Skip to main content

Recent Advances and Future Perspective in MRI Technique for the Trigeminal Neuralgia

  • Chapter
  • First Online:
Trigeminal Neuralgia
  • 517 Accesses

Abstract

Trigeminal neuralgia is a neurological disorder characterized by recurrent, paroxysmal, short-lasting, lancinating facial pain restricted to the unilateral trigeminal territory, and usually triggered by innoxious stimuli. Trigeminal neuralgia can be divided into classical, secondary, and idiopathic. Trigeminal neuralgia is classified as idiopathic when any diagnostic tests fail to reveal a lesion or disease that can cause trigeminal neuralgia [1]. Neurovascular compression with morphological change of the trigeminal nerve on MRI or during surgery is essential to classify trigeminal neuralgia as classical. Secondary trigeminal neuralgia is categorized when trigeminal neuralgia is caused by an underlying disease such as tumor in cerebellopontine angle/Meckel’s cave or multiple sclerosis [1]. Although the diagnosis of trigeminal neuralgia is made clinically, the combination of high-resolution, 3D T2-weighted images, post-contrast 3D T1-weighted images, and TOF-MRA with 3D reconstruction have provided us reliable information about the presence and severity of neurovascular compression in classical trigeminal neuralgia [2, 3]. It also helps us to exclude other causes of trigeminal neuralgia such as infectious, inflammatory, demyelinating disease, or tumor. Moreover, recent advances in structural and functional imaging enable us to elucidate the pathomechanism of trigeminal neuralgia [4]. In this chapter, we review recent trends in the MR techniques for detecting neurovascular conflict between the trigeminal nerve and vascular structures in trigeminal neuralgia, which includes MR cisternography and post-contrast 3D T1-weighted imaging. We also provide clinical application of MR techniques such as high-resolution, T1-weighted imaging for brain morphometry, diffusion tensor imaging, and resting-state functional MRI to investigate structural and functional alteration in the central nervous system in trigeminal neuralgia. To acquire the previously mentioned, 3D, high-resolution, and various MR images within the clinically acceptable scan time, fast imaging techniques are inevitable. Fast imaging techniques such as parallel imaging, compressed sensing, or modified K-space sampling are briefly reviewed in advance. Lastly, we briefly introduce the clinical application of 7.0 T MRI in trigeminal neuralgia.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

References

  1. Bendtsen L, Zakrzewska JM, Heinskou TB, Hodaie M, Leal PRL, Nurmikko T, et al. Advances in diagnosis, classification, pathophysiology, and management of trigeminal neuralgia. The Lancet Neurology. 2020;19(9):784–96.

    Article  CAS  PubMed  Google Scholar 

  2. Donahue JH, Ornan DA, Mukherjee S. Imaging of vascular compression syndromes. Radiol Clin N Am. 2017;55(1):123–38.

    Article  PubMed  Google Scholar 

  3. Haller S, Etienne L, Kövari E, Varoquaux AD, Urbach H, Becker M. Imaging of neurovascular compression syndromes: trigeminal neuralgia, Hemifacial spasm, vestibular Paroxysmia, and glossopharyngeal neuralgia. AJNR Am J Neuroradiol. 2016;37(8):1384–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Zhang C, Hu H, Das SK, Yang MJ, Li B, Li Y, et al. Structural and functional brain abnormalities in trigeminal neuralgia: a systematic review. J Oral Facial Pain Headache. 2020;34(3):222–35.

    Article  CAS  PubMed  Google Scholar 

  5. Kozak BM, Jaimes C, Kirsch J, Gee MS. MRI techniques to decrease imaging times in children. Radiographics. 2020;40(2):485–502.

    Article  PubMed  Google Scholar 

  6. Hamilton J, Franson D, Seiberlich N. Recent advances in parallel imaging for MRI. Prog Nucl Magn Reson Spectrosc. 2017;101:71–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Cho SJ, Choi YJ, Chung SR, Lee JH, Baek JH. High-resolution MRI using compressed sensing-sensitivity encoding (CS-SENSE) for patients with suspected neurovascular compression syndrome: comparison with the conventional SENSE parallel acquisition technique. Clin Radiol. 2019;74(10):817.e9–e14.

    Article  CAS  PubMed  Google Scholar 

  8. Chavhan GB, Babyn PS, Jankharia BG, Cheng H-LM, Shroff MM. Steady-state MR imaging sequences: physics, classification, and clinical applications. Radiographics. 2008;28(4):1147–60.

    Article  PubMed  Google Scholar 

  9. Schmalbrock P. Comparison of three-dimensional fast spin echo and gradient echo sequences for high-resolution temporal bone imaging. J Magn Reson Imaging. 2000;12(6):814–25.

    Article  CAS  PubMed  Google Scholar 

  10. Ciftci E, Anik Y, Arslan A, Akansel G, Sarisoy T, Demirci A. Driven equilibrium (drive) MR imaging of the cranial nerves V-VIII: comparison with the T2-weighted 3D TSE sequence. Eur J Radiol. 2004;51(3):234–40.

    Article  CAS  PubMed  Google Scholar 

  11. Busse RF, Hariharan H, Vu A, Brittain JH. Fast spin echo sequences with very long echo trains: design of variable refocusing flip angle schedules and generation of clinical T2 contrast. Magn Reson Med. 2006;55(5):1030–7.

    Article  PubMed  Google Scholar 

  12. Jung NY, Moon WJ, Lee MH, Chung EC. Magnetic resonance cisternography: comparison between 3-dimensional driven equilibrium with sensitivity encoding and 3-dimensional balanced fast-field echo sequences with sensitivity encoding. J Comput Assist Tomogr. 2007;31(4):588–91.

    Article  PubMed  Google Scholar 

  13. Byun JS, Kim H-J, Yim YJ, Kim ST, Jeon P, Kim KH, et al. MR imaging of the internal Auditory Canal and inner ear at 3T: comparison between 3D driven equilibrium and 3D balanced fast field Echo sequences. Korean J Radiol. 2008;9(3):212–8.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Mugler JP 3rd. Optimized three-dimensional fast-spin-echo MRI. J Magn Reson Imaging. 2014;39(4):745–67.

    Article  PubMed  Google Scholar 

  15. Markl M, Leupold J. Gradient echo imaging. J Magn Reson Imaging. 2012;35(6):1274–89.

    Article  PubMed  Google Scholar 

  16. Bieri O, Scheffler K. Fundamentals of balanced steady state free precession MRI. J Magn Reson Imaging. 2013;38(1):2–11.

    Article  PubMed  Google Scholar 

  17. Avey G. Technical improvements in head and neck MR imaging: at the cutting edge. Neuroimaging Clin N Am. 2020;30(3):295–309.

    Article  PubMed  Google Scholar 

  18. Touska P, Connor SEJ. Recent advances in MRI of the head and neck, skull base and cranial nerves: new and evolving sequences, analyses and clinical applications. Br J Radiol. 2019;92(1104):20190513.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Wu M, Jiang X, Qiu J, Fu X, Niu C. Gray and white matter abnormalities in primary trigeminal neuralgia with and without neurovascular compression. J Headache Pain. 2020;21(1):136.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wang Y, Cao DY, Remeniuk B, Krimmel S, Seminowicz DA, Zhang M. Altered brain structure and function associated with sensory and affective components of classic trigeminal neuralgia. Pain. 2017;158(8):1561–70.

    Article  PubMed  Google Scholar 

  21. Vaculik MF, Noorani A, Hung PS, Hodaie M. Selective hippocampal subfield volume reductions in classic trigeminal neuralgia. Neuroimage Clin. 2019;23:101911.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Tang Y, Wang M, Zheng T, Yuan F, Yang H, Han F, et al. Grey matter volume alterations in trigeminal neuralgia: a systematic review and meta-analysis of voxel-based morphometry studies. Prog Neuro-Psychopharmacol Biol Psychiatry. 2020;98:109821.

    Article  CAS  Google Scholar 

  23. Shen S, Zheng H, Wang J, Guo W, Guo X, Ji H, et al. Gray matter volume reduction with different disease duration in trigeminal neuralgia. Neuroradiology. 2021:1–11.

    Google Scholar 

  24. Parise M, Kubo TT, Doring TM, Tukamoto G, Vincent M, Gasparetto EL. Cuneus and fusiform cortices thickness is reduced in trigeminal neuralgia. J Headache Pain. 2014;15(1):17.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Obermann M, Rodriguez-Raecke R, Naegel S, Holle D, Mueller D, Yoon MS, et al. Gray matter volume reduction reflects chronic pain in trigeminal neuralgia. NeuroImage. 2013;74:352–8.

    Article  PubMed  Google Scholar 

  26. Mo J, Zhang J, Hu W, Luo F, Zhang K. Whole-brain morphological alterations associated with trigeminal neuralgia. J Headache Pain. 2021;22(1):95.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Li M, Yan J, Wen H, Lin J, Liang L, Li S, et al. Cortical thickness, gyrification and sulcal depth in trigeminal neuralgia. Sci Rep. 2021;11(1):16322.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Li M, Yan J, Li S, Wang T, Zhan W, Wen H, et al. Reduced volume of gray matter in patients with trigeminal neuralgia. Brain Imaging Behav. 2017;11(2):486–92.

    Article  PubMed  Google Scholar 

  29. Hung PS, Noorani A, Zhang JY, Tohyama S, Laperriere N, Davis KD, et al. Regional brain morphology predicts pain relief in trigeminal neuralgia. Neuroimage Clin. 2021;31:102706.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Henssen D, Dijk J, Knepflé R, Sieffers M, Winter A, Vissers K. Alterations in grey matter density and functional connectivity in trigeminal neuropathic pain and trigeminal neuralgia: a systematic review and meta-analysis. Neuroimage Clin. 2019;24:102039.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Danieli L, Riccitelli GC, Distefano D, Prodi E, Ventura E, Cianfoni A, et al. Brain tumor-enhancement visualization and morphometric assessment: a comparison of MPRAGE, SPACE, and VIBE MRI techniques. AJNR Am J Neuroradiol. 2019;40(7):1140–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Wetzel SG, Johnson G, Tan AG, Cha S, Knopp EA, Lee VS, et al. Three-dimensional, T1-weighted gradient-echo imaging of the brain with a volumetric interpolated examination. AJNR Am J Neuroradiol. 2002;23(6):995–1002.

    PubMed  PubMed Central  Google Scholar 

  33. Chung MS, Yim Y, Sung JK, Kim I, Nickel D, Chang M, et al. CS-VIBE accelerates cranial nerve MR imaging for the diagnosis of facial neuritis: comparison of the diagnostic performance of post-contrast MPRAGE and CS-VIBE. Eur Radiol. 2021;

    Google Scholar 

  34. Zhang Y, Mao Z, Pan L, Ling Z, Liu X, Zhang J, et al. Dysregulation of pain- and emotion-related networks in trigeminal neuralgia. Front Hum Neurosci. 2018;12:107.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. DeSouza DD, Hodaie M, Davis KD. Structural magnetic resonance imaging can identify trigeminal system abnormalities in classical trigeminal neuralgia. Front Neuroanat. 2016;10:95.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Albano L, Agosta F, Basaia S, Castellano A, Messina R, Parisi V, et al. Alterations of brain structural MRI are associated with outcome of surgical treatment in trigeminal neuralgia. Eur J Neurol. 2021.

    Google Scholar 

  37. Danyluk H, Lee EK, Wong S, Sajida S, Broad R, Wheatley M, et al. Hippocampal and trigeminal nerve volume predict outcome of surgical treatment for trigeminal neuralgia. Cephalalgia. 2020;40(6):586–96.

    Article  PubMed  Google Scholar 

  38. Devor M, Govrin-Lippmann R, Rappaport ZH. Mechanism of trigeminal neuralgia: an ultrastructural analysis of trigeminal root specimens obtained during microvascular decompression surgery. J Neurosurg. 2002;96(3):532–43.

    Article  PubMed  Google Scholar 

  39. Rappaport ZH, Govrin-Lippmann R, Devor M. An electron-microscopic analysis of biopsy samples of the trigeminal root taken during microvascular decompressive surgery. Stereotact Funct Neurosurg. 1997;68(1–4 Pt 1):182–6.

    Article  CAS  PubMed  Google Scholar 

  40. Fujiwara S, Sasaki M, Wada T, Kudo K, Hirooka R, Ishigaki D, et al. High-resolution diffusion tensor imaging for the detection of diffusion abnormalities in the trigeminal nerves of patients with trigeminal neuralgia caused by neurovascular compression. J Neuroimaging. 2011;21(2):e102–8.

    Article  PubMed  Google Scholar 

  41. Leal PRL, Roch JA, Hermier M, Souza MAN, Cristino-Filho G, Sindou M. Structural abnormalities of the trigeminal root revealed by diffusion tensor imaging in patients with trigeminal neuralgia caused by neurovascular compression: a prospective, double-blind, controlled study. Pain. 2011;152(10):2357–64.

    Article  PubMed  Google Scholar 

  42. Lin W, Chen YL, Zhang QW. Vascular compression of the trigeminal nerve in asymptomatic individuals: a voxel-wise analysis of axial and radial diffusivity. Acta Neurochir. 2014;156(3):577–80.

    Article  PubMed  Google Scholar 

  43. Lutz J, Linn J, Mehrkens JH, Thon N, Stahl R, Seelos K, et al. Trigeminal neuralgia due to neurovascular compression: high-spatial-resolution diffusion-tensor imaging reveals microstructural neural changes. Radiology. 2011;258(2):524–30.

    Article  PubMed  Google Scholar 

  44. Neetu S, Sunil K, Ashish A, Jayantee K, Usha KM. Microstructural abnormalities of the trigeminal nerve by diffusion-tensor imaging in trigeminal neuralgia without neurovascular compression. Neuroradiol J. 2016;29(1):13–8.

    Article  PubMed  Google Scholar 

  45. Chai W, You C, Zhang W, Peng W, Tan L, Guan Y, et al. Diffusion tensor imaging of microstructural alterations in the trigeminal nerve due to neurovascular contact/compression. Acta Neurochir. 2019;161(7):1407–13.

    Article  PubMed  Google Scholar 

  46. Liu J, Zhu J, Yuan F, Zhang X, Zhang Q. Abnormal brain white matter in patients with right trigeminal neuralgia: a diffusion tensor imaging study. J Headache Pain. 2018;19(1):46.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Zhang Y, Mao Z, Cui Z, Ling Z, Pan L, Liu X, et al. Diffusion tensor imaging of axonal and myelin changes in classical trigeminal neuralgia. World Neurosurg. 2018;112:e597–607.

    Article  PubMed  Google Scholar 

  48. DeSouza DD, Hodaie M, Davis KD. Abnormal trigeminal nerve microstructure and brain white matter in idiopathic trigeminal neuralgia. Pain. 2014;155(1):37–44.

    Article  PubMed  Google Scholar 

  49. Li R, Chang N, Liu Y, Zhang Y, Luo Y, Zhang T, et al. The integrity of the substructure of the corpus callosum in patients with right classic trigeminal neuralgia. J Craniofac Surg. 2021;32(2):632–6.

    Article  CAS  PubMed  Google Scholar 

  50. Wang Y, Zhang Y, Zhang J, Wang J, Xu J, Li J, et al. Structural and functional abnormalities of the insular cortex in trigeminal neuralgia: a multimodal magnetic resonance imaging analysis. Pain. 2018;159(3):507–14.

    Article  PubMed  Google Scholar 

  51. Hung PS, Chen DQ, Davis KD, Zhong J, Hodaie M. Predicting pain relief: use of pre-surgical trigeminal nerve diffusion metrics in trigeminal neuralgia. Neuroimage Clin. 2017;15:710–8.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Chen ST, Yang JT, Weng HH, Wang HL, Yeh MY, Tsai YH. Diffusion tensor imaging for assessment of microstructural changes associate with treatment outcome at one-year after radiofrequency Rhizotomy in trigeminal neuralgia. BMC Neurol. 2019;19(1):62.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Leal PRL, Roch J, Hermier M, Berthezene Y, Sindou M. Diffusion tensor imaging abnormalities of the trigeminal nerve root in patients with classical trigeminal neuralgia: a pre- and postoperative comparative study 4 years after microvascular decompression. Acta Neurochir. 2019;161(7):1415–25.

    Article  PubMed  Google Scholar 

  54. Lee YL, Chen ST, Yang JT, Weng HH, Wang HL, Tsai YH. Diffusivity parameters of diffusion tensor imaging and apparent diffusion coefficient as imaging markers for predicting the treatment response of patients with trigeminal neuralgia. J Neurosurg. 2019;132(6):1993–9.

    Article  PubMed  Google Scholar 

  55. Pikis S, Bunevicius A, Donahue J, Lavezzo K, Patterson G, Xu Z, et al. Diffusivity metrics three months after upfront gamma knife radiosurgery for trigeminal neuralgia may be correlated with pain relief. World Neurosurg. 2021;153:e220–e5.

    Article  PubMed  Google Scholar 

  56. Tohyama S, Hung PS, Zhong J, Hodaie M. Early postsurgical diffusivity metrics for prognostication of long-term pain relief after gamma knife radiosurgery for trigeminal neuralgia. J Neurosurg. 2018;131(2):539–48.

    Article  PubMed  Google Scholar 

  57. Wu M, Qiu J, Jiang X, Li M, Wang SD, Dong Q, et al. Diffusion tensor imaging reveals microstructural alteration of the trigeminal nerve root in classical trigeminal neuralgia without neurovascular compression and correlation with outcome after internal neurolysis. Magn Reson Imaging. 2020;71:37–44.

    Article  PubMed  Google Scholar 

  58. Azeez AK, Biswal BB. A review of resting-state analysis methods. Neuroimaging Clin N Am. 2017;27(4):581–92.

    Article  PubMed  Google Scholar 

  59. Tian T, Guo L, Xu J, Zhang S, Shi J, Liu C, et al. Brain white matter plasticity and functional reorganization underlying the central pathogenesis of trigeminal neuralgia. Sci Rep. 2016;6:36030.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Wang Y, Xu C, Zhai L, Lu X, Wu X, Yi Y, et al. Spatial-temporal signature of resting-state BOLD signals in classic trigeminal neuralgia. J Pain Res. 2017;10:2741–50.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Wang Y, Zhang X, Guan Q, Wan L, Yi Y, Liu CF. Altered regional homogeneity of spontaneous brain activity in idiopathic trigeminal neuralgia. Neuropsychiatr Dis Treat. 2015;11:2659–66.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Xiang CQ, Liu WF, Xu QH, Su T, Yong-Qiang S, Min YL, et al. Altered spontaneous brain activity in patients with classical trigeminal neuralgia using regional homogeneity: a resting-state functional MRI study. Pain Pract. 2019;19(4):397–406.

    Article  PubMed  Google Scholar 

  63. Yan J, Li M, Fu S, Li G, Wang T, Yin Y, et al. Alterations of dynamic regional homogeneity in trigeminal neuralgia: a resting-state fMRI study. Front Neurol. 2019;10:1083.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Yuan J, Cao S, Huang Y, Zhang Y, Xie P, Zhang Y, et al. Altered spontaneous brain activity in patients with idiopathic trigeminal neuralgia: a resting-state functional MRI study. Clin J Pain. 2018;34(7):600–9.

    Article  PubMed  Google Scholar 

  65. Zhang Y, Mao Z, Pan L, Ling Z, Liu X, Zhang J, et al. Frequency-specific alterations in cortical rhythms and functional connectivity in trigeminal neuralgia. Brain Imaging Behav. 2019;13(6):1497–509.

    Article  PubMed  Google Scholar 

  66. Zhu PW, Chen Y, Gong YX, Jiang N, Liu WF, Su T, et al. Altered brain network centrality in patients with trigeminal neuralgia: a resting-state fMRI study. Acta Radiol. 2020;61(1):67–75.

    Article  PubMed  Google Scholar 

  67. Arrighi-Allisan AE, Delman BN, Rutland JW, Yao A, Alper J, Huang KH, et al. Neuroanatomical determinants of secondary trigeminal neuralgia: application of 7T ultra-high-field multimodal magnetic resonance imaging. World Neurosurg. 2020;137:e34–42.

    Article  PubMed  Google Scholar 

  68. Moon HC, You ST, Baek HM, Jeon YJ, Park CA, Cheong JJ, et al. 7.0 tesla MRI tractography in patients with trigeminal neuralgia. Magn Reson Imaging. 2018;54:265–70.

    Article  PubMed  Google Scholar 

  69. Moon HC, Park CA, Jeon YJ, You ST, Baek HM, Lee YJ, et al. 7 tesla magnetic resonance imaging of caudal anterior cingulate and posterior cingulate cortex atrophy in patients with trigeminal neuralgia. Magn Reson Imaging. 2018;51:144–50.

    Article  PubMed  Google Scholar 

  70. Rutland JW, Huang KH, Gill CM, Villavisanis DF, Alper J, Verma G, et al. First application of 7-T ultra-high field diffusion tensor imaging to detect altered microstructure of thalamic-somatosensory anatomy in trigeminal neuralgia. J Neurosurg. 2019:1–9.

    Google Scholar 

  71. Lee YJ, Moon HC, Tak S, Cheong C, Park YS. Atrophic changes and diffusion abnormalities of affected trigeminal nerves in trigeminal neuralgia using 7-T MRI. Stereotact Funct Neurosurg. 2019;97(3):169–75.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jin Woo Choi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Choi, J.W., Kang, C.H. (2023). Recent Advances and Future Perspective in MRI Technique for the Trigeminal Neuralgia. In: Park, K., Cho, K.R. (eds) Trigeminal Neuralgia. Springer, Singapore. https://doi.org/10.1007/978-981-19-9171-4_7

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-9171-4_7

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-9170-7

  • Online ISBN: 978-981-19-9171-4

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics