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Relationships between brain water content and diffusion tensor imaging parameters (apparent diffusion coefficient and fractional anisotropy) in multiple sclerosis

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Abstract

Fifteen multiple sclerosis patients were examined by diffusion tensor imaging (DTI) to determine fractional anisotropy (FA) and apparent diffusion coefficient (ADC) in a superventricular volume of interest of 8×8×2 cm3 containing gray matter (GM) and white matter (WM) tissue. Point resolved spectroscopy 2D-chemical shift imaging of the same volume was performed without water suppression. The water contents and DTI parameters in 64 voxels of 2 cm3 were compared. The water content was increased in patients compared with controls (GM: 244±21 vs. 194±10 a.u.; WM: 245±32 vs. 190±11 a.u.), FA decreased (GM: 0.226±0.038 vs. 0.270±0.020; WM: 0.337±0.044 vs. 0.402±0.011) and ADC increased [GM: 1134±203 vs. 899±28 (×10−6 mm2/s); WM: 901±138 vs. 751±17 (×10−6 mm2/s)]. Correlations of water content with FA and ADC in WM were strong (r=−0.68, P<0.02; r=0.75; P<0.01, respectively); those in GM were weaker (r=−0.50, P<0.05; r=0.45, P<0.1, respectively). Likewise, FA and ADC were more strongly correlated in WM (r=−0.88; P<0.00001) than in GM (r=−0.69, P<0.01). The demonstrated relationship between DTI parameters and water content in multiple sclerosis patients suggests a potential for therapy monitoring in normal-appearing brain tissue.

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References

  1. Basser PJ, Mattiello J, Le Bihan D (1994) Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103:247–254

    PubMed  CAS  Google Scholar 

  2. Mori S, van Zijl PCM (2002) Fibre tracking: principles and strategies-a technical review. NMR Biomed 15:468–480

    PubMed  Google Scholar 

  3. Terajima K, Nakada T (2002) EZ-tracing: a new ready-to-use algorithm for magnetic resonance tractography. J Neurosci Methods 116:147–155

    PubMed  Google Scholar 

  4. Eichler FS, Itoh R, Barker PB, Mori S, Garrett ES, van Zijl PC, Moser HW, Raymond GV, Melhem ER (2002) Proton MR spectroscopic and diffusion tensor brain MR imaging in X-linked adrenoleukodystrophy: initial experience. Radiology 225:245–252

    PubMed  Google Scholar 

  5. Clark C, Werring D, Miller D (2000) Diffusion imaging of the spinal cord in vivo: estimation of the principal diffusion and application to multiple sclerosis. Magn Reson Med 43:133–138

    PubMed  CAS  Google Scholar 

  6. De Stefano N, Iannucci G, Sormani MP, Guidi L, Bartelozzi ML, Comi G, Federico A, Filippi M (2002) MR correlates of cerebral atrophy in patients with multiple sclerosis. J Neurol 249:1072–1077

    PubMed  Google Scholar 

  7. Ciccarelli O, Werring DJ, Barker GJ, Griffin CM, Wheeler-Kingshott CA, Miller DH, Thompson AJ (2003) A study of the mechanisms of normal-appearing white matter damage in multiple sclerosis using diffusion tensor imaging-evidence of Wallerian degeneration. J Neurol 250:287–292

    PubMed  Google Scholar 

  8. Oh J, Henry RG, Genain C, Nelson SJ, Pelletier D (2004) Mechanisms of normal appearing injury related to pericallosal T1 lesions in multiple sclerosis using directional diffusion tensor and H-1 MRS imaging. J Neurol Neurosurg Psychiatry 75:1281–1286

    PubMed  CAS  Google Scholar 

  9. Sijens PE, Irwan R, Potze JH, Mostert JP, De Keyser J, Oudkerk M (2005) Analysis of the human brain in primary progressive multiple sclerosis with mapping of the spatial distributions using 1H MR spectroscopy and diffusion tensor imaging. Eur Radiol. Aug;15(8):1686–1693. Epub2005 Apr22. PMID: 15846494 [PubMed-in process]

    PubMed  Google Scholar 

  10. Laule C, Vavasour IM, Moore GRW, Oger J, Li DKB, Paty DW, MacKay AL (2004) Water content and myelin water fraction in multiple sclerosis; a T1 relaxation study. J Neurol 251: 284–293

    PubMed  CAS  Google Scholar 

  11. Tourtellotte W, Parker J (1968) Some spaces and barriers in postmortem multiple sclerosis. Prog Brain Res 29:493–525

    Article  PubMed  CAS  Google Scholar 

  12. Sappey-Marinier D (1990) High-resolution NMR spectroscopy of cerebral white matter in multiple sclerosis. Magn Reson Med 15:229–239

    PubMed  CAS  Google Scholar 

  13. McDonald WI, Compston A, Edan G, Goodkin D, Hartung H-P, Lublin FD, McFarland HF, Paty DW, Polman CH, Reingold SC, Sandberg-Wollheim M, Sibley W, Thompson A, Van Den Noort S, Weinshenker BY, Wolinsky JS (2001) Recommended diagnostic criteria for multiple sclerosis: guidelines from the international panel on the diagnosis of multiple sclerosis. Ann Neurol 50:121–127

    PubMed  CAS  Google Scholar 

  14. Miller DH, Barkhof F, Frank JA, Parker GJM, Thompson AJ (2002) Measurement of atrophy in multiple sclerosis; pathological basis, methodological aspects and clinical relevance. Brain 125:1676–1695

    PubMed  Google Scholar 

  15. Sijens PE, Mostert JP, Oudkerk M, De Keyser J (2005) 1H MR Spectroscopy of the brain in multiple sclerosis subtypes with analysis of the metabolite concentrations in gray and white matter: initial findings. Eur Radiol Jul 19; [Epub ahead of print] PMID: 16028056 [PubMed-as supplied by publisher]

  16. Irwan R, Sijens PE, Potze JH, Oudkerk M (2005) Correlation of MR spectroscopy and diffusion tensor imaging. Magn Reson Imaging 23:851–858

    PubMed  Google Scholar 

  17. Irwan R, Sijens PE, Kappert P, Oudkerk M. Phase correction for eight-channel head coil in MR spectroscopy. In: Proceedings ISMRM Twelfth Scientific Meeting and Exhibition 2004

  18. Sijens PE, van den Bent MJ, Nowak PJCM, van Dijk P, Oudkerk M (1997) Chemical shift imaging reveals loss of brain tumor choline signal after administration of Gd-contrast agent. Magn Reson Med 37:222–225

    PubMed  CAS  Google Scholar 

  19. Spiegel MR (1961) Theory and problems of statistics. Schaum's Outline Series. McGraw-Hill, New York, 359 pp. ISBN: 0070602344

    Google Scholar 

  20. Papanikolaou N, Papadiki E, Karampekios S, Spilioti M, Maris T, Prassopoulos P, Gourtsoyiannis N (2004) T2 relaxation time analysis in patients with multiple sclerosis: correlation with magnetization transfer ratio. Eur Radiol 4:115–122

    Google Scholar 

  21. Helms G, Piringer A (2001) Magntization transfer of water T2 relaxation components in human brain: implications for T2 based segmentation of spectroscopic volumes. Magn Reson Imaging 19:803–811

    PubMed  CAS  Google Scholar 

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Sijens, P.E., Irwan, R., Potze, J.H. et al. Relationships between brain water content and diffusion tensor imaging parameters (apparent diffusion coefficient and fractional anisotropy) in multiple sclerosis. Eur Radiol 16, 898–904 (2006). https://doi.org/10.1007/s00330-005-0033-0

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