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Glioma is the most common and most difficult to treat brain cancer. Despite many efforts treatment, efficacy remains low. As neurosurgical removal is the standard procedure for glioma, a method, allowing for both early detection and exact determination of the location, size and extent of the tumor, could improve a patient's positive response to therapy. We propose application of susceptibility weighted molecular magnetic resonance imaging using, targeted contrast agents, based on superparamagnetic iron oxide nanoparticles, for imaging of the, glioma rim, namely brain-tumor interface. Iron oxide attached to the targeted cells increases, susceptibility differences at the boundary between tumor and normal tissue, providing the opportunity, to utilize susceptibility weighted imaging for improved tumor delineation. We investigated potential, enhancement of the tumor-brain contrast, including tumor core and rim when using susceptibility, weighted MRI for molecular imaging of glioma. There were significant differences in contrast-to-noise ratio before, 12 and 120min after contrast, agent injection between standard gradient echo pulse sequence and susceptibility weighted molecular, magnetic resonance imaging for the core-brain, tumor rim-core and tumor rim-brain areas. Currently, the most common MRI contrast agent used for glioma diagnosis is a non-specific, gadolinium-based agent providing T1-weighted enhancement. Susceptibility-weighted magnetic, resonance imaging is much less efficient when no targeted superparamagnetic contrast agents are, used. The improved determination of glioma extent provided by SWI offers an important new tool for, diagnosis and surgical planning. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.


Barbara Blasiak, James Landry, Randy Tyson, Jonathan Sharp, Umar Iqbal, Abedelnasser Abulrob, David Rushforth, John Matyas, Dragana Ponjevic, Garnette R Sutherland, Stefan Wolfsberger, Boguslaw Tomanek. Molecular susceptibility weighted imaging of the glioma rim in a mouse model. Journal of neuroscience methods. 2014 Apr 15;226:132-8

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PMID: 24525326

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