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

• Published in: IEEE transactions on information technology in biomedicine 2011-07, Vol.15 (4), p.647-654
• Main Authors: Kounelakis, M. M., Dimou, I. N., Zervakis, M. M., Tsougos, I. I., Tsolaki, E. E., Kousi, E., Kapsalaki, E. E., Theodorou, K. K.
• Format: Article
• Language: English
• Subjects: 1.5T and 3T magnetic resonance spectroscopy (MRS) scanners; Bibliographies; Brain glioma discrimination; Brain Neoplasms - classification; Brain Neoplasms - diagnosis; Brain Neoplasms - pathology; Data acquisition; Data mining; Feature extraction; Glioma - classification; Glioma - diagnosis; Glioma - pathology; Humans; Magnetic resonance imaging; Magnetic Resonance Spectroscopy - instrumentation; Magnetic Resonance Spectroscopy - methods; metabolic ratio markers; Reproducibility of Results; Support vector machines; Tumors
• ISSN: 1089-7771; 1558-0032
• DOI: 10.1109/TITB.2011.2131146

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.