Advertisement

Advanced MRI Techniques in Brain Tumors

  • Stefanos B. Lachanis
  • Ioannis E. Papachristos
Chapter

Abstract

MRI plays a significant role and is the cornerstone in imaging brain tumors but has certain limitations. Advanced imaging techniques mainly perfusion imaging, MR spectroscopy, diffusion imaging, diffusion tensor imaging, and fMRI provide complementary functional, hemodynamic, metabolic, cellular, and cytoarchitectural information transforming MRI into a comprehensive tool that combines anatomy and morphology with physiology and function. These techniques are currently in clinical use and are the subject of intense research. The integration of imaging characteristics and genomic data has started a new trend in approach toward the management of brain tumors. The aim of this article is to summarize the established and potential applications of these techniques in tumor diagnosis and classification, treatment planning, and posttreatment assessment and surveillance.

Keywords

Brain tumors MRI MR spectroscopy Perfusion Diffusion Radiogenomics 

References

  1. 1.
    Law M, Young RJ, Babb JS et al (2008) Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MRI. Radiology 247:490–498CrossRefGoogle Scholar
  2. 2.
    Danchaivijitr N, Waldman AD, Tozer DJ et al (2008) Low-grade gliomas: do changes in rCBV measurements at longitudinal perfusion-weighted MRI predict malignant transformation? Radiology 247:170–178CrossRefGoogle Scholar
  3. 3.
    Mangla R, Singh G, Ziegelitz D et al (2010) Changes in relative cerebral blood volume 1 month after radiation-temozolomide therapy can help predict overall survival in patients with glioblastoma. Radiology 250:887–896Google Scholar
  4. 4.
    Horska A, Barker PB (2010) Imaging of brain tumors: MR spectroscopy and metabolic imaging. Neuroimaging Clin N Am 20:293–310CrossRefGoogle Scholar
  5. 5.
    Cha S (2006) Update on brain tumor imaging: from anatomy to physiology. AJNR Am J Neuroradiol 27:475–487PubMedGoogle Scholar
  6. 6.
    Hilario A, Ramos A, Perez-Nunez A et al (2012) The added value of apparent diffusion coefficient to cerebral blood volume in the preoperative grading of diffuse gliomas. AJNR Am J Neuroradiol 33:701–707CrossRefGoogle Scholar
  7. 7.
    Gupta A, Shah A, Young RJ et al (2010) Imaging of brain tumors: functional magnetic resonance imaging and diffusion tensor imaging. Neuroimag Clin N Am 20:379–400CrossRefGoogle Scholar
  8. 8.
    Al-Okaili R, Krejza J, Woo JH et al (2007) Intraaxial brain masses: MR imaging-based diagnostic strategy-initial experience. Radiology 243:539–550CrossRefGoogle Scholar
  9. 9.
    Chang SM, Nelson S, Vandenberg S et al (2009) Integration of preoperative anatomic and metabolic physiologic imaging of newly diagnosed glioma. J Neurooncol 92:401–415CrossRefGoogle Scholar
  10. 10.
    Kotrotsou A, Zinn PO, Colen RR (2016) Radiomics in brain tumors: an emerging technique for characterization of tumor environment. Magn Reson Imaging Clin N Am 24(4):719–729CrossRefGoogle Scholar
  11. 11.
    Anil R, Colen RR (2016) Imaging genomics in glioblastoma multiforme: a predictive tool for patients prognosis, survival, and outcome. Magn Reson Imaging Clin N Am 24(4):731–740CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Stefanos B. Lachanis
    • 1
    • 2
  • Ioannis E. Papachristos
    • 2
  1. 1.MRI Department401 General Army HospitalAthensGreece
  2. 2.CT-MRI DepartmentIatropolis Medical CenterAthensGreece

Personalised recommendations