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Analyzing Magnetic Resonance Spectroscopic Signals With Macromolecular Contamination By The Morlet Wavelet

4TH EUROPEAN CONFERENCE OF THE INTERNATIONAL FEDERATION FOR MEDICAL AND BIOLOGICAL ENGINEERING(2009)

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Abstract
We study the Morlet wavelet transform on characterizing Magnetic Resonance Spectroscopy (MRS) signals acquired at short echo-time. These MRS signals usually contain contributions from metabolites, water and a baseline which mainly originates from large molecules, known as macromolecules, and lipids. As its shape and intensity are not known a priori, the baseline accommodation becomes one of the major obstructions in in vivo short echo-time MRS quantification. We acquired an in vivo macromolecule MRS signal on a horizontal 4.7T Biospec system by optimizing the inversion time, which represents the delay between the inversion pulse and the first pulse of the PRESS sequence. As a consequence, the metabolites are nullified while the others are maintained. The metabolite-nullified signal from a volume-of-interest centralized in the hippocampus of a healthy mouse was a combination of residual water, baseline and noise. Compared to the simulated signal of creatine, the signal decays much faster. The time-scale representation of the wavelet can therefore distinguish the two signals without any additional pre-processing. The amplitude of the metabolite is also correctly derived although at earlier time it still has an effect of the baseline. In addition, we also show that the Morlet wavelet can be used to characterize different lineshapes, e.g. Lorentzian, Gaussian or Voigt, which are generally used to model the MRS signals. That is, the first derivative of the modulus of the wavelet transform relates to the damping effect of the Lorentzian lineshape while its second derivative indicates the second-order broadening of the Gaussian and Voigt. The performance of the wavelet when applied to an in vitro creatine is also presented.
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Key words
Macromolecule,short echo-time MRS,non-lorentzian lineshape,Morlet wavelet transform
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