Marie-Curie Fellow
Research & Training Network
A. Suvichakorn (ER)

Marie Curie Fellow (ER) of the FAST project
Université Catholique de Louvain, Belgium
Contact
 

• T4 Signal processing
  Task 4 focuses on MRSI signal processing proper of Gigabytes of data. The ultimate goal is reliable quantitation of metabolites. The required computing times are long and will eventually need grid-computing.

  • T4.1 led by KU Leuven, concerns time-domain processing. This is a daunting task for many reasons:
    1. MRSI signals contain a strong non-parametric part from `macromolecules' that perturbs the wanted, parametric, part from metabolites. This requires a semi-parametric approach which is still under development, worldwide.
    2. Many spectral peaks of the parametric part overlap strongly.
    3. All processing needs to be fully automatic and numerically stable. Reliance on starting values will be reduced by development of genetic algorithms.
    4. Reliable error bars on metabolite concentrations that accommodate the non-parametric part must be provided.

Since the Morlet wavelet transform can give rich information in both time and frequency domains simultaneously, it provides another viewpoint of the MRS signal. With the collaboration with our research partners (especially Université Lyon 1 and Ecole Polytechnique Fédérale de Lausanne who kindly provide us the MRS signals), Dr. Suvichakorn developed a method to derive the lineshape parameters and the amplitude of the MRS signal with overlapping frequencies.

• A. Suvichakorn, H. Ratiney, S. Cavassila, and J-P. Antoine,  Wavelet-based techniques in MRS, to appear as a chapter in Recent Advances in Signal Processing, A. Lazinica (ed.),  IN-TECH, Vienna, Austria, and Rijeka, Croatia,  2010 (in press) ISBN 978-953-307-002-5 (30 pages).


• A. Suvichakorn, H. Ratiney, A. Bucur, S. Cavassila, and J-P.  Antoine, Toward a quantitative analysis of in vivo magnetic resonance proton spectroscopic signals using the continuous Morlet wavelet transform, Meas. Sci. Technol.   20, 2009, paper #104029 (11 pages).


• D.M. Sima, M.I. Osorio Garcia, J-B. Poullet, A. Suvichakorn,  J-P. Antoine, S.Van Huffel, and D. van Ormondt,
  Lineshape estimation for magnetic resonance spectroscopy MRS signals: self-deconvolution revisited, Meas. Sci. Technol.   20, 2009, paper #104031 (12 pages).


• A. Suvichakorn, H. Ratiney, A. Bucur, S. Cavassila and J. -P. Antoine, Analyzing Magnetic Resonance Spectroscopic Signals with Macromolecular Contamination by the Morlet Wavelet, ECIFMBE Proceedings on the 4th European Conference of the International Federation for Medical and Biological Engineering, pp. 163–166,  23–27 November 2008 Antwerp, Belgium.


• A. Suvichakorn, H. Ratiney, A. Bucur, S. Cavassila, and J.P. Antoine. Morlet Wavelet Analysis of Magnetic Resonance Spectroscopic Signals with Macromolecular Contamination. In IEEE International Workshop on Imaging Systems and Techniques,  pages 321-325, 10-12 September 2008, Chania, Greece.


• A. Suvichakorn and J-P. Antoine, Analyzing NMR Spectra with the Morlet wavelet. Proceedings on the 16th European Signal Processing Conference (EUSIPCO-2008), 25-29 August 2008, Lausanne.


• A. Suvichakorn, H. Ratiney, A. Bucur, S. Cavassila and J-P. Antoine, Quantification method using the Morlet wavelet for Magnetic Resonance Spectroscopic signals with macromolecular contamination. Proceedings on the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2008), pp. 2681-2684, 20-25 August 2008, Vancouver.