State-of-the-Art MRI Techniques for Multiple Sclerosis

Authors: M. Keřkovský 1;  J. Stulík 1;  I. Obhlídalová 2;  P. Praksová 2;  J. Bednařík 2;  M. Dostál 1,3;  M. Kuhn 4–6;  A. Šprláková-Puková 1;  M. Mechl 1
Authors‘ workplace: Klinika radiologie a nukleární medicíny LF MU a FN Brno 1;  Neurologická klinika LF MU a FN Brno 2;  Biofyzikální ústav, LF MU a FN Brno 3;  Psychiatrická klinika LF MU a FN Brno 4;  Institut biostatistiky a analýz, LF MU, Brno 5;  Behaviorální a sociální neurovědy, CEITEC – Středoevropský technologický institut MU 6
Published in: Cesk Slov Neurol N 2017; 80(6): 647-657
Category: Review Article
doi: 10.14735/amcsnn2017647


Magnetic resonance imaging (MRI) is currently a key component of multiple sclerosis diagnostics. In addition to conventional techniques, based on the evaluat ion of the number and localization of visible brain and spinal cord lesions, in recent years we have seen a rapid development of new MRI techniques providing new quantitative biomarkers which better characterize pathological structural changes in central nervous system tissues occurring due to a demyelinating disease. This article summarizes new trends in MRI diagnostics of multiple sclerosis in terms of the technical foundations of different methods, possibilities for data analysis and their practical use.

Key words:
multiple sclerosis – magnetic resonance imaging – neuroimaging – diffusion tensor imaging – proton magnetic resonance spectroscopy

The authors declare they have no potential conflicts of interest concerning drugs, products, or services used in the study.

The Editorial Board declares that the manuscript met the ICMJE “uniform requirements” for biomedical papers.

Supported by Czech health research council of the Ministry of Health of the Czech Republic (NV15-32133A) and by funds from the Faculty of Medicine MU to junior researcher (M. Keřkovský).


1. Confavreux C, Vukusic S, Moreau T, et al. Relapses and progres sion of disability in multiple sclerosis. N Engl J Med 2000;343(20):1430– 8. doi: 10.1056/ NEJM2000 11163432001.

2. Calabrese M, Favaretto A, Martini V, et al. Grey matter lesions in MS: from histology to clinical implications. Prion 2013;7(1):20– 7. doi: 10.4161/ pri.22580.

3. Filippi M, Agosta F.Imag ing biomarkers in multiple sclerosis. J Magn Reson Imag ing 2010;31(4):770– 88. doi: 10.1002/ jmri.22102.

4. van Waesberghe JH, Kamphorst W, De Groot CJ, et al. Axonal loss in multiple sclerosis lesions: magnetic resonance imaging insights into substrates of disability. Ann Neurol 1999;46(5):747– 54.

5. Bakshi R, Thompson AJ, Rocca MA, et al. MRI in multiple sclerosis: current status and future prospects. Lancet Neurol 2008;7(7):615– 25. doi: 10.1016/ S1474- 4422(08)70137-6.

6. Miller DH, Chard DT, Ciccarelli O. Clinically isolated syndromes. Lancet Neurol 2012;11(2):157– 69. doi: 10.1016/ S1474-4422(11)70274-5.

7. Filippi M, Rocca MA, Ciccarelli O, et al. MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. Lancet Neurol 2016;15(3):292– 303. doi: 10.1016/ S1474-4422(15)00393-2.

8. Nayak NB, Salah R, Huang JC, et al. A comparison of sagittal short T1 inversion recovery and T2-weighted FSE sequences for detection of multiple sclerosis spinal cord lesions. Acta Neurol Scand 014;129(3):198-203. doi: 10.1111/ ane.12168.

9. Patzig M, Burke M, Brückmann H, et al. Comparison of 3D cube FLAIR with 2D FLAIR for multiple sclerosis imaging at 3 Tesla. Rofo 2014;186(5):484– 8. doi:10.1055/ s-0033-1355896.

10. Redpath TW, Smith FW. Technical note: use of a double inversion recovery pulse sequence to image selectively grey or white brain matter. Br J Radiol 1994;67(804):1258– 63. doi: 10.1259/ 0007-1285-67-804- 1258.

11. Wattjes MP, Lutterbey GG, Gieseke J, et al. Double inversion recovery brain imaging at 3T: diagnostic value in the detection of multiple sclerosis lesions. Am J Neuroradiol 2007;28(1):54– 9.

12. Geurts JJ, Pouwels PJ, Uitdehaag BM, et al. Intracortical lesions in multiple sclerosis: improved detection with 3D double inversion-recovery MR imaging. Radiology 2005;236(1):254– 60. doi: 10.1148/ radiol.2361040 450.

13. Bø L, Vedeler CA, Nyland HI, et al. Subpial demyelination in the cerebral cortex of multiple sclerosis patients. J Neuropathol Exp Neurol 2003;62(7):723– 32.

14. Calabrese M, De Stefano N, Atzori M, et al. Detection of cortical inflammatory lesions by double inversion recovery magnetic resonance imaging in patients with multiple sclerosis. Arch Neurol 2007;64(10):1416– 22. doi:

10.1001/ archneur.64.10.1416.

15. Calabrese M, Gallo P. Magnetic resonance evidence of cortical onset of multiple sclerosis. Mult Scler 2009;15(8):933– 41. doi: 10.1177/ 1352458509106510.

16. Rinaldi F, Calabrese M, Gros si P, et al. Cortical lesions and cognitive impairment in multiple sclerosis. Neurol Sci 2010;31(Suppl 2):S235– 7. doi: 10.1007/ s10072-010-0368-4.

17. Liu C, Li W, Tong KA, et al. Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain. J Magn Reson Imaging 2015;42(1):23– 41. doi: 10.1002/ jmri.24768.

18. Schenck JF. The role of magnetic susceptibility in magnetic resonance imaging: MRI magnetic compatibility of the first and second kinds. Med Phys 1996;23(6):815– 50. doi: 10.1118/ 1.597854.

19. Haacke EM, Mittal S, Wu Z, et al. Susceptibility-weight ed imaging: technical aspects and clinical applications, part 1. Am J Neuroradiol 2009;30(1):19– 30. doi: 10.3174/ ajnr.A1400.

20. Fog T. On the ves sel-plaque relationships in the brain in multiple sclerosis. Acta Neurol Scand Suppl 1964;40(Suppl 10):9– 15.

21. Mistry N, Dixon J, Tal lantyre E, et al. Central veins in brain lesions visualized with high-field magnetic resonance imaging: a pathologically specific diagnostic biomarker for inflammatory demyelination in the brain. JAMA Neurol 2013;70(5):623– 8. doi: 10.1001/ jamaneurol. 2013.1405.

22. Tal lantyre EC, Dixon JE, Donaldson I, et al. Ultra-high-field imaging distinguishes MS lesions from asymptomatic white matter lesions. Neurology 2011;76(6):534– 9. doi: 10.1212/ WNL.0b013e31820b7630.

23. Kilsdonk ID, Wattjes MP, Lopez-Soriano A, et al. Improved differentiation between MS and vascular brain lesions us ing FLAIR* at 7 Tesla. Eur Radiol 2014;24(4):841– 9. doi: 10.1007/ s00330-013-3080-y.

24. Enzinger C, Barkhof F, Ciccarel li O, et al. Nonconventional MRI and microstructural cerebral changes in multiple sclerosis. Nat Rev Neurol 2015;11(12):676– 86. doi:10.1038/ nrneurol.2015.194.

25. Tallantyre EC, Morgan PS, Dixon JE, et al. A comparison of 3T and 7T in the detection of small parenchymal veins within MS lesions. Invest Radiol 2009;44(9):491– 4. doi: 10.1097/ RLI.0b013e3181b4c144.

26. Kel ly JE, Mar S, D‘Angelo G, et al. Susceptibility-weight ed imaging helps to discriminate pediatric multiple sclerosis from acute disseminated encephalomyelitis. Pediatr Neurol 2015;52(1):36– 41. doi: 10.1016/ j.pediatrneurol. 2014.10.014.

27. Absinta M, Sati P, Gaitán MI, et al. Seven-tesla phase imaging of acute multiple sclerosis lesions: a new window into the inflammatory process. Ann Neurol 2013;74(5):669– 78. doi: 10.1002/ ana.23959.

28. Wetter NC, Hubbard EA, Motl RW, et al. Fully automated open-source lesion mapping of T2-FLAIR images with FSL correlates with clinical disability in MS. Brain Behav 2016;6(3):e00440. doi: 10.1002/ brb3.440.

29. Odenthal C, Coulthard A. The prognostic utility of MRI in clinically isolated syndrome: a literature review. AJNR Am J Neuroradiol 2015;36(3):425– 31. doi: 10.3174/ ajnr.A3954.

30. Barkhof F. The clinico-radiological paradox in multiple sclerosis revisited. Curr Opin Neurol 2002;15(3):239– 45.

31. Rudick RA, Lee JC, Simon J, et al. Significance of T2 lesions in multiple sclerosis: A 13-year longitudinal study. Ann Neurol 2006;60(2):236– 42. doi: 10.1002/ ana.20883.

32. Minneboo A, Jasperse B, Barkhof F, et al. Predicting short-term disability progression in early multiple sclerosis: added value of MRI parameters. J Neurol Neurosurg Psychiatry 2008;79(8):917– 23. doi: 10.1136/ jn np. 2007.124123.

33. Gauthier SA, Mandel M, Guttmann CR, et al. Predicting short-term disability in multiple sclerosis. Neurology 2007;68(24):2059– 65. doi: 10.1212/ 01. wnl.0000264890.97479.b1.

34. De Stefano N, Arnold DL. Towards a better understand ing of pseudoatrophy in the brain of multiple sclerosis patients. Mult Scler 2015;21(6):675– 6. doi: 10.1177/ 1352458514564494.

35. Filippi M, Rocca MA, Barkhof F, et al. As sociation between pathological and MRI findings in multiple sclerosis. Lancet Neurol 2012;11(4):349– 60. doi: 10.1016/ S1474-4422(12)70003-0.

36. Dastidar P, Heinonen T, Lehtimäki T, et al. Volumes of brain atrophy and plaques correlated with neurological disability in secondary progressive multiple sclerosis. J Neurol Sci 1999;165(1):36– 42.

37. Vaněčková M, Seidl Z, Krásenský J, et al. Naše zkušenosti s MR monitorováním pacientů s roztroušenou sklerózou v klinické praxi. Cesk Slov Neurol N 2010;73/ 106(4):716– 20.

38. Zivadinov R, Stosic M, Cox JL, et al. The place of conventional MRI and newly emerging MRI techniques in monitoring different aspects of treatment outcome. J Neurol 2008;255(Suppl 1):61– 74. doi: 10.1007/ s00415-


39. Miller DH, Soon D, Fernando KT, et al. MRI outcomes in a placebo-control led trial of natalizumab in relapsing MS. Neurology 2007;68(17):1390– 401. doi: 10.1212/ 01. wnl.0000260064.77700.fd.

40. Koudriavtseva T, Mainero C. Brain Atrophy as a Measure of Neuroprotective Drug Effects in Multiple Sclerosis: Influence of Inflammation. Front Hum Neurosci 2016;10:226. doi: 10.3389/ fnhum.2016.00226.

41. Henry RG, Shieh M, Okuda DT, et al. Regional grey matter atrophy in clinically isolated syndromes at presentation. J Neurol Neurosurg Psychiatry 2008;79(11):1236– 44. doi: 10.1136/ jn np.2007.134825.

42. Kalincik T, Vaneckova M, Tyblova M, et al. Volumetric MRI markers and predictors of disease activity in early multiple sclerosis: a longitudinal cohort study. PLoS One 2012;7(11):e50101. doi: 10.1371/ journal.pone.0050 101.

43. Kaunzner UW, Gauthier SA. MRI in the assessment and monitoring of multiple sclerosis: an update on best practice. Ther Adv Neurol Disord 2017;10(6):247– 61. doi: 10.1177/ 1756285617708911.

44. Vaneckova M, Kalincik T, Krasensky J, et al. Corpus callosum atrophy – a simple predictor of multiple sclerosis progression: a longitudinal 9-year study. Eur Neurol 2012;68(1):23– 7. doi: 10.1159/ 000337683.

45. Mechelli A, Price CJ, Friston KJ, et al. Voxel-based morphometry of the human brain: Methods and applications. Current Medical Imaging Reviews 2005;1(2):105– 13.

46. Smith SM, Zhang Y, Jenkinson M, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 2002;17(1): 479– 89.

47. Mukherjee P, Berman JI, Chung SW, et al. Diffusion tensor MR imaging and fiber tractography: theoretic underpinnings. Am J Neuroradiol 2008;29(4):632– 41. doi: 10.3174/ ajnr.A1051.

48. Christiansen P, Gideon P, Thomsen C, et al. Increased water self-diffusion in chronic plaques and in apparently normal white matter in patients with multiple sclerosis. Acta Neurol Scand 1993;87(3):195– 9.

49. Yurtsever I, Hakyemez B, Taskapilioglu O, et al. The contribution of diffusion-weighted MR imaging in multiple sclerosis during acute attack. Eur J Radiol 2008;65(3):421– 6. doi: 10.1016/ j.ejrad.2007.05.002.

50. Eisele P, Szabo K, Griebe M, et al. Reduced diffusion in a subset of acute MS lesions: a serial multiparametric MRI study. AJNR Am J Neuroradiol 2012;33(7):1369– 73. doi: 10.3174/ ajnr.A2975.

51. Vaněčková M, Seidl Z, Čáp F, et al. Návrh bezpečnostní MR monitorace u pacientů s roztroušenou sklerózou léčených natalizumabem. Cesk Slov Neurol N 2016;79(6):663– 9.

52. Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J 1994;66:259– 67. doi: 10.1016/ S0006-3495(94)80775-1.

53. Assaf Y, Pasternak O. Diffusion tensor imaging (DTI)- based white matter mapping in brain research: a review. J Mol Neurosci 2008;34(1):51– 61. doi: 10.1007/ s12031-007-0029-0.

54. Mottershead JP, Schmierer K, Clemence M, et al.High field MRI correlates of myelin content and axonal density in multiple sclerosis – a post-mortem study of the spinal cord. J Neurol 2003;250(11):1293– 301. doi: 10.1007/ s00415-003-0192-3.

55. Filippi M, Iannucci G, Cercignani M, et al. A quantitative study of water diffusion in multiple sclerosis lesions and normal-appear ing white matter using echo-planar imaging. Arch Neurol 2000;57(7):1017– 21.

56. Yu CS, Lin FC, Liu Y, et al. Histogram analysis of diffusion measures in clinically isolated syndromes and relapsing-remitting multiple sclerosis. Eur J Radiol 2008;68(2):328– 34. doi: 10.1016/ j.ejrad.2007.08.036.

57. Hubbard EA, Wetter NC, Sutton BP, et al. Diffusion tensor imaging of the corticospinal tract and walking performance in multiple sclerosis. J Neurol Sci 2016;363:225– 31. doi: 10.1016/ j.jns.2016.02.044.

58. Meijer KA, Muhlert N, Cercignani M, et al. White matter tract abnormalities are associated with cognitive dysfunction in secondary progressive multiple sclerosis. Mult Scler 2016;22(11):1429– 37. doi: 10.1177/ 1352458515622694.

59. Valsasina P, Rocca MA, Agosta F, et al. Mean diffusivity and fractional anisotropy histogram analysis of the cervical cord in MS patients. Neuroimage 2005;26(3):822– 8. doi: 10.1016/ j.neuroimage.2005.02.033.

60. Hesseltine SM, Law M, Babb J, et al. Diffusion tensor imaging in multiple sclerosis: assessment of regional differences in the axial plane within normal-appearing cervical spinal cord. Am J Neuroradiol 2006;27(6):1189– 93.

61. Smith SM, Jenkinson M, Johansen-Berg H, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 2006;31(4):1487– 505. doi: 10.1016/ j.neuroimage.2006.02.024.

62. De Leener B, Taso M, Cohen-Adad J, et al. Segmentation of the human spinal cord. MAGMA 2016;29(2):125– 53. doi: 10.1007/ s10334-015-0507-2.

63. Kivrak AS, Paksoy Y, Erol C, et al. Comparison of apparent diffusion coefficient values among different MRI platforms: a multicenter phantom study. Diagn Interv Radiol 2013;19(6):433– 7. doi: 10.5152/ dir. 2013.13034.

64. Santarelli X, Garbin G, Ukmar M, et al. Dependence of the fractional anisotropy in cervical spine from the number of diffusion gradients, repeated acquisition and voxel size. Magn Reson Imaging 2010;28(1):70– 6. doi: 10.1016/ j.mri.2009.05.046.

65. Henkelman RM, Stanisz GJ, Graham SJ. Magnetization transfer in MRI: a review. NMR Biomed 2001;14(2): 57– 64.

66. Schmierer K, Scaravilli F, Altmann DR, et al. Magnetization transferratio and myelin in postmortem multiple sclerosis brain. Ann Neurol 2004;56(3):407– 15. doi: 10.1002/ ana.20202.

67. Ropele S, Fazekas F. Magnetization transfer MR imaging in multiple sclerosis. Neuroimaging Clin N Am 2009;19(1):27– 36. doi: 10.1016/ j.nic.2008.09. 004.

68. Filippi M, Rocca MA. Magnetization transfer magnetic resonance imaging of the brain, spinal cord, and optic nerve. Neurotherapeutics 2007;4(3):401– 13. doi: 10.1016/ j.nurt.2007.03.002.

69. Hayton T, Furby J, Smith KJ, et al. Grey matter magnetization transferratio independently correlates with neurological deficit in secondary progressive multiple sclerosis. J Neurol 2009;256(3):427– 35. doi: 10.1007/ 00415-009-0110-4.

70. Bertholdo D, Watcharakorn A, Castillo M. Brain proton magnetic resonance spectroscopy: introduction and overview. Neuroimaging Clin N Am 2013;23(3):359– 80. doi: 10.1016/ j.nic.2012.10.002.

71. Aboul-Enein F. MR Spectroscopy in Multiple Sclerosis – A New Piece of the Puzzle or Just a New Puzzle In: Kim DH et al, eds. Magnetic Resonance Spectroscopy. Rijeka (Croatia): InTech 2012:48– 72.

72. Ge Y. Multiple sclerosis: the role of MR imaging. AJNR Am J Neuroradiol 2006;27(6):1165– 76.

73. Llufriu S, Kornak J, Ratiney H, et al. Magnetic resonance spectroscopy markers of disease progression in multiple sclerosis. JAMA Neurol 2014;71(7):840– 7. doi: 10.1001/ jamaneurol.2014.895.

Paediatric neurology Neurosurgery Neurology

Article was published in

Czech and Slovak Neurology and Neurosurgery

Issue 6

2017 Issue 6

Most read in this issue
Forgotten password

Don‘t have an account?  Create new account

Forgotten password

Enter the email address that you registered with. We will send you instructions on how to set a new password.


Don‘t have an account?  Create new account