• DocumentCode
    1478354
  • Title

    Strengths and Weaknesses of 1.5T and 3T MRS Data in Brain Glioma Classification

  • Author

    Kounelakis, M.G. ; Dimou, I.N. ; Zervakis, M.E. ; Tsougos, I. ; Tsolaki, E. ; Kousi, E. ; Kapsalaki, E. ; Theodorou, K.

  • Author_Institution
    Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece
  • Volume
    15
  • Issue
    4
  • fYear
    2011
  • fDate
    7/1/2011 12:00:00 AM
  • Firstpage
    647
  • Lastpage
    654
  • Abstract
    Although magnetic resonance spectroscopy (MRS) methods of 1.5Tesla (T) and 3T have been widely applied during the last decade for noninvasive diagnostic purposes, only a few studies have been reported on the value of the information extracted in brain cancer discrimination. The purpose of this study is threefold. First, to show that the diagnostic value of the information extracted from two different MRS scanners of 1.5T and 3T is significantly influenced in terms of brain gliomas discrimination. Second, to statistically evaluate the discriminative potential of publicly known metabolic ratio markers, obtained from these two types of scanners in classifying low-, intermediate-, and high-grade gliomas. Finally, to examine the diagnostic value of new metabolic ratios in the discrimination of complex glioma cases where the diagnosis is both challenging and critical. Our analysis has shown that although the information extracted from 3T MRS scanner is expected to provide better brain gliomas discrimination; some factors like the features selected, the pulse-sequence parameters, and the spectroscopic data acquisition methods can influence the discrimination efficiency. Finally, it is shown that apart from the bibliographical known, new metabolic ratio features such as N-acetyl aspartate/S, Choline/S, Creatine/S , and myo-Inositol/S play significant role in gliomas grade discrimination.
  • Keywords
    biomedical MRI; brain; cancer; medical image processing; Choline/S; Creatine/S; MRS data; N-acetyl aspartate/S; brain cancer discrimination; brain glioma classification; discrimination efficiency; discriminative potential; magnetic flux density 1.5 T; magnetic flux density 3 T; magnetic resonance spectroscopy; metabolic ratio; myo-Inositol/S; noninvasive diagnostics; pulse-sequence parameter; spectroscopic data acquisition; Bibliographies; Data acquisition; Data mining; Feature extraction; Magnetic resonance imaging; Support vector machines; Tumors; 1.5T and 3T magnetic resonance spectroscopy (MRS) scanners; Brain glioma discrimination; metabolic ratio markers; Brain Neoplasms; Glioma; Humans; Magnetic Resonance Spectroscopy; Reproducibility of Results;
  • fLanguage
    English
  • Journal_Title
    Information Technology in Biomedicine, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1089-7771
  • Type

    jour

  • DOI
    10.1109/TITB.2011.2131146
  • Filename
    5737788