Toward a quantitative analysis of in vivo proton magnetic resonance spectroscopic signals using the continuous Morlet wavelet transform (original) (raw)
Related papers
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2008
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.
IFMBE Proceedings, 2009
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.
IEEE International Workshop on Imaging Systems and Techniques, 2008
We apply theMorlet wavelet transform to characterizing Magnetic Resonance Spectroscopy (MRS) signals acquired at short echo-time. These signals usually contain contributions from metabolites, water and a baseline which mainly originates from large molecules, known as macromolecules, and lipids. The baseline accommodation is one of the major obstructions in in vivo short echo-time MRS quantification as its shape and intensity are
Measurement Science and Technology, 2011
We introduce a new class of wavelets, called metabolite-based autocorrelation wavelets, for the analysis of magnetic resonance spectroscopic MRS) signals by means of the continuous wavelet transform (CWT). Each MRS signal consists of a number of frequency components typical for the active nuclei and the chemical environment around them in a particular voxel. Identifying individual metabolite components is crucial for the evolving field of MRS for clinical applications. In a first step, we develop the theoretical analysis, considering continuous wavelets derived from (Lorentzian lineshape) signal models. With this analytical approach, we can not only tailor individual wavelets but also determine signal parameters such as the damping factor of the Lorentzian lineshape. Then, we design more complex wavelets numerically from discrete metabolite profiles. As the resulting wavelets are discrete, too, they require an extra step of up-and downsampling in order to perform a proper CWT. The outcome is that the present analysis indicates without ambiguity the presence of a given metabolite in a MRS signal.
Metabolite-based wavelets for analyzing magnetic resonance spectroscopic signals
2010 IEEE International Conference on Imaging Systems and Techniques, 2010
We analyze Magnetic Resonance Spectroscopic signals by the continuous wavelet transform. Instead of the standard (Morlet) wavelet, we introduce a new class of wavelets, derived from the metabolite data themselves, using the properly normalized autocorrelation function of each signal. This allows to detect without ambiguity the presence of a given metabolite in a signal consisting of many different components.
Toward quantitative short-echo-time in vivo proton MR spectroscopy without water suppression
Magnetic Resonance in Medicine, 2006
A methodological development for quantitative short-echotime (TE) in vivo proton MR spectroscopy (MRS) without water suppression (WS) is described that integrates experimental and software approaches. Experimental approaches were used to eliminate frequency modulation sidebands and first-order phase errors. The dominant water signal was modeled and extracted by the matrix pencil method (MPM) and was used as an internal reference for absolute metabolite quantification. Spectral fitting was performed by combining the baseline characterization by a wavelet transform (WT)-based technique and time-domain (TD) parametric spectral analysis using full prior knowledge of the metabolite model spectra. The model spectra were obtained by spectral simulation instead of in vitro measurements. The performance of the methodology was evaluated by Monte Carlo (MC) studies, phantom measurements, and in vivo measurements on rat brains. More than 10 metabolites were quantified from spectra measured at TE ؍ 20 ms on a 4.7 T system.
Analyzing NMR spectra with the Morlet wavelet
We study the time-scale representation provided by the Morlet wavelet transform for characterizing NMR signals. From an analytical analysis and simulations, we conclude that the wavelet shows a satisfactory performance even when a baseline, an additive Gaussian noise or a solvent are present in the signals. It can also cope with non-Lorentzian lineshapes which commonly occur because of the inhomogeneous distribution of molecules in a substance. These results mean that the Morlet wavelet transform is a potential tool to quantify in vivo NMR signals.
Magnetic Resonance in Medicine, 1998
An automated method for analysis of in vivo proton magnetic resonance (MR) spectra and reconstruction of metabolite distributions from MR spectroscopic imaging (MRSI) data is described. A parametric spectral model using acquisition specific, a priori information is combined with a wavelet-based, nonparametric characterization of baseline signals. For image reconstruction, the initial fit estimates were additionally modified according to a priori spatial constraints. The automated fitting procedure was applied to four different examples of MRS data obtained at 1.5 T and 4.1 T. For analysis of major metabolites at medium TE values, the method was shown to perform reliably even in the presence of large baseline signals and relatively poor signal-to-noise ratios typical of in vivo proton MRSI. identification of additional metabolites was also demonstrated for short TE data. Automated formation of metabolite images will greatly facilitate and expand the clinical applications of MR spectroscopic imaging.
Journal of Magnetic Resonance, 1997
Wavelet Transform (WT) was used to quantify the Magnetic Resonance Spectroscopy (MRS) parameters, chemical shift, apparent relaxation time T , resonance amplitude and phase. Wavelet Transform is a time-frequency representation which separates each component from the FID, which is then successively quantified and substracted from the raw signal. Two iterative procedures were developed. They were combined with a nonlinear regression analysis method and tested on both simulated and real sets of biomedical MRS data selected with respect to the main problems usually encountered in quantifying biomedical MRS, specifically "chemical noise" resulting from overlapping resonances and baseline distorsion. The results indicate that the Wavelet Transform method can provide efficient and accurate quantification of MRS data.