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Exploring the Intrinsic Structure of Magnetic Resonance Spectra Tumor Data Based on Independent Component Analysis and Correlation Analysis

  • Jian Ma
  • Zengqi Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)

Abstract

Analysis on magnetic resonance spectra (MRS) data gives a deep insight into pathology of many types of tumors. In this paper, a new method based on independent component analysis (ICA) and correlation analysis is proposed for MRS tumour data structure analysis. First, independent components and their coefficients are derived by ICA. Those components are interpreted in terms of metabolites, which interrelate with each other in tissues. Then correlation analysis is performed to reveal the interrelationship on coefficient of ICs, where residue dependence of components of metabolites remains. The method was performed on MRS data of hepatic encephalopathy. Experimental results reveal the intrinsic data structure and describe the pathological interrelation between parts of the structure successfully.

Keywords

Hepatic Encephalopathy Independent Component Analysis Independent Component Analysis Intrinsic Structure Mutual Independence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ladroue, C., et al.: Independent component analysis for automated decomposition of in vivo magnetic resonance spectra. Magnet. Reson. Med. 50, 697–703 (2003)CrossRefGoogle Scholar
  2. 2.
    Anthony, M.L., et al.: Classification of toxin-induced changes in 1H NMR spectra of urine using an artificial neural network. J. Pharmaceut Biomed. 13, 205–211 (1995)CrossRefGoogle Scholar
  3. 3.
    Simonetti, A.W., Melssen, W.J., de Edelenyi, F., van Asten, J.J., Heerschap, A., Buydens, L.M.: Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification. NMR Biomed. 18, 34–43 (2005)CrossRefGoogle Scholar
  4. 4.
    Devos, A., et al.: Classification of brain tumours using short echo time H-1 MR spectra. J. Magn. Reson. 170, 164–175 (2004)CrossRefGoogle Scholar
  5. 5.
    Mountford, C.E., Malycha, R.L.S.P., Gluch, L., Lean, C., Russell, P., Barraclough, B., Gillett, D., Himmelreich, U., Dolenko, B., Nikulin, A.E., Smith, I.C.P.: Diagnosis and prognosis of breast cancer by magnetic resonance spectroscopy of fine-needle aspirates analysed using a statistical classification strategy. Brit. J. Surg. 88, 1234–1240 (2002)CrossRefGoogle Scholar
  6. 6.
    Tate, A.R., et al.: Automated classification of short echo time in vivo 1H brain tumor spectra: A multicenter study. Magnet. Reson. Med. 49, 29–36 (2003)CrossRefGoogle Scholar
  7. 7.
    Howells, S.L., Maxwell, R.J., Griffiths, J.R.: Classification of tumour 1H NMR spectra by pattern recognition. NMR Biomed 5, 59–64 (1992)CrossRefGoogle Scholar
  8. 8.
    James, C.J., Hesse, C.W.: Independent component analysis for biomedical signals. Physiol Meas 26, R15 (2005)CrossRefGoogle Scholar
  9. 9.
    Hyvarinen, A.: Survey on independent component analysis. Neural Comput Surveys 2, 94–128 (1999)Google Scholar
  10. 10.
    Häussinger, D.: Hepatic encephalopathy: clinical aspects and pathogenesis. Deutsche medizinische Wochenschrift 129(Suppl. 2), 66–67 (2004)CrossRefGoogle Scholar
  11. 11.
    Kreis, R., et al.: Disorders of the brain in chronic hepatic encephalopathy detected with H-1 MR spectroscopy. Radiology 182, 19–27 (1992)Google Scholar
  12. 12.
    Kreis, R., Farrow, N., Ross, B.D.: Localized 1H NMR spectroscopy in patients with chronic hepatic encephalopathy. Analysis of changes in cerebral glutamine, choline and inositols. NMR Biomed 4, 109–116 (1991)CrossRefGoogle Scholar
  13. 13.
    Gavert, H., Hurri, J., Sarela, J., Hyvrinen, A.: Fast-ICA for matlab 5.x (2001), http://www.cis.hut.fi/projects/ica/fastica/
  14. 14.
    Kreis, R.: Quantitative localized 1H MR spectroscopy for clinical use. Progress in Nuclear Magnetic Resonance Spectroscopy 31, 155–195 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jian Ma
    • 1
  • Zengqi Sun
    • 1
  1. 1.Department of Computer ScienceTsinghua UniversityBeijingChina

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