Neurosurgical Review

, Volume 42, Issue 1, pp 1–7 | Cite as

Utility of preoperative meningioma consistency measurement with magnetic resonance elastography (MRE): a review

  • Alexander G. ChartrainEmail author
  • Mehmet Kurt
  • Amy Yao
  • Rui Feng
  • Kambiz Nael
  • J Mocco
  • Joshua B. Bederson
  • Priti Balchandani
  • Raj K. ShrivastavaEmail author


Meningioma consistency is a critical factor that influences preoperative planning for surgical resection. Recent studies have investigated the utility of preoperative magnetic resonance elastography (MRE) in predicting meningioma consistency. However, it is unclear whether existing methods are optimal for application to clinical practice. The results and conclusions of these studies are limited by their imaging acquisition methods, such as the use of a single MRE frequency and the use of shear modulus as the final measurement variable, rather than its storage and loss modulus components. In addition, existing studies do not account for the effects of cranial anatomy, which have been shown to significantly distort the MRE signal. Given the interaction of meningiomas with these anatomic structures and the lack of supporting evidence with more accurate imaging parameters, MRE may not yet be reliable for use in clinical practice.


Magnetic resonance elastography Meningioma Tumor consistency 



Magnetic resonance elastography


Fluid-attenuated inversion recovery


Fractional anisotropy


Diffusion-weighted imaging


Diffusion tensor imaging


Computed tomography


Intracranial pressure


Compliance with ethical standards

This work was performed ethically and complies with the ethical standards of our Institutional Review Board.

Disclosure of funding

None for all of the authors.

Conflict of interest

The authors declare that they have no conflict of interest.

Informed consent

Not required for this review as per our Institutional Review Board.


  1. 1.
    Andrew A. Badachhape, Ramona S. Durham, Brent D. Efron, Ruth J. Okamoto, Curtis L. Johnson, Philip V. Bayly (2016) In vivo characterization of the human skull-brain interface using magnetic resonance elastography. Proc. 2016 Biomed. Eng. Soc. Annu. Meet.Google Scholar
  2. 2.
    Asbach P, Klatt D, Hamhaber U, Braun J, Somasundaram R, Hamm B, Sack I (2008) Assessment of liver viscoelasticity using multifrequency MR elastography. Magn Reson Med 60:373–379. doi: 10.1002/mrm.21636 CrossRefPubMedGoogle Scholar
  3. 3.
    Braun J, Guo J, Lützkendorf R, Stadler J, Papazoglou S, Hirsch S, Sack I, Bernarding J (2014) High-resolution mechanical imaging of the human brain by three-dimensional multifrequency magnetic resonance elastography at 7T. NeuroImage 90:308–314. doi: 10.1016/j.neuroimage.2013.12.032 CrossRefPubMedGoogle Scholar
  4. 4.
    Chauvet D, Imbault M, Capelle L, Demene C, Mossad M, Karachi C, Boch A-L, Gennisson J-L, Tanter M (2015) In vivo measurement of brain tumor elasticity using intraoperative shear wave elastography. Ultraschall der Medizin-Eur J Ultrasound. doi: 10.1055/s-0034-1399152
  5. 5.
    Clayton EH, Genin GM, Bayly PV (2012) Transmission, attenuation and reflection of shear waves in the human brain. J R Soc Interface 9:2899–2910. doi: 10.1098/rsif.2012.0325 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Fehlner A, Hirsch S, Weygandt M, Christophel T, Barnhill E, Kadobianskyi M, Braun J, Bernarding J, Lützkendorf R, Sack I, Hetzer S (2016) Increasing the spatial resolution and sensitivity of magnetic resonance elastography by correcting for subject motion and susceptibility-induced image distortions. J Magn Reson Imaging. doi: 10.1002/jmri.25516
  7. 7.
    Fehlner A, Papazoglou S, McGarry MD, Paulsen KD, Guo J, Streitberger K-J, Hirsch S, Braun J, Sack I (2015) Cerebral multifrequency MR elastography by remote excitation of intracranial shear waves. NMR Biomed 28:1426–1432. doi: 10.1002/nbm.3388 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Guo J, Hirsch S, Fehlner A, Papazoglou S, Scheel M, Braun J, Sack I (2013) Towards an elastographic atlas of brain anatomy. PLoS One. doi: 10.1371/journal.pone.0071807
  9. 9.
    Holland D, Kuperman JM, Dale AM (2011) Efficient correction of inhomogeneous static magnetic field-induced distortion in echo planar imaging. NeuroImage 50:1–18. doi: 10.1016/j.neuroimage.2009.11.044.Efficient CrossRefGoogle Scholar
  10. 10.
    Hoover JM, Morris JM, Meyer FB (2011) Use of preoperative magnetic resonance imaging T1 and T2 sequences to determine intraoperative meningioma consistency. Surg Neurol Int 2:142. doi: 10.4103/2152-7806.85983 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Hughes JD, Fattahi N, Van Gompel J, Arani A, Meyer F, Lanzino G, Link MJ, Ehman R, Huston J (2015) Higher-resolution magnetic resonance elastography in meningiomas to determine intratumoral consistency. Neurosurgery 77:653–659. doi: 10.1227/NEU.0000000000000892 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Di Ieva A, Grizzi F, Rognone E, Tse ZTH, Parittotokkaporn T, Rodriguez y Baena F, Tschabitscher M, Matula C, Trattnig S, Rodriguez y Baena R (2010) Magnetic resonance elastography: a general overview of its current and future applications in brain imaging. Neurosurg Rev 33:137–145. doi:  10.1007/s10143-010-0249-6
  13. 13.
    Johnson CL, Holtrop JL, McGarry MD, Weaver JB, Paulsen KD, Georgiadis JG, Sutton BP (2014) 3D multislab, multishot acquisition for fast, whole-brain MR elastography with high signal-to-noise efficiency. Magn Reson Med 71:477–485CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Johnson CL, Schwarb H, D.J. McGarry M, Anderson AT, Huesmann GR, Sutton BP, Cohen NJ (2016) Viscoelasticity of subcortical gray matter structures. Hum Brain Mapp doi:  10.1002/hbm.23314
  15. 15.
    Kai Y, Hamada JI, Morioka M, Yano S, Todaka T, Ushio Y (2002) Appropriate interval between embolization and surgery in patients with meningioma. Am J Neuroradiol 23:139–142PubMedGoogle Scholar
  16. 16.
    Kashimura H, Inoue T, Ogasawara K, Arai H, Otawara Y, Kanbara Y, Ogawa A (2007) Prediction of meningioma consistency using fractional anisotropy value measured by magnetic resonance imaging. J Neurosurg 107:784–787. doi: 10.3171/jns-07/10/0784 CrossRefPubMedGoogle Scholar
  17. 17.
    Kruse SA, Rose GH, Glaser KJ, Manduca A, Felmlee JP, Jack CR, Ehman RL (2008) Magnetic resonance elastography of the brain. NeuroImage 39:231–237. doi: 10.1016/j.neuroimage.2007.08.030 CrossRefPubMedGoogle Scholar
  18. 18.
    Kurt M, Han Lv, Kaveh Laksari, Lyndia Wu, Karla Epperson, David B. Camarillo, Kim B. Pauly, Max Wintermark (2016) In vivo multi-frequency magnetic resonance elastography of the human brain: which frequencies matter? Proc. 2016 Biomed. Eng. Soc. Annu. Meet.Google Scholar
  19. 19.
    Low G (2016) General review of magnetic resonance elastography. World J Radiol 8:59. doi: 10.4329/wjr.v8.i1.59 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Manduca A, Oliphant TE, Dresner MA, Mahowald JL, Kruse SA, Amromin E, Felmlee JP, Greenleaf JF, Ehman RL (2001) Magnetic resonance elastography: non-invasive mapping of tissue elasticity. Med Image Anal 5:237–254. doi: 10.1016/S1361-8415(00)00039-6 CrossRefPubMedGoogle Scholar
  21. 21.
    Mariappan YK, Glaser KJ, Ehman RL (2010) Magnetic resonance elastography: a review. Clin Anat 23:497–511. doi: 10.1002/ca.21006 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    McGrath DM, Ravikumar N, Beltrachini L, Wilkinson ID, Frangi AF, Taylor ZA (2016) Evaluation of wave delivery methodology for brain MRE: insights from computational simulations. Magn Reson Med n/a-n/a. doi: 10.1002/mrm.26333
  23. 23.
    McGrath DM, Ravikumar N, Wilkinson ID, Frangi AF, Taylor ZA (2016) Magnetic resonance elastography of the brain: an in silico study to determine the influence of cranial anatomy. Magn Reson Med 76:645–662. doi: 10.1002/mrm.25881 CrossRefPubMedGoogle Scholar
  24. 24.
    Murphy MC, Huston J, Glaser KJ, Manduca A, Meyer FB, Lanzino G, Morris JM, Felmlee JP, Ehman RL (2013) Preoperative assessment of meningioma stiffness using magnetic resonance elastography. J Neurosurg 118:643–648. doi: 10.3171/2012.9.JNS12519 CrossRefPubMedGoogle Scholar
  25. 25.
    Muthupillai R, Lomas D, Rossman P, Greenleaf J, Manduca A, Ehman R (1995) Magnetic resonance elastography by direct visualization of propagating acoustic strain waves. Science (80- ) 269:1854–1857. doi:  10.1126/science.7569924
  26. 26.
    Okamoto RJ, Clayton EH, Bayly PV (2011) Viscoelastic properties of soft gels: comparison of magnetic resonance elastography and dynamic shear testing in the shear wave regime. Phys Med Biol 56:6379–6400. doi: 10.1088/0031-9155/56/19/014 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Ortega-Porcayo LA, Ballesteros-Zebadúa P, Marrufo-Meléndez OR, Ramírez-Andrade JJ, Barges-Coll J, Tecante A, Ramírez-Gilly M, Gómez-Amador JL (2015) Prediction of mechanical properties and subjective consistency of meningiomas using T1-T2 assessment versus fractional anisotropy. World Neurosurg 84:1691–1698. doi: 10.1016/j.wneu.2015.07.018 CrossRefPubMedGoogle Scholar
  28. 28.
    Papazoglou S, Hirsch S, Braun J, Sack I (2012) Multifrequency inversion in magnetic resonance elastography. Phys Med Biol 57:2329–2346. doi: 10.1088/0031-9155/57/8/2329 CrossRefPubMedGoogle Scholar
  29. 29.
    Reiss-Zimmermann M, Streitberger K-J, Sack I, Braun J, Arlt F, Fritzsch D, Hoffmann K-T (2015) High resolution imaging of viscoelastic properties of intracranial tumours by multi-frequency magnetic resonance elastography. Clin Neuroradiol 25:371–378. doi: 10.1007/s00062-014-0311-9 CrossRefPubMedGoogle Scholar
  30. 30.
    Romani R, Tang W, Mao Y, Wang D, Tang H, Zhu F, Che X, Gong Y, Zheng K, Zhong P, Li S, Bao W, Benner C, Wu J, Zhou L (2014) Diffusion tensor magnetic resonance imaging for predicting the consistency of intracranial meningiomas. Acta Neurochir 156:1837–1845. doi: 10.1007/s00701-014-2149-y CrossRefPubMedGoogle Scholar
  31. 31.
    Sack I, Beierbach B, Wuerfel J, Klatt D, Hamhaber U, Papazoglou S, Martus P, Braun J (2009) The impact of aging and gender on brain viscoelasticity. NeuroImage 46:652–657. doi: 10.1016/j.neuroimage.2009.02.040 CrossRefPubMedGoogle Scholar
  32. 32.
    Shiroishi MS, Cen SY, Tamrazi B, D’Amore F, Lerner A, King KS, Kim PE, Law M, Hwang DH, Boyko OB, Liu CSJ (2016) Predicting meningioma consistency on preoperative neuroimaging studies. Neurosurg Clin N Am 27:145–154. doi: 10.1016/ CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Sitthinamsuwan B, Khampalikit I, Nunta-aree S, Srirabheebhat P, Witthiwej T, Nitising A (2012) Predictors of meningioma consistency: a study in 243 consecutive cases. Acta Neurochir 154:1383–1389. doi: 10.1007/s00701-012-1427-9 CrossRefPubMedGoogle Scholar
  34. 34.
    Smith KA, Leever JD, Chamoun RB (2015) Prediction of consistency of pituitary adenomas by magnetic resonance imaging. J Neurol Surgery, Part B Skull Base 76:340–343. doi: 10.1055/s-0035-1549005 CrossRefGoogle Scholar
  35. 35.
    Teasdale E, Patterson J, McLellan D, Macpherson P (1984) Subselective preoperative embolization for meningiomas. J Neurosurg 60:506–511. doi: 10.3171/jns.1984.60.3.0506 CrossRefPubMedGoogle Scholar
  36. 36.
    Watanabe K, Kakeda S, Yamamoto J, Ide S, Ohnari N, Nishizawa S, Korogi Y (2016) Prediction of hard meningiomas: quantitative evaluation based on the magnetic resonance signal intensity. Acta Radiol 57:333–340. doi: 10.1177/0284185115578323 CrossRefPubMedGoogle Scholar
  37. 37.
    Xu L, Lin Y, Han JC, Xi ZN, Shen H, Gao PY (2007) Magnetic resonance elastography of brain tumors: preliminary results. Acta Radiol 48:327–330. doi: 10.1080/02841850701199967 CrossRefPubMedGoogle Scholar
  38. 38.
    Yamaguchi N, Kawase T, Sagoh M, Ohira T, Shiga H, Toya S (1997) Prediction of consistency of meningiomas with preoperative magnetic resonance imaging. Surg Neurol 48:579–583. doi: 10.1016/S0090-3019(96)00439-9 CrossRefPubMedGoogle Scholar
  39. 39.
    Yao A, Pain M, Balchandani P, Shrivastava RK (2016) Can MRI predict meningioma consistency?: a correlation with tumor pathology and systematic review. Neurosurg Rev. doi: 10.1007/s10143-016-0801-0
  40. 40.
    Yrjänä SK, Tuominen H, Karttunen A, Lähdesluoma N, Heikkinen E, Koivukangas J (2006) Low-field MR imaging of meningiomas including dynamic contrast enhancement study: evaluation of surgical and histopathologic characteristics. Am J Neuroradiol 27:2128–2134PubMedGoogle Scholar
  41. 41.
    Zada G, Yashar P, Robison A, Winer J, Khalessi A, Mack WJ, Giannotta SL (2013) A proposed grading system for standardizing tumor consistency of intracranial meningiomas. Neurosurg Focus 35:E1. doi: 10.3171/2013.8.FOCUS13274 CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Alexander G. Chartrain
    • 1
    Email author
  • Mehmet Kurt
    • 2
  • Amy Yao
    • 1
  • Rui Feng
    • 1
  • Kambiz Nael
    • 1
  • J Mocco
    • 1
  • Joshua B. Bederson
    • 1
  • Priti Balchandani
    • 1
  • Raj K. Shrivastava
    • 1
    Email author
  1. 1.Department of NeurosurgeryIcahn School of Medicine at Mount SinaiNew YorkUSA
  2. 2.Department of Mechanical EngineeringStevens Institute of TechnologyHobokenUSA

Personalised recommendations