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A Priori Knowledge Based Frequency–Domain Quantification of Magnetic Resonance Spectroscopy
Modeling and Control in Biomedical Systems, Volume # 7 | Part# 1
Authors
Guo, Yu; Ruan, Su; Landré, Jérôme; Constans, Jean-Marc
Identifier
10.3182/20090812-3-DK-2006.00036
Index Terms
Biomedical signal processing; Quantification of physiological parametes for diagnosis assessment
Abstract
Because of the overlapping of the spectra of different metabolites and the interference of the baseline mainly from broad resonances of macromolecule and lipids, it is difficult to achieve the quantification of spectra of different metabolites which is important for both research and clinical applications of Magnetic Resonance Spectroscopy (MRS). In this paper, a novel MRS quantification method based on frequency a priori knowledge is proposed. Firstly, a wavelet filter is used to remove the broad components of an observed spectrum in which baseline and the relatively broad components of metabolite spectrum are included. Secondly, a linear nonnegative pursuit algorithm based on regularized FOCUSS (Focal Underdetermined System Solver) algorithm is used to decompose the filtered spectra in a dictionary which is based on a set of Lorentzian and Gaussian functions corresponding to spectrum models. Benefitting from the a priori knowledge of the peak frequency of each metabolite, the filtered metabolite spectrum can be sparsely represented with these basis functions and the spectra of different metabolites are relevant to certain basis functions. Therefore, with the corresponding relation between the basis functions and spectrum models and the estimated decomposition coefficients, a mixed spectrum without baseline can be reconstructed and spectra of different metabolites can be quantified at the same time. The accuracy and the robustness of MRS quantification are improved by the proposed method, from simulation data, compared with commonly used MRS quantification methods. Quantification on in vivo brain spectra is also demonstrated.
References
Bartha, R., Drost, D.J., and Williamson, P.C. (1999). Factors affecting the quantification of short echo in-vivo 1H MR spectra: prior knowledge, peak elimination, and filtering. NMR Biomedicine, 12(4), 205–216 Chen, S.S., Donoho, D.L., and Saunders, M.A. (2001) Atomic decomposition by basis pursuit. SIAM Revivew., 43(1), 129–159 Gillies, P., Marshall, I., Asplund, M., Winkler, P., and Higinbotham, J. (2006). Quantification of MRS data in the frequency domain using a wavelet filter, an approximated Voigt lineshape model and prior knowledge. NMR Biomedicine, 19, 617–626 Gorodnitsky, I.F., and Rao, B.D. (1997). Sparse signal reconstruction from limited data using FOCUSS: A reweighted norm minimization algorithm. IEEE Transactions. Signal Processing, 45, 600–616 In’t Zandt, H.J.A., Van der Graaf, M., and Heerschap, A. (2001). Common processing of in vivo MR spectra. NMR Biomedicine, 14, 224-232 Laudadio, T., Selén, Y., Vanhamme, L., Stoica, P., Van Hecke, P., and Van Huffela, S. (2004). Subspace-based MRS data quantitation of multiplets using prior knowledge. Journal of Magnetic Resonance, 168(1), 53–65 Mallat, S. and Zhang, Z. (1993). Matching pursuits with time-frequency dictionaries. IEEE Transactions Signal Processing, 41(12), 3397–3415 Mierisova, S. and Ala-korpela, M. (2001). MR spectroscopy quantitation: a review of frequency domain methods. NMR Biomedicine, 14, 247–259 Naressi, A., Couturier, C., Devos, J.M., Janssen, M., Mangeat, C., de Beer, R., Graveron-Demilly, D. (2001) Java-based graphical user interface for the MRUI quantitation package. Magnetic Resonance Materials in Physics, Biology and Medicine, 12, 141–152 Pati, Y.C., Rezaiifar, R., and Krishnaprasad, P.S. (1993). Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. Conference Record of the 27th Asilomar Conference on Signals, Systems & Computers, 1-5 Poullet, J., Sima, D.M., Simonetti, A.W., et al. (2007). An automated quantitation of short echo time MRS spectra in an open source software environment: AQSES. NMR Biomedicine, 20, 493–504 Rao, B.D., Engan, K., Cotter, S.F., and et al. (2003), Subset selection in noise based on diversity measure minimization. IEEE Transactions Signal Processing, 51(3), 760–770 Ratiney, H., Sdika, M., and Coenradie, Y. (2005). Timedomain semiparametric estimation based on a metabolite basis set. NMR Biomedicine, 18,1–13. Soher, B.J, Young, K. and Maudsley, A.A. (2001). Representation of strong baseline contributions in 1H MR spectra. Magnetic Resonance in Medicine, 45(6), 966-972 Vanhamme, L., van den Boogaart, A., and Van Huffel, S. (1997). Improved method for accurate and efficient quantification of MRS data with use of prior knowledge. Journal of Magnetic Resonance, 129, 35–43 Young, K., Govindaraju, V., Soher, B.J., and Maudsley, A.A. (1998). Automated spectral analysis II: application of wavelet shrinkage for characterization of nonparameterized signals. Magnetic Resonance in Medicine, 40, 816–821 11: pp. 251-279.