Pattern recognition of MRSI data shows regions of glioma growth that agree with DTI markers of brain tumor infiltration |
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| Authors: | Alan J. Wright G. Fellows T. J. Byrnes K. S. Opstad D. J. O. McIntyre J. R. Griffiths B. A. Bell C. A. Clark T. R. Barrick F. A. Howe |
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| Affiliation: | 1. Division of Basic Medical Sciences, St George's University of London, London, England;2. Academic Neurosurgery Unit, St George's University of London, London, England;3. National Hospital for Neurology and Neurosurgery, London, England;4. Division of Cardiac and Vascular Sciences, St George's University of London, London, England;5. Molecular Imaging (MRI and MRS), Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, England;6. Radiology and Physics Unit, Institute of Child Health, University College London, London, England |
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| Abstract: | Gliomas are the most common primary brain tumors and the majority are highly malignant, with one of the worst prognoses for patients. Gliomas are characterized by invasive growth into normal brain tissue that makes complete surgical resection and accurate radiotherapy planning extremely difficult. We have performed independent component analysis of magnetic resonance spectroscopy imaging data from human gliomas to segment brain tissue into tumor core, tumor infiltration, and normal brain, with confirmation by diffusion tensor imaging analysis. Our data are consistent with previous studies that compared anomalies in isotropic and anisotropic diffusion images to determine regions of potential glioma infiltration. We show that coefficients of independent components can be used to create colored images for easy visual identification of regions of infiltrative tumor growth. Magn Reson Med, 2009. © 2009 Wiley‐Liss, Inc. |
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| Keywords: | MRSI independent component analysis brain tumor DTI image segmentation visualization |
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