title: Analysis of Magnetic Resonance Spectroscopic signals with data-based autocorrelation wavelets.
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.
authors.affiliation: Electrical Eng. Dept. (DELET), Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Brazil. adalberto.schuck@uclouvain.be
authors.forename: A
authors.initials: A
authors.lastname: Schuck
authors.forename: C
authors.initials: C
authors.lastname: Lemke
authors.forename: A
authors.initials: A
authors.lastname: Suvichakorn
authors.forename: J-P
authors.initials: JP
authors.lastname: Antoine
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D009682:Magnetic Resonance Spectroscopy; D013223:Statistics as Topic; D058067:Wavelet Analysis