publication type
Article
- DOI
10.1002/nbm.1060