title: Quantification method using the Morlet wavelet for magnetic resonance spectroscopic signals with macromolecular contamination.
abstract: We study the Morlet wavelet transform on characterizing Magnetic Resonance Spectroscopic (MRS) signals acquired at short echo-time. These signals contain contributions from metabolites, water and a baseline which mainly originates from large molecules, known as macromolecules, and lipids. The baseline signal decays faster than the metabolite ones. Therefore, by making use of the time-scale representation of the wavelet, the two signals can be distinguished without any additional pre-processing. This is confirmed by the experimental results which show that the Morlet wavelet can correctly quantify the metabolite contributions even when a baseline is embedded in the MRS signals.
authors.affiliation: Institut de physique théorique (FYMA), Université catholique de Louvain, Louvain-la-Neuve, Belgium.
authors.forename: Aimamorn
authors.initials: A
authors.lastname: Suvichakorn
authors.forename: Hélène
authors.initials: H
authors.lastname: Ratiney
authors.forename: Adriana
authors.initials: A
authors.lastname: Bucur
authors.forename: Sophie
authors.initials: S
authors.lastname: Cavassila
authors.forename: Jean-Pierre
authors.initials: JP
authors.lastname: Antoine
mesh_terms: D000465:Algorithms; D003401:Creatine / Q000737:chemistry; D005583:Fourier Analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008055:Lipids / Q000737:chemistry; D046911:Macromolecular Substances; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D015233:Models, Statistical; D019047:Phantoms, Imaging; D012044:Regression Analysis; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted
chemical_list: D008055:Lipids; D046911:Macromolecular Substances; D003401:Creatine