Analysis of Magnetic Resonance Spectroscopic signals with data-based autocorrelation wavelets

Author

Schuck, A. ; Lemke, C. ; Suvichakorn, A. ; Antoine, J.-P.

Author_Institution

Electr. Eng. Dept. (DELET), Fed. Univ. of Rio Grande do Sul (UFRGS), Porto Alegre, Brazil

fYear

2010

fDate

Aug. 31 2010-Sept. 4 2010

Firstpage

855

Lastpage

858

Abstract

A new class of wavelet functions called data-based autocorrelation wavelets is developed for analyzing Magnetic Resonance Spectroscopic (MRS) signals by means of the continuous wavelet transform (CWT), instead of the traditional wavelet like Morlet wavelet. These new wavelets are derived from the normalized autocorrelation function from metabolite data and then used for detecting the presence of a given metabolite in a signal with a presence of many different components and finally for quantifying some of its parameters.

Keywords

biomagnetism; magnetic resonance spectroscopy; medical signal processing; molecular biophysics; wavelet transforms; continuous wavelet transform; data-based autocorrelation wavelet; magnetic resonance spectroscopic signal analysis; metabolite data; normalized autocorrelation function; Continuous wavelet transforms; Correlation; Damping; Wavelet analysis; Wavelet domain; Algorithms; Data Interpretation, Statistical; Magnetic Resonance Spectroscopy; Statistics as Topic; Wavelet Analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE

Conference_Location

Buenos Aires

ISSN

1557-170X

Print_ISBN

978-1-4244-4123-5

Type

conf

DOI

10.1109/IEMBS.2010.5628034

Filename

5628034