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Abstract #0500

Tissue type mapping of gliomas using multimodal MRI

Felix Raschke1, Thomas Richard Barrick2, Guang Yang3, Timothy Lloyd Jones4, Xujiong Ye5, and Franklyn Arron Howe2

1Faculty of Medicine and University Hospital Carl Gustav Carus, OncoRay – National Center for Radiation Research in Oncology, Dresden, Germany, 2Neurosciences Research Centre, St George's, University of London, London, United Kingdom, 3National Heart and Lung Institute, London, United Kingdom, 4Academic Neurosurgery Unit, St George's, University of London, London, United Kingdom, 5Laboratory of Vision Engineering, School of Computer Science, University of Lincoln

1H MRSI can assess glioma infiltration margins and malignant invasion but technical limitations prevent widespread use. In this study we used 2D 1H MRSI to determine voxels of specific tumour tissue type from which we extracted multimodal MRI (M-MRI) image characteristics. Subsequently, we applied superpixel segmentation and Bayesian statistical analysis to M-MRI alone to derive nosologic tumor images of these same tissue types with whole brain coverage. We obtained 100% classification accuracy for overall glioma grade, and an average 0.77 Dice overlap coefficient with the manual segmentation volume. Such methodology could aid prognostic assessment, surgical treatment and radiotherapy dose planning.

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