White Matter Abnormalities in Multiple Sclerosis Evaluated by Quantitative Synthetic MRI, Diffusion Tensor Imaging, and Neurite Orientation Dispersion and Density Imaging

References

1. 1.↵
   2. Vavasour IM,
   3. Huijskens SC,
   4. Li DK, et al

   . Global loss of myelin water over 5 years in multiple sclerosis normal-appearing white matter. Mult Scler 2018;24:1557–68 doi:10.1177/1352458517723717 pmid:28782447

   CrossRefPubMed
2. 2.↵
   2. Hagiwara A,
   3. Hori M,
   4. Yokoyama K, et al

   . Utility of a multiparametric quantitative MRI model that assesses myelin and edema for evaluating plaques, periplaque white matter, and normal-appearing white matter in patients with multiple sclerosis: a feasibility study. AJNR Am J Neuroradiol 2017;38:237–42 doi:10.3174/ajnr.A4977 pmid:27789453

   Abstract/FREE Full Text
3. 3.↵
   2. Hagiwara A,
   3. Hori M,
   4. Yokoyama K, et al

   . Analysis of white matter damage in patients with multiple sclerosis via a novel in vivo MR method for measuring myelin, axons, and g-ratio. AJNR Am J Neuroradiol 2017;38:1934–40 doi:10.3174/ajnr.A5312 pmid:28775058

   Abstract/FREE Full Text
4. 4.↵
   2. Guo AC,
   3. MacFall JR,
   4. Provenzale JM

   . Multiple sclerosis: diffusion tensor MR imaging for evaluation of normal-appearing white matter. Radiology 2002;222:729–36 doi:10.1148/radiol.2223010311 pmid:11867792

   CrossRefPubMedWeb of Science
5. 5.↵
   2. Granberg T,
   3. Fan Q,
   4. Treaba CA, et al

   . In vivo characterization of cortical and white matter neuroaxonal pathology in early multiple sclerosis. Brain 2017;140:2912–26 doi:10.1093/brain/awx247 pmid:29053798

   CrossRefPubMed
6. 6.↵
   2. Warntjes JB,
   3. Leinhard OD,
   4. West J, et al

   . Rapid magnetic resonance quantification on the brain: optimization for clinical usage. Magn Reson Med 2008;60:320–29 doi:10.1002/mrm.21635 pmid:18666127

   CrossRefPubMed
7. 7.↵
   2. Hagiwara A,
   3. Hori M,
   4. Cohen-Adad J, et al

   . Linearity, bias, intrascanner repeatability, and interscanner reproducibility of quantitative multidynamic multiecho sequence for rapid simultaneous relaxometry at 3 T: a validation study with a standardized phantom and healthy controls. Invest Radiology 2019;54:47–39 doi:10.1097/RLI.0000000000000510 pmid:30300164

   CrossRefPubMed
8. 8.↵
   2. Wallaert L,
   3. Hagiwara A,
   4. Andica C, et al

   . The advantage of synthetic MRI for the visualization of anterior temporal pole lesions on double inversion recovery (DIR), phase-sensitive inversion recovery (PSIR), and myelin images in a patient with CADASIL. Magn Reson Med Sci 2018;17:275–76 doi:10.2463/mrms.ci.2017-0110 pmid:29238005

   CrossRefPubMed
9. 9.↵
   2. Warntjes M,
   3. Engstrom M,
   4. Tisell A, et al

   . Modeling the presence of myelin and edema in the brain based on multi-parametric quantitative MRI. Front Neurol 2016;7:16 doi:10.3389/fneur.2016.00016 pmid:26925030

   CrossRefPubMed
10. 10.↵
    2. Warntjes JBM,
    3. Persson A,
    4. Berge J, et al

    . Myelin detection using rapid quantitative MR imaging correlated to macroscopically registered luxol fast blue-stained brain specimens. AJNR Am J Neuroradiol 2017;38:1096–102 doi:10.3174/ajnr.A5168 pmid:28428209

    Abstract/FREE Full Text
11. 11.↵
    2. Ouellette R,
    3. Warntjes M,
    4. Forslin Y, et al

    . Rapid estimation of myelin for diagnostic imaging (REMyDI): a clinical and histopathological validation in multiple sclerosis. In: Proceedings of the Annual Meeting of the International Society for Magnetic Resonance in Medicine and the European Society for Magnetic Resonance in Medicine, Paris, France, June 16-21, 2018;Abstract 1864
12. 12.↵
    2. Hagiwara A,
    3. Hori M,
    4. Kamagata K, et al

    . Myelin measurement: comparison between simultaneous tissue relaxometry, magnetization transfer saturation index, and T1w/T2w ratio methods. Sci Rep 2018;8:10554 doi:10.1038/s41598-018-28852-6 pmid:30002497

    CrossRefPubMed
13. 13.↵
    2. Alexander DC,
    3. Dyrby TB,
    4. Nilsson M, et al

    . Imaging brain microstructure with diffusion MRI: practicality and applications. NMR Biomed 2019;32:e3841 doi:10.1002/nbm.3841 pmid:29193413

    CrossRefPubMed
14. 14.↵
    2. Pierpaoli C,
    3. Jezzard P,
    4. Basser PJ, et al

    . Diffusion tensor MR imaging of the human brain. Radiology 1996;201:637–48 doi:10.1148/radiology.201.3.8939209 pmid:8939209

    CrossRefPubMedWeb of Science
15. 15.↵
    2. Zhang H,
    3. Schneider T,
    4. Wheeler-Kingshott CA, et al

    . NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 2012;61:1000–16 doi:10.1016/j.neuroimage.2012.03.072 pmid:22484410

    CrossRefPubMed
16. 16.↵
    2. Stikov N,
    3. Campbell JS,
    4. Stroh T, et al

    . In vivo histology of the myelin g-ratio with magnetic resonance imaging. Neuroimage 2015;118:397–405 doi:10.1016/j.neuroimage.2015.05.023 pmid:26004502

    CrossRefPubMed
17. 17.↵
    2. Arshad M,
    3. Stanley JA,
    4. Raz N

    . Test-retest reliability and concurrent validity of in vivo myelin content indices: myelin water fraction and calibrated T1 w/T2 w image ratio. Hum Brain Mapp 2017;38:1780–90 doi:10.1002/hbm.23481 pmid:28009069

    CrossRefPubMed
18. 18.↵
    2. Smith SM,
    3. Jenkinson M,
    4. Johansen-Berg H, et al

    . Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 2006;31:1487–505 doi:10.1016/j.neuroimage.2006.02.024 pmid:16624579

    CrossRefPubMedWeb of Science
19. 19.↵
    2. Polman CH,
    3. Reingold SC,
    4. Banwell B, et al

    . Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 2011;69:292–302 doi:10.1002/ana.22366 pmid:21387374

    CrossRefPubMedWeb of Science
20. 20.↵
    2. Kurtzke JF

    . A new scale for evaluating disability in multiple sclerosis. Neurology 1955;5:580–83 doi:10.1212/WNL.5.8.580 pmid:13244774

    FREE Full Text
21. 21.↵
    2. Fazekas F,
    3. Chawluk JB,
    4. Alavi A, et al

    . MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am J Roentgenol 1987;149:351–56 doi:10.2214/ajr.149.2.351 pmid:3496763

    CrossRefPubMedWeb of Science
22. 22.↵
    2. Andica C,
    3. Hagiwara A,
    4. Kamagata K, et al

    . Gray matter alterations in early and late relapsing-remitting multiple sclerosis evaluated with synthetic quantitative magnetic resonance imaging. Sci Rep 2019;9:8147 doi:10.1038/s41598-019-44615-3 pmid:31148572

    CrossRefPubMed
23. 23.↵
    2. Mohammadi S,
    3. Moller HE,
    4. Kugel H, et al

    . Correcting eddy current and motion effects by affine whole-brain registrations: evaluation of three-dimensional distortions and comparison with slicewise correction. Magn Reson Med 2010;64:1047–56 doi:10.1002/mrm.22501 pmid:20574966

    CrossRefPubMed
24. 24.↵
    2. Becker SM,
    3. Tabelow K,
    4. Voss HU, et al

    . Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS). Med Image Anal 2012;16:1142–55 doi:10.1016/j.media.2012.05.007 pmid:22677817

    CrossRefPubMed
25. 25.↵
    2. Becker SM,
    3. Tabelow K,
    4. Mohammadi S, et al

    . Adaptive smoothing of multi-shell diffusion weighted magnetic resonance data by msPOAS. Neuroimage 2014;95:90–105 doi:10.1016/j.neuroimage.2014.03.053 pmid:24680711

    CrossRefPubMed
26. 26.↵
    2. Daducci A,
    3. Canales-Rodriguez EJ,
    4. Zhang H, et al

    . Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data. Neuroimage 2015;105:32–44 doi:10.1016/j.neuroimage.2014.10.026 pmid:25462697

    CrossRefPubMed
27. 27.↵
    2. Smith SM,
    3. Jenkinson M,
    4. Woolrich MW, et al

    . Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004;23(Suppl 1):S208–19 doi:10.1016/j.neuroimage.2004.07.051 pmid:15501092

    CrossRefPubMedWeb of Science
28. 28.↵
    2. Keihaninejad S,
    3. Ryan NS,
    4. Malone IB, et al

    . The importance of group-wise registration in tract based spatial statistics study of neurodegeneration: a simulation study in Alzheimer's disease. PLoS One 2012;7:e45996 doi:10.1371/journal.pone.0045996 pmid:23139736

    CrossRefPubMed
29. 29.↵
    2. Mori S,
    3. Wakana S,
    4. Nagae-Poetscher LM, et al

    . MRI Atlas of Human White Matter. Amsterdam: Elsevier; 2005
30. 30.↵
    2. Egger C,
    3. Opfer R,
    4. Wang C, et al

    . MRI FLAIR lesion segmentation in multiple sclerosis: does automated segmentation hold up with manual annotation? Neuroimage Clin 2017;13:264–70 doi:10.1016/j.nicl.2016.11.020 pmid:28018853

    CrossRefPubMed
31. 31.↵
    2. Schmidt P,
    3. Gaser C,
    4. Arsic M, et al

    . An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage 2012;59:3774–83 doi:10.1016/j.neuroimage.2011.11.032 pmid:22119648

    CrossRefPubMed
32. 32.↵
    2. Hagiwara A,
    3. Warntjes M,
    4. Hori M, et al

    . SyMRI of the brain: rapid quantification of relaxation rates and proton density, with synthetic MRI, automatic brain segmentation, and myelin measurement. Invest Radiology 2017;52:647–57 doi:10.1097/RLI.0000000000000365 pmid:28257339

    CrossRefPubMed
33. 33.↵
    2. Sbardella E,
    3. Tona F,
    4. Petsas N, et al

    . DTI measurements in multiple sclerosis: evaluation of brain damage and clinical implications. Mult Scler Int 2013;2013:671730 doi:10.1155/2013/671730 pmid:23606965

    CrossRefPubMed
34. 34.↵
    2. Liu Y,
    3. Duan Y,
    4. He Y, et al

    . Whole brain white matter changes revealed by multiple diffusion metrics in multiple sclerosis: a TBSS study. Eur J Radiology 2012;81:2826–32 doi:10.1016/j.ejrad.2011.11.022 pmid:22172535

    CrossRefPubMed
35. 35.↵
    2. Huang J,
    3. Liu Y,
    4. Zhao T, et al

    . White matter microstructural alterations in clinically isolated syndrome and multiple sclerosis. J Clin Neurosci 2018;53:27–33 doi:10.1016/j.jocn.2018.01.007 pmid:29754967

    CrossRefPubMed
36. 36.↵
    2. Miller DH,
    3. Barkhof F,
    4. Frank JA, et al

    . Measurement of atrophy in multiple sclerosis: pathological basis, methodological aspects and clinical relevance. Brain 2002;125:1676–95 doi:10.1093/brain/awf177 pmid:12135961

    CrossRefPubMedWeb of Science
37. 37.↵
    2. Inglese M,
    3. Bester M

    . Diffusion imaging in multiple sclerosis: research and clinical implications. NMR Biomed 2010;23:865–72 doi:10.1002/nbm.1515 pmid:20882528

    CrossRefPubMed
38. 38.↵
    2. Schneider T,
    3. Brownlee W,
    4. Zhang H, et al

    . Sensitivity of multi-shell NODDI to multiple sclerosis white matter changes: a pilot study. Funct Neurol 2017;32:97–101 doi:10.11138/FNeur/2017.32.2.097 pmid:28676143

    CrossRefPubMed
39. 39.↵
    2. Chung AW,
    3. Seunarine KK,
    4. Clark CA

    . NODDI reproducibility and variability with magnetic field strength: a comparison between 1.5 T and 3 T. Hum Brain Mapp 2016;37:4550–65 doi:10.1002/hbm.23328 pmid:27477113

    CrossRefPubMed
40. 40.↵
    2. Barbosa S,
    3. Blumhardt LD,
    4. Roberts N, et al

    . Magnetic resonance relaxation time mapping in multiple sclerosis: normal appearing white matter and the “invisible” lesion load. Magn Reson Imaging 1994;12:33–42 doi:10.1016/0730-725X(94)92350-7 pmid:8295506

    CrossRefPubMedWeb of Science
41. 41.↵
    2. Davies GR,
    3. Hadjiprocopis A,
    4. Altmann DR, et al

    . Normal-appearing grey and white matter T1 abnormality in early relapsing-remitting multiple sclerosis: a longitudinal study. Mult Scler 2007;13:169–77 doi:10.1177/1352458506070726 pmid:17439881

    CrossRefPubMedWeb of Science
42. 42.↵
    2. Shepherd TM,
    3. Kirov II,
    4. Charlson E, et al

    . New rapid, accurate T2 quantification detects pathology in normal-appearing brain regions of relapsing-remitting patients with MS. Neuroimage Clin 2017;14:363–70 doi:10.1016/j.nicl.2017.01.029 pmid:28239545

    CrossRefPubMed
43. 43.↵
    2. Reitz SC,
    3. Hof SM,
    4. Fleischer V, et al

    . Multi-parametric quantitative MRI of normal appearing white matter in multiple sclerosis, and the effect of disease activity on T2. Brain Imaging Behav 2017;11:744–53 doi:10.1007/s11682-016-9550-5 pmid:27138529

    CrossRefPubMed
44. 44.↵
    2. Fujita S,
    3. Hagiwara A,
    4. Hori M, et al

    . 3D quantitative synthetic MRI-derived cortical thickness and subcortical brain volumes: scan-rescan repeatability and comparison with conventional T1-weighted images. J Magn Reson Imaging 2019 Apr 10. [Epub ahead of print] doi:10.1002/jmri.26744 pmid:30968991

    CrossRefPubMed