Skip to main content
SpringerLink
Log in

Brain Tumour Imaging: Developing Techniques and Future Perspectives

  • Chapter
  • First Online:
Atlas of Clinical Cases on Brain Tumor Imaging

  • 876 Accesses

Abstract

This chapter gives an overview of emerging MRI techniques, which show promise in the application of brain tumour imaging, but are not yet used routinely in the clinic. For each method, the basic principles are reviewed, and potential applications and advantages/disadvantages for widespread clinical use discussed.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover 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

Similar content being viewed by others

References

  1. Detre JA, Leigh JS, Williams DS, Koretsky AP. Perfusion imaging. Magn Res Med. 1992;23:37–45.

    Article  CAS  Google Scholar 

  2. Williams DS, Detre JA, Leigh JS, Koretsky AP. Magnetic resonance imaging of perfusion using spin inversion of arterial water. Proc Natl Acad Sci. 1992;89:212–6. https://doi.org/10.1073/pnas.89.1.212.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Alsop DC, Detre JA, Golay X, et al. Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: a consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magn Reson Med. 2014;116:102–16. https://doi.org/10.1002/mrm.25197.

    Article  Google Scholar 

  4. O’Connor JPB, Tofts PS, Miles KA, et al. Dynamic contrast-enhanced imaging techniques: CT and MRI. Br J Radiol. 2011;84:S112–20. https://doi.org/10.1259/bjr/55166688.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Grobner T. Gadolinium—a specific trigger for the development of nephrogenic brosing dermopathy and nephrogenic systemic brosis? Nephrol Dial Transpl. 2006;21:1104–8. https://doi.org/10.1093/ndt/gfk062.

    Article  CAS  Google Scholar 

  6. Marckmann P. Nephrogenic systemic fibrosis: suspected causative role of gadodiamide used for contrast-enhanced magnetic resonance imaging. J Am Soc Nephrol. 2006;17:2359–62. https://doi.org/10.1681/ASN.2006060601.

    Article  PubMed  Google Scholar 

  7. Gulani V, Calamante F, Shellock FG, et al. Gadolinium deposition in the brain: summary of evidence and recommendations. Lancet Neurol. 2017;16:564–70. https://doi.org/10.1016/S1474-4422(17)30158-8.

    Article  PubMed  Google Scholar 

  8. Lehmann P, Monet P, de Marco G, et al. A comparative study of perfusion measurement in brain tumours at 3 Tesla MR: arterial spin labeling versus dynamic susceptibility contrast-enhanced MRI. Eur Neurol. 2010;64:21–6. https://doi.org/10.1159/000311520.

    Article  CAS  PubMed  Google Scholar 

  9. Järnum H, Steffensen EG, Knutsson L, et al. Perfusion MRI of brain tumours: a comparative study of pseudo-continuous arterial spin labelling and dynamic susceptibility contrast imaging. Neuroradiology. 2010;52:307–17. https://doi.org/10.1007/s00234-009-0616-6.

    Article  PubMed  Google Scholar 

  10. Reginster P, Martin B, Denolin V. Comparative study of pseudo-continuous arterial spin labeling and dynamic susceptibility contrast imaging at 3.0 Tesla in brain tumors. Neurooncol Open Access. 2017;2:1–13. https://doi.org/10.21767/2572-0376.100018.

    Article  Google Scholar 

  11. Sunwoo L, Yun TJ, You S-H, et al. Differentiation of glioblastoma from brain metastasis: qualitative and quantitative analysis using arterial spin labeling MR imaging. PLoS One. 2016;11:e0166662. https://doi.org/10.1371/journal.pone.0166662.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kelly PJ, Daumas-Duport C, Scheithauer BW, et al. Stereotactic histologic correlations of computed tomography- and magnetic resonance imaging-defined abnormalities in patients with glial neoplasms. Mayo Clin Proc. 1987;62:450–9.

    Article  CAS  PubMed  Google Scholar 

  13. Noguchi T, Yoshiura T, Hiwatashi A, et al. Perfusion imaging of brain tumors using arterial spin-labeling: correlation with histopathologic vascular density. Am J Neuroradiol. 2008;29:688–93. https://doi.org/10.3174/ajnr.A0903.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Yoo R-E, Yun TJ, Cho YD, et al. Utility of arterial spin labeling perfusion magnetic resonance imaging in prediction of angiographic vascularity of meningiomas. J Neurosurg. 2016;125:536–43. https://doi.org/10.3171/2015.8.JNS151211.536.

    Article  CAS  PubMed  Google Scholar 

  15. Dangouloff-Ros V, Deroulers C, Foissac F, et al. Arterial spin labeling to predict brain tumor grading in children: correlations between histopathologic vascular density and perfusion MR imaging. Radiology. 2016;281:553–66. https://doi.org/10.1148/radiol.2016152228.

    Article  PubMed  Google Scholar 

  16. Law-ye B, Schertz M, Galanaud D, et al. Arterial spin labeling to predict brain tumor grading: limits of cutoff cerebral blood flow values. Radiology. 2017;282:1–3.

    Article  Google Scholar 

  17. Yoo RE, Choi SH, Cho HR, et al. Tumor blood flow from arterial spin labeling perfusion MRI: a key parameter in distinguishing high-grade gliomas from primary cerebral lymphomas, and in predicting genetic biomarkers in high-grade gliomas. J Magn Reson Imaging. 2013;38:852–60. https://doi.org/10.1002/jmri.24026.

    Article  PubMed  Google Scholar 

  18. Hartmann M, Heiland S, Harting I, et al. Distinguishing of primary cerebral lymphoma from high-grade glioma with perfusion-weighted magnetic resonance imaging. Neurosci Lett. 2003;338:119–22. https://doi.org/10.1016/S0304-3940(02)01367-8.

    Article  CAS  PubMed  Google Scholar 

  19. Ma JH, Kim HS, Rim NJ, et al. Differentiation among glioblastoma multiforme, solitary metastatic tumor, and lymphoma using whole-tumor histogram analysis of the normalized cerebral blood volume in enhancing and perienhancing lesions. Am J Neuroradiol. 2010;31:1699–706. https://doi.org/10.3174/ajnr.A2161.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Calli C, Kitis O, Yunten N, et al. Perfusion and diffusion MR imaging in enhancing malignant cerebral tumors. Eur J Radiol. 2006;58:394–403. https://doi.org/10.1016/j.ejrad.2005.12.032.

    Article  PubMed  Google Scholar 

  21. Mullins ME, Barest GD, Schaefer PW, et al. Radiation necrosis versus glioma recurrence: conventional MR imaging clues to diagnosis. Am J Neuroradiol. 2005;26:1967–72.

    PubMed  PubMed Central  Google Scholar 

  22. Linhares P, Carvalho B, Figueiredo R, et al. Early pseudoprogression following chemoradiotherapy in glioblastoma patients: the value of RANO evaluation. J Oncol. 2013;2013:690585. https://doi.org/10.1155/2013/690585.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Xu Q, Liu Q, Ge H, et al. Tumor recurrence versus treatment effects in glioma. Medicine (Baltimore). 2017;96:e9332. https://doi.org/10.1097/MD.0000000000009332.

    Article  Google Scholar 

  24. Oszunar Y, Mullins ME, Kwong K, et al. Glioma recurrence versus radiation necrosis? A pilot comparison of arterial spin-labeled, dynamic susceptibility contrast enhanced MRI, and FDG-PET imaging. Acad Radiol. 2010;17:282–90. https://doi.org/10.1016/j.acra.2009.10.024.

    Article  Google Scholar 

  25. Choi JC, Kim HS, Jahng G-H, et al. Pseudoprogression in patients with glioblastoma: added value of arterial spin labeling to dynamic susceptibility contrast perfusion MR imaging. Acta radiol. 2013;54:448–54.

    Article  PubMed  Google Scholar 

  26. Liu G, Song X, Chan KWY, McMahon MT. Nuts and bolts of CEST MR imaging. NMR Biomed. 2013;26:810–28. https://doi.org/10.1002/nbm.2899.Nuts.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Zhou J, Payen J, Wilson DA, et al. Using the amide proton signals of intracellular proteins and peptides to detect pH effects in MRI. Nat Med. 2003;9:1085–90.

    Article  CAS  PubMed  Google Scholar 

  28. Zhou J, Blakeley JO, Hua J, et al. Practical data acquisition method for human brain tumor amide proton transfer (APT) imaging. Magn Reson Med. 2008;60:842–9. https://doi.org/10.1002/mrm.21712.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Togao O, Yoshiura T, Keupp J, et al. Amide proton transfer imaging of adult diffuse gliomas: correlation with histopathological grades. Neuro-Oncology. 2014;16:441–8. https://doi.org/10.1093/neuonc/not158.

    Article  CAS  PubMed  Google Scholar 

  30. Park JE, Kim HS, Park KJ, et al. Histogram analysis of amide proton transfer imaging to identify contrast-enhancing low-grade brain tumor that mimics high-grade tumor: increased accuracy of MR perfusion. Radiology. 2015;277:151–61. https://doi.org/10.1148/radiol.2015142347.

    Article  PubMed  Google Scholar 

  31. Harston GWJ, Tee YK, Blockley N, et al. Identifying the ischaemic penumbra using pH-weighted magnetic resonance imaging. Brain. 2015;138:36–42. https://doi.org/10.1093/brain/awu374.

    Article  PubMed  Google Scholar 

  32. Zhou J, Tryggestad E, Wen Z, et al. Differentiation between glioma and radiation necrosis. Nat Med. 2011;17:130–4. https://doi.org/10.1038/nm.2268.Differentiation.

    Article  CAS  PubMed  Google Scholar 

  33. Sagiyama K, Mashimo T, Togao O, et al. In vivo chemical exchange saturation transfer imaging allows early detection of a therapeutic response in glioblastoma. Proc Natl Acad Sci U S A. 2014;111:4542–7. https://doi.org/10.1073/pnas.1323855111.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Mehrabian H, Myrehaug S, Soliman H, et al. Evaluation of glioblastoma response to therapy with chemical exchange saturation transfer. Int J Radiat Oncol Biol Phys. 2018;101:713–23. https://doi.org/10.1016/j.ijrobp.2018.03.057.

    Article  CAS  PubMed  Google Scholar 

  35. Tee YK, Harston GWJ, Blockley N, et al. Comparing different analysis methods for quantifying the MRI amide proton transfer (APT) effect in hyperacute stroke patients. NMR Biomed. 2014;27:1019–29. https://doi.org/10.1002/nbm.3147.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Chappell MA, Donahue MJ, Tee YK, et al. Quantitative Bayesian model-based analysis of amide proton transfer MRI. Magn Reson Med. 2013;70:556–67. https://doi.org/10.1002/mrm.24474.

    Article  CAS  PubMed  Google Scholar 

  37. Park KJ, Kim HS, Park JE, Shim WH. Added value of amide proton transfer imaging to conventional and perfusion MR imaging for evaluating the treatment response of newly diagnosed glioblastoma. Eur Radiol. 2016;26:4390–403. https://doi.org/10.1007/s00330-016-4261-2.

    Article  PubMed  Google Scholar 

  38. Reichenbach JR, Venkatesan R, Schillinger DJ, et al. Small vessels in the human brain: MR venography with deoxyhemoglobin as an intrinsic contrast agent. Radiology. 1997;204:272–7. https://doi.org/10.1148/radiology.204.1.9205259.

    Article  CAS  PubMed  Google Scholar 

  39. Haacke EM, Xu Y, Cheng Y-CN, Reichenbach JR. Susceptibility weighted imaging (SWI). Magn Reson Med. 2004;52:612–8. https://doi.org/10.1002/mrm.20198.

    Article  PubMed  Google Scholar 

  40. Sehgal V, Delproposto Z, Haacke EM, et al. Clinical applications of neuroimaging with susceptibility-weighted imaging. J Magn Reson Imaging. 2005;22:439–50. https://doi.org/10.1002/jmri.20404.

    Article  PubMed  Google Scholar 

  41. Rauscher A, Sedlacik J, Barth M, et al. Magnetic susceptibility-weighted MR phase imaging of the human brain. Am J Neuroradiol. 2005;26:736–42. pii: 26/4/736.

    PubMed  PubMed Central  Google Scholar 

  42. Di Ieva A, Matula C, Grizzi F, et al. Fractal analysis of the susceptibility weighted imaging patterns in malignant brain tumors during antiangiogenic treatment: technical report on four cases serially imaged by 7T magnetic resonance during a period of four weeks. World Neurosurg. 2012;77:28–31. https://doi.org/10.1016/j.wneu.2011.09.006.

    Article  Google Scholar 

  43. Lupo JM, Chuang CF, Chang SM, et al. 7-Tesla susceptibility-weighted imaging to assess the effects of radiotherapy on normal-appearing brain in patients with glioma. Int J Radiat Oncol Biol Phys. 2012;82:e493–500. https://doi.org/10.1016/j.ijrobp.2011.05.046.

    Article  PubMed  Google Scholar 

  44. Löbel U, Sedlacik J, Sabin ND, et al. Three-dimensional susceptibility-weighted imaging and two-dimensional T2∗-weighted gradient-echo imaging of intratumoral hemorrhages in pediatric diffuse intrinsic pontine glioma. Neuroradiology. 2010;52:1167–77. https://doi.org/10.1007/s00234-010-0771-9.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Li C, Ai B, Li Y, et al. Susceptibility-weighted imaging in grading brain astrocytomas. Eur J Radiol. 2010;75:81–5. https://doi.org/10.1016/j.ejrad.2009.08.003.

    Article  Google Scholar 

  46. Lou X, Ma L, Wang FL, et al. Susceptibility-weighted imaging in the diagnosis of early basal ganglia germinoma. Am J Neuroradiol. 2009;30:1694–9. https://doi.org/10.3174/ajnr.A1696.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Park MJ, Kim HS, Jahng GH, et al. Semiquantitative assessment of intratumoral susceptibility signals using non-contrast-enhanced high-field high-resolution susceptibility-weighted imaging in patients with gliomas: comparison with MR perfusion imaging. Am J Neuroradiol. 2009;30:1402–8. https://doi.org/10.3174/ajnr.A1593.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Pinker K, Noebauer-Huhmann IM, Stavrou I, et al. High-resolution contrast-enhanced, susceptibility-weighted MR imaging at 3T in patients with brain tumors: correlation with positron-emission tomography and histopathologic findings. Am J Neuroradiol. 2007;28:1280–6. https://doi.org/10.3174/ajnr.A0540.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hsu CC-T, Kwan GNC, Hapugoda S, et al. Susceptibility weighted imaging in acute cerebral ischemia: review of emerging technical concepts and clinical applications. Neuroradiol J. 2017;30:109–19. https://doi.org/10.1177/1971400917690166.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Thomas B, Somasundaram S, Thamburaj K, et al. Clinical applications of susceptibility weighted MR imaging of the brain—a pictorial review. Neuroradiology. 2008;50:105–16. https://doi.org/10.1007/s00234-007-0316-z.

    Article  PubMed  Google Scholar 

  51. Oot RF, New PFJ, Pile-Spellman J, et al. The detection of intracranial calcifications by MR. Am J Neuroradiol. 1986;7:801–9. https://doi.org/10.3174/ajnr.a1461.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Avrahami E, Cohn DF, Feibel M, Tadmor R. MRI demonstration and CT correlation of the brain in patients with idiopathic intracerebral calcification. J Neurol. 1994;241:381–4.

    Article  CAS  PubMed  Google Scholar 

  53. Berberat J, Grobholz R, Boxheimer L, et al. Differentiation between calcification and hemorrhage in brain tumors using susceptibility-weighted imaging: a pilot study. Am J Roentgenol. 2014;202:847–50. https://doi.org/10.2214/AJR.13.10745.

    Article  Google Scholar 

  54. Zulfiqar M, Dumrongpisutikul N, Intrapiromkul J, Yousem DM. Detection of intratumoral calcification in oligodendrogliomas by susceptibility-weighted MR imaging. Am J Neuroradiol. 2012;33:858–64. https://doi.org/10.3174/ajnr.A2862.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Grabner G, Kiesel B, Wöhrer A, et al. Local image variance of 7 Tesla SWI is a new technique for preoperative characterization of diffusely infiltrating gliomas: correlation with tumour grade and IDH1 mutational status. Eur Radiol. 2017;27:1556–67. https://doi.org/10.1007/s00330-016-4451-y.

    Article  PubMed  Google Scholar 

  56. Schweser F, Deistung A, Lehr BW, Reichenbach JR. Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: an approach to in vivo brain iron metabolism? Neuroimage. 2011;54:2789–807. https://doi.org/10.1016/j.neuroimage.2010.10.070.

    Article  PubMed  Google Scholar 

  57. Deistung A, Schweser F, Wiestler B, et al. Quantitative susceptibility mapping differentiates between blood depositions and calcifications in patients with glioblastoma. PLoS One. 2013;8:1–8. https://doi.org/10.1371/journal.pone.0057924.

    Article  CAS  Google Scholar 

  58. Mendichovszky I, Jackson A. Imaging hypoxia in gliomas. Br J Radiol. 2011;84:145–58. https://doi.org/10.1259/bjr/82292521.

    Article  Google Scholar 

  59. Brown JM, Wilson WR. Exploiting tumour hypoxia in cancer treatment. Nat Rev Cancer. 2004;4:437–47. https://doi.org/10.1038/nrc1367.

    Article  CAS  PubMed  Google Scholar 

  60. Preibisch C, Shi K, Kluge A, et al. Characterizing hypoxia in human glioma: a simultaneous multimodal MRI and PET study. NMR Biomed. 2017;30:1–13. https://doi.org/10.1002/nbm.3775.

    Article  CAS  Google Scholar 

  61. Ogawa S, Lee TM, Kay AR. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci USA. 1990;87:9868–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Yablonskiy DA, Haacke EM. Theory of NMR signal behavior in magnetically inhomogeneous tissues: the static dephasing regime. Magn Reson Med. 1994;32:749–63.

    Article  CAS  PubMed  Google Scholar 

  63. Christen T, Lemasson B, Pannetier N, et al. Is T2∗ enough to assess oxygenation? Quantitative blood oxygen level-dependent analysis in brain tumor. Radiology. 2012;262:495–502. https://doi.org/10.1148/radiol.11110518.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Christen T, Schmiedeskamp H, Straka M, et al. Measuring brain oxygenation in humans using a multiparametric quantitative blood oxygenation level dependent MRI approach. Magn Reson Med. 2012;68:905–11. https://doi.org/10.1002/mrm.23283.

    Article  PubMed  Google Scholar 

  65. He X, Yablonskiy DA. Quantitative BOLD: mapping of human cerebral deoxygenated blood volume and oxygen extraction fraction: default state. Magn Reson Med. 2007;57:115–26. https://doi.org/10.1002/mrm.21108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Stadlbauer A, Merkel A, Zimmermann M, et al. Intraoperative magnetic resonance imaging of cerebral oxygen metabolism during resection of brain lesions. World Neurosurg. 2017;100:388–94. https://doi.org/10.1016/j.wneu.2017.01.060.

    Article  PubMed  Google Scholar 

  67. Stone AJ, Blockley NP. A streamlined acquisition for mapping baseline brain oxygenation using quantitative BOLD. Neuroimage. 2017;147:79–88. https://doi.org/10.1016/J.NEUROIMAGE.2016.11.057.

    Article  PubMed  Google Scholar 

  68. Jensen JH, Helpern JA, Ramani A, et al. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med. 2005;53:1432–40. https://doi.org/10.1002/mrm.20508.

    Article  PubMed  Google Scholar 

  69. Wu EX, Cheung MM. MR diffusion kurtosis imaging for neural tissue characterization. NMR Biomed. 2010;23:836–48. https://doi.org/10.1002/nbm.1506.

    Article  PubMed  Google Scholar 

  70. Raab P, Hattingen E, Franz K, Zanella FE, Lanfermann H. Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology. 2010;254:876–81. https://doi.org/10.1148/radiol.09090819.

    Article  PubMed  Google Scholar 

  71. Van Cauter S, Veraart J, Sijbers J, et al. Gliomas: diffusion kurtosis MR imaging in grading. Radiology. 2012;263:492–501.

    Article  PubMed  Google Scholar 

  72. Falk Delgado A, Nilsson M, van Westen D, Falk Delgado A. Glioma grade discrimination with MR diffusion kurtosis imaging: a meta-analysis of diagnostic accuracy. Radiology. 2018;287:119–27. https://doi.org/10.1148/radiol.2017171315.

    Article  PubMed  Google Scholar 

  73. Hempel JM, Schittenhelm J, Brendle C, et al. Histogram analysis of diffusion kurtosis imaging estimates for in vivo assessment of 2016 WHO glioma grades: a cross-sectional observational study. Eur J Radiol. 2017;95:202–11. https://doi.org/10.1016/j.ejrad.2017.08.008.

    Article  PubMed  Google Scholar 

  74. Jiang R, Jiang J, Zhao L, et al. Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation. Oncotarget. 2015;6:42380–93. https://doi.org/10.18632/oncotarget.5675.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Nilsson M, Englund E, Szczepankiewicz F, et al. Imaging brain tumour microstructure. Neuroimage. 2018;182:232–50. https://doi.org/10.1016/j.neuroimage.2018.04.075.

    Article  PubMed  Google Scholar 

  76. Poot DHJ, den Dekker AJ, Achten E, et al. Optimal experimental design for diffusion kurtosis imaging. IEEE Trans Med Imaging. 2010;29:819–29. https://doi.org/10.1109/TMI.2009.2037915.

    Article  PubMed  Google Scholar 

  77. Metzler-Baddeley C, O’Sullivan MJ, Bells S, et al. How and how not to correct for CSF-contamination in diffusion MRI. Neuroimage. 2012;59:1394–403. https://doi.org/10.1016/j.neuroimage.2011.08.043.

    Article  PubMed  Google Scholar 

  78. Collier Q, Veraart J, Jeurissen B, et al. Diffusion kurtosis imaging with free water elimination: a Bayesian estimation approach. Magn Reson Med. 2018;80:802–13. https://doi.org/10.1002/mrm.27075.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Le Bihan D, Breton E, Lallemand D, et al. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology. 1988;168:566–7.

    Article  Google Scholar 

  80. Togao O, Hiwatashi A, Yamashita K, et al. Differentiation of high-grade and low-grade diffuse gliomas by intravoxel incoherent motion MR imaging. Neuro-Oncology. 2016;18:132–41. https://doi.org/10.1093/neuonc/nov147.

    Article  PubMed  Google Scholar 

  81. Server A, Kulle B, Gadmar ØB, et al. Measurements of diagnostic examination performance using quantitative apparent diffusion coefficient and proton MR spectroscopic imaging in the preoperative evaluation of tumor grade in cerebral gliomas. Eur J Radiol. 2011;80:462–70. https://doi.org/10.1016/j.ejrad.2010.07.017.

    Article  PubMed  Google Scholar 

  82. Lam WWM, Poon WS, Metreweli C. Diffusion MR imaging in glioma: does it have any role in the pre-operation determination of grading of glioma? Clin Radiol. 2002;57:219–25. https://doi.org/10.1053/crad.2001.0741.

    Article  CAS  PubMed  Google Scholar 

  83. Federau C, O’Brien K, Meuli R, et al. Measuring brain perfusion with intravoxel incoherent motion (IVIM): initial clinical experience. J Magn Reson Imaging. 2014;39:624–32. https://doi.org/10.1002/jmri.24195.

    Article  PubMed  Google Scholar 

  84. Bisdas S, Koh TS, Roder C, et al. Intravoxel incoherent motion diffusion-weighted MR imaging of gliomas: feasibility of the method and initial results. Neuroradiology. 2013;55:1189–96. https://doi.org/10.1007/s00234-013-1229-7.

    Article  PubMed  Google Scholar 

  85. Meeus EM, Novak J, Withey SB, et al. Evaluation of intravoxel incoherent motion fitting methods in low-perfused tissue. J Magn Reson Imaging. 2017;45:1325–34. https://doi.org/10.1002/jmri.25411.

    Article  PubMed  Google Scholar 

  86. Wu WC, Chen YF, Tseng HM, et al. Caveat of measuring perfusion indexes using intravoxel incoherent motion magnetic resonance imaging in the human brain. Eur Radiol. 2015;25:2485–92. https://doi.org/10.1007/s00330-015-3655-x.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Catanese A, Malacario F, Cirillo L, et al. Application of intravoxel incoherent motion (IVIM) magnetic resonance imaging in the evaluation of primitive brain tumours. Neuroradiol J. 2018;31:4–9. https://doi.org/10.1177/1971400917693025.

    Article  CAS  PubMed  Google Scholar 

  88. Hare H V., Frost R, Meakin JA, Bulte DP. On the origins of the cerebral IVIM signal. bioRxiv. 2017. https://doi.org/10.1101/158014.

  89. Kim DY, Kim HS, Goh MJ, et al. Utility of intravoxel incoherent motion MR imaging for distinguishing recurrent metastatic tumor from treatment effect following gamma knife radiosurgery: initial experience. AJNR Am J Neuroradiol. 2014;35:2082–90. https://doi.org/10.3174/ajnr.A3995.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Detsky JS, Keith J, Conklin J, et al. Differentiating radiation necrosis from tumor progression in brain metastases treated with stereotactic radiotherapy: utility of intravoxel incoherent motion perfusion MRI and correlation with histopathology. J Neurooncol. 2017;134:433–41. https://doi.org/10.1007/s11060-017-2545-2.

    Article  PubMed  Google Scholar 

  91. Federau C, Hagmann P, Maeder P, et al. Dependence of brain intravoxel incoherent motion perfusion parameters on the cardiac cycle. PLoS One. 2013;8:1–7. https://doi.org/10.1371/journal.pone.0072856.

    Article  CAS  Google Scholar 

  92. Le Bihan D. What can we see with IVIM MRI? Neuroimage. 2019;187:56–67. https://doi.org/10.1016/j.neuroimage.2017.12.062.

    Article  PubMed  Google Scholar 

  93. Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 2016;131:803–20. https://doi.org/10.1007/s00401-016-1545-1.

    Article  PubMed  Google Scholar 

  94. Rutman AM, Kuo MD. Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol. 2009;70:232–41. https://doi.org/10.1016/j.ejrad.2009.01.050.

    Article  PubMed  Google Scholar 

  95. Seow P, Wong J, Ahmad-Annuar A, et al. Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review. Br J Radiol. 2018;91:1–14.

    Article  Google Scholar 

  96. Hait WN. Forty years of translational cancer research. Cancer Discov. 2011;1:383–90. https://doi.org/10.1158/2159-8290.CD-11-0196.

    Article  PubMed  Google Scholar 

  97. Parsons DW, Jones S, Zhang X, et al. An integrated genomic analysis of human glioblastoma multiforme. Science. 2008;321(5897):1807–12. https://doi.org/10.1126/science.1164382.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Nobusawa S, Watanabe T, Kleihues P, Ohgaki H. IDH1 mutations as molecular signature and predictive factor of secondary glioblastomas. Clin Cancer Res. 2009;15:6002–7. https://doi.org/10.1158/1078-0432.CCR-09-0715.

    Article  CAS  PubMed  Google Scholar 

  99. Law M, Brodsky JE, Babb J, et al. High cerebral blood volume in human gliomas predicts deletion of chromosome 1p: preliminary results of molecular studies in gliomas with elevated perfusion. J Magn Reson Imaging. 2007;25:1113–9. https://doi.org/10.1002/jmri.20920.

    Article  PubMed  Google Scholar 

  100. Khayal I, VandenBerg S, Smith K, et al. MRI apparent diffusion coefficient reflects histopathologic subtype, axonal disruption, and tumor fraction in diffuse-type grade II gliomas. Neuro-Oncology. 2011;13:1192–201. https://doi.org/10.1093/neuonc/nou223.

    Article  PubMed  PubMed Central  Google Scholar 

  101. Mahajan A, Goh V, Basu S, et al. Bench to bedside molecular functional imaging in translational cancer medicine: to image or to imagine? Clin Radiol. 2015;70:1060–82. https://doi.org/10.1016/j.crad.2015.06.082.

    Article  CAS  PubMed  Google Scholar 

  102. O’Connor JPB, Aboagye EO, Adams JE, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2017;14:169–86. https://doi.org/10.1038/nrclinonc.2016.162.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. 164 Nottingham Road, Stapleford, Nottingham, NG9 8AR, Institute of Biomedical Engineering, University of Oxford, Oxford, UK

    Paula L. Croal

Authors
  1. Paula L. Croal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paula L. Croal .

Editor information

Editors and Affiliations

  1. Radiology Department, Adnan Menderes University, Aydın, Turkey

    Yelda Özsunar

  2. School of Medicine, Akdeniz University, Antalya, Turkey

    Utku Åženol

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Croal, P.L. (2020). Brain Tumour Imaging: Developing Techniques and Future Perspectives. In: Özsunar, Y., Şenol, U. (eds) Atlas of Clinical Cases on Brain Tumor Imaging. Springer, Cham. https://doi.org/10.1007/978-3-030-23273-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-23273-3_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23272-6

  • Online ISBN: 978-3-030-23273-3

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation