Skip to main content

Abstract

The MR morphologic study, consisting of the acquisition of sequences without and with contrast agent, can be completed with new advanced MR techniques (spectroscopy, diffusion and perfusion), which are particularly useful in cases of diagnostic doubt. These techniques are not usually used in the evaluation of normal and pathologic sequelae after treatment, as these can be well documented with morphologic MR, but they become essential especially in combination when assessing treatment response. Their use is often essential in the differential diagnosis between scar tissue vs. residual tumor, stability vs. progression/recurrence and recurrence vs. radionecrosis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Howe FA, Opstad KS (2003) 1H MR spectroscopy of brain tumours and masses. NMR Biomed 16:123-131

    Article  PubMed  CAS  Google Scholar 

  2. Möller-Hartmann W, Herminghaus S, Krings T et al (2002) Clinical application of proton magnetic resonance spectroscopy in the diagnosis of intracranial mass lesions. Neuroradiology 44:371-381

    Article  PubMed  Google Scholar 

  3. Graves EE, Nelson SJ, Vigneron DB et al (2001) Serial proton MR spectroscopic imaging of recurrent malignant gliomas after gamma knife radiosurgery. AJNR 22:613-624

    PubMed  CAS  Google Scholar 

  4. Tedeschi G, Lundbom N, Raman R et al (1997) Increased choline signal coinciding with malignant degeneration of cerebral gliomas: a serial proton magnetic resonance spectroscopy imaging study. J Neurosurg 87:516-524

    Article  PubMed  CAS  Google Scholar 

  5. Lichy MP, Bachert P, Hamprecht F et al (2006) Application of 1H-MRS spectroscopic imaging in radiation oncology: choline as a marker for determining the relative probability of tumor progression after radiation of glial brain tumors. Rofo 178:627-339

    Article  PubMed  CAS  Google Scholar 

  6. Murphy PS, Rowland IJ, Viviers L et al (2003) Could assessment of glioma methylene lipid resonance by in vivo 1H-MRS be of clinical value? Br J Radiol 76:459-463

    Article  PubMed  CAS  Google Scholar 

  7. Pirzkall A, Mcknight TR, Graves EE et al (2001) MR-spectroscopy guided target delineation for high-grade gliomas. Int J Radiat Oncol Biol Phys 50:915-928

    Article  PubMed  CAS  Google Scholar 

  8. Balmaceda C, Critchell D, Mao X et al (2006) Multisection 1H magnetic resonance spectroscopic imaging assessment of glioma response to chemiotherapy. J Neurooncol 76:185-191

    Article  PubMed  CAS  Google Scholar 

  9. Weybright P, Sundgren PC, Maly P et al (2005) Differentiation between brain tumor recurrence and radiation injury using MR spectroscopy. Am J Roentgenol 185:1471-1476

    Article  Google Scholar 

  10. Zeng QS, Li CF, Zhang K et al (2007) Multivoxel 3D proton MR spectroscopy in the distinction of recurrent glioma from radiation injury. J Neurooncol 84:63-69

    Article  PubMed  CAS  Google Scholar 

  11. Rock JP, Scarpace L, Hearshen D et al (2004) Associations among magnetic resonance spectroscopy, apparent diffusion coefficients, and image-guided histopathology with special attention to radiation necrosis. Neurosurgery 54:1111-1117

    Article  PubMed  Google Scholar 

  12. Smith JS, Cha S, Mayo MC et al (2005) Serial diffusion-weighted magnetic resonance imaging in cases of glioma: distinguishing tumor recurrence from postresection injury. J Neurosurg 103:428-438

    Article  PubMed  Google Scholar 

  13. Ulmer S, Braga TA, Barker FG et al (2006) Clinical and radiographics features of peritumoral infarction following resection of glioblastoma. Neurology 67:1668-1670

    Article  PubMed  CAS  Google Scholar 

  14. Moffat BA, Chenevert TL, Lawrence TS et al (2005) Functional diffusion map: a non invasive MRI biomarker for early stratification of clinical brain tumor response. Proc Natl Acad Sci USA 102:5524-5529

    Article  PubMed  CAS  Google Scholar 

  15. Moffat BA, Chenevert TL, Meyer CR et al (2006) The functional diffusion map: an imaging biomarker for the early prediction of cancer treatment outcome. Neoplasia 8:259-267

    Article  PubMed  CAS  Google Scholar 

  16. Hamstra DA, Galban CJ, Meyer CR et al (2008) Functional diffusion map as an early imaging biomarker for high-grade glioma: correlation with conventional radiologic response and overall survival. J Clin Oncol 26:3387-3394

    Article  PubMed  Google Scholar 

  17. Asao CH, Korogi Y, Kitajima M et al (2005) Diffusion weighted imaging of radiation-induced brain injury for differentiation from tumor recurrence. AJNR 26:1455-1460

    PubMed  Google Scholar 

  18. Hein PA, Eskey CJ, Dunn JF et al (2004) Diffusion-weighted imaging in the follow-up of treated high-grade gliomas: tumor recurrence versus radiation injury. AJNR 25:201-209

    PubMed  Google Scholar 

  19. Xu J-L, Li YL, Liam JM, et al (2010) Distinction between postoperative recurrent glioma and radiation injury using MR diffusion tensor imaging. Neuroradiology 52:1193-1199

    Article  PubMed  Google Scholar 

  20. Al Sayyari A, Buckley R, McHenery C et al (2011) Distinguishing recurrent primary brain tumor from radiation injury: a preliminary study using a susceptibility-weighted MR imaging-guided apparent diffusion coefficient analysis strategy. AJNR Am J Neuroradiol 31:1049-1054

    Article  Google Scholar 

  21. Sudgren PC, Fan X, Weibright P et al (2006) Differentiation of recurrent brain tumor versus radiation injury using diffusion tensor imaging in patients with new contrast-enhancing lesions. Magn Reson Imaging 24:1131-1142

    Article  Google Scholar 

  22. Leon SP, Folkerth RD, Black PM (1996) Microvessel density is a prognostic indicator for patients with astroglial brain tumors. Cancer 77:362-372

    Article  PubMed  CAS  Google Scholar 

  23. Covarrubias DJ, Rosen BR, Lev MH (2004) Dynamic magnetic resonance perfusion imaging of brain tumors. Oncologist 9:528-537

    Article  PubMed  Google Scholar 

  24. Chaskis C, Stadnik T, Michotte A et al (2006) Prognostic value of perfusion-weighted imaging in brain glioma: a prospective study. Acta Neurochir 148:277-285

    Article  CAS  Google Scholar 

  25. Sugahara T, Korogi Y, Tomiguchi S et al (2000) Posttherapeutic intraaxial brain tumor: the value of perfusion-sensitive contrast-enhanced MR imaging for differentiating tumor recurrence from non neoplastic contrast-enhancing tissue. AJNR 21:901-909

    PubMed  CAS  Google Scholar 

  26. Prazincola L, Steno J, Srbecky M et al (2009) MR imaging of late radiation therapy- and chemiotherapy-induced injured: a pictorial essay. Eur Radiol 19:2716-2727

    Article  Google Scholar 

  27. Barajas RF, Chang JS, Segal MS et al (2009) Differentiation of recurrent glioblastoma multiforme from radiation necrosis after external beam radiation therapy with dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 253:486-496

    Article  PubMed  Google Scholar 

  28. Tsien C, Galban CJ, Chenevert TL et al (2010) Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression on in high-grade glioma. J Clin Oncol 28:2293-2299

    Article  PubMed  CAS  Google Scholar 

  29. Di Costanzo A, Scarabino T, Trojsi F et al (2006) Multiparametric 3T MR approach to the assessment of cerebral gliomas: tumor extent and malignancy. Neuroradiology 48:622-631

    Article  PubMed  Google Scholar 

  30. Zeng QS, Li CF, Liu H et al (2007) Distinction between recurrent glioma and radiation injury using magnetic resonance spectroscopy in combination with diffusion-weighted imaging. Int J Radiat Oncol Biol Phys 68:151-158

    Article  PubMed  Google Scholar 

  31. Bobek-Billewicz B, Stasik-Pres G, Majchrzak H et al (2010) Differentiation between brain tumor recurrence and radiation injury using perfusion, diffusion-weighted imaging and MR spectroscopy. Folia Neuropathol 48:81-92

    PubMed  Google Scholar 

  32. Voglein J, Tuttenberg J, Weimer M et al (2011) Treatment monitoring in gliomas: comparisons of dynamic susceptibility-weighted contrast-enhanced and spectroscopic MRI techniques for identifying treatment failure. Invest Radiol 46:390-400

    Article  PubMed  Google Scholar 

  33. Kim YH, Oh SW, Lim YJ et al (2010) Differentiating radiation necrosis from tumor recurrence in high-grade gliomas: assessing the efficacy of 18F-FDG PET, 11 C-methionine PET and perfusion MRI. Clin Neurol Neurosurgery. 112:758-65

    Article  Google Scholar 

  34. Prat R, Galeano I, Lucas A et al (2010) Relative value of magnetic resonance spectroscopy, magnetic resonance perfusion, and 2-(18F) fluoro-2-deoxy-D-glucose positron emission tomography for detection of recurrence or grade increase in gliomas. J Clin Neurosci 17:50-53

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Italia

About this chapter

Cite this chapter

Popolizio, T., Pollice, S., Scarabino, T. (2012). Advanced MR Imaging. In: Scarabino, T. (eds) Imaging Gliomas After Treatment. Springer, Milano. https://doi.org/10.1007/978-88-470-2370-3_10

Download citation

  • DOI: https://doi.org/10.1007/978-88-470-2370-3_10

  • Publisher Name: Springer, Milano

  • Print ISBN: 978-88-470-2369-7

  • Online ISBN: 978-88-470-2370-3

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics