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Advanced MR techniques for preoperative glioma characterization: part 2

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  • معلومة اضافية
    • بيانات النشر:
      Wiley
    • الموضوع:
      2023
    • Collection:
      Imperial College London: Spiral
    • الموضوع:
    • نبذة مختصرة :
      Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF). EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.
    • ISSN:
      1053-1807
    • Relation:
      Journal of Magnetic Resonance Imaging; http://hdl.handle.net/10044/1/103155
    • الرقم المعرف:
      10.1002/jmri.28663
    • الدخول الالكتروني :
      http://hdl.handle.net/10044/1/103155
      https://doi.org/10.1002/jmri.28663
    • Rights:
      © 2023 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. ; https://creativecommons.org/licenses/by/4.0/
    • الرقم المعرف:
      edsbas.98928DD8