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- W3088321382 abstract "In this paper, we assessed the reliability of glucose metabolic brain data for identifying lateralization of magnetic resonance imaging (MRI)-negative temporal lobe epilepsy (TLE) patients. We designed and developed an efficacious and automatic metabolic-wise lateralization framework. The proposed lateralization framework comprises three main systematic levels. In the first stage of our investigation, we pre-processed interictal fluorodeoxyglucose positron emission tomography images to extract glucose metabolic brain data. In the second stage, we used a voxel selection method involving a feature-ranking strategy to select the most discriminative metabolic voxels. Finally, we used a support vector machine followed by a 10-fold cross-validation strategy to assess the proposed lateralization framework in 27 patients with right MRI-negative TLE and 29 patients with left MRI-negative TLE. The proposed lateralization framework achieved an excellent accuracy of 96.43 % concordance with experienced PET interpreter. Thus, we show that pattern analysis of glucose metabolic brain data can accurately lateralize MRI-negative TLE patients in the clinical setting." @default.
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- W3088321382 date "2020-11-01" @default.
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- W3088321382 title "Pattern analysis of glucose metabolic brain data for lateralization of MRI-negative temporal lobe epilepsy" @default.
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- W3088321382 doi "https://doi.org/10.1016/j.eplepsyres.2020.106474" @default.
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