Mixture model of NMR - its application to diagnosis and treatment of brain cancer

• Autorzy: Binczyk F. , Tarnawski R. , Polańska J.
• Języki publikacji: EN

Abstrakty

EN

Nuclear Magnetic Resonance (NMR) is widely used technique in cancer diagnosis and treatment planning. It is employed to search for the high concentration regions of particular metabolites, which are directly related to the concentration of cancer cells. NMR signal maybe be characterized by a set of peaks which are representation of every distinct metabolite. Area under peak must be calculated in order to obtain proper information about metabolite amount. Commercially available software allows for the analysis of one-peak-in-time only. The proposed technique, based on Gaussian Mixture Model (GMM), allows for modeling all-peaks-in-time, and corrects after the neighboring peaks giving more accurate estimates of metabolite concentration. The resulting software processes NMR signal from the very beginning up to the final result, which is given in a form of so called metabolite map.

Słowa kluczowe

EN

GMM; EM algorithm; BIC; NMR; Savitzky-Golay filter

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2010
• Tom: Vol. 20, no. 4
• Strony: 457--472
• Opis fizyczny: Bibliogr. 16 poz., rys., wzory

Twórcy

autor

Binczyk F.

autor

Tarnawski R.

autor

Polańska J.

• Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.,

Bibliografia

• [1] R. A. DE GRAFF: In vivo NMR spectroscopy. John Wiley and Sons Ltd., 2007.
• [2] J. F. JANSEN, H. W. BACKES, N. KLAAS and M. E. KOOI: 1HMR spectroscopy of the brain: Absolute quantification of metabolites. Radiology, 240 (2007), 318-333.
• [3] J. KEELER: Understanding NMR spectroscopy. John Wiley and Sons Ltd., 2005.
• [4] A. SAVITZKY and M.J. E. GOLAY: Smoothing and differentiation of data by Ssimplified least squares procedures. Analytical Chemistry, 36(8), (1964), 1627-1639.
• [5] A. POLANSKI and M. KIMMEL: Bioinformatics. Springer Verlag Berlin Heidelberg, 2007.
• [6] P. E. MILLAR: Using the Bayesian information criterion to judge models and statistical significance. North American Stata Users’ Group Meetings, 2006.
• [7] S. MC KINLEY and M. LEVINE: Cubic spline interpolation. Math 45: Linear Algebra, 1992.
• [8] M. PIETROWSKA, L. MARCZAK, J. POLANSKA, E. NOWICKA, K. BEHRENT, R. TARNAWSKI, M. STOBIECKI, A. POLANSKI and P. WIDLAK: Optimizing of MALDI-ToF-based low-molecular-weight serum proteome pattern analysis in detection of breast cancer patients; the effect of albumin removal on classification performance. Neoplasma, 56(6), (2010), 537-44.
• [9] webpage: http://tarquin.sourceforge.net/index.php, Tarquin project, acces 6.12.2010.
• [10] J. GONG and J.P. HORNAK: A fast T1 algorithm. Magn. Reson. Imaging, 10 (1992), 623-626.
• [11] D. SHAW: Fourier transform NMR spectroscopy. Elsevier, NY, 1976.
• [12] J-B. POULLET: An automated quantitation of short echo time MRS spectra in an open source software environment: AQSES NMR in Biomedicine, 20(5), (2007), 493-504.
• [13] D. STEFAN, F. DI CESARE, A. ANDRASESCU, E. POPA, A. LAZARIEV, E. VESCOVO, O. STRBAK, S. WILLIAMS, Z. STARCUK, M. CABANAS, D. VAN ORMONDT and D. GRAVERON-DEMILLY: Quantitation of magnetic resonance spectroscopy signals: the jMRUI software package. Measurement Science and Technology, 20(104035), (2009).
• [14] A. P. DEMPSTER, M.N. LAIRD and D.B. RUBIN: Maximum likelihood from incomplete data wia the EM algorithm. Journal of the Royal Statistical Society B, 39(1), (1977), 1-22.
• [15] G. MCLACHLAN (ED.): Mixture models. Marcel Dekker, NY, 1988.
• [16] E. CABANES, S. CONFORT-GOUNY, Y. LE FUR, G. SIMOND and P.J. COZZONE: Optimization of residual water signal removal by HLSVD on simulated short echo time proton MR spectra of the human brain. J.of Magnetic Resonance, 150(2), (2001).