Multiparametric Characterization and Spatial Distribution of Different MS Lesion Phenotypes

References

1. 1.↵
   1. Kuhlmann T,
   2. Ludwin S,
   3. Prat A, et al

   . An updated histological classification system for multiple sclerosis lesions. Acta Neuropathol 2017;133:13–24 doi:10.1007/s00401-016-1653-y pmid:27988845

   CrossRefPubMed
2. 2.↵
   1. Pardini M,
   2. Brown JWL,
   3. Magliozzi R, et al

   . Surface-in pathology in multiple sclerosis: a new view on pathogenesis? Brain J Brain 2021;144:1646–54 doi:10.1093/brain/awab025 pmid:33876200

   CrossRefPubMed
3. 3.↵
   1. Klistorner S,
   2. Barnett MH,
   3. Graham SL, et al

   . The expansion and severity of chronic MS lesions follows a periventricular gradient. Mult Scler 2022;28:1504–14 doi:10.1177/13524585221080667 pmid:35296170

   CrossRefPubMed
4. 4.↵
   1. Vaneckova M,
   2. Piredda GF,
   3. Andelova M, et al

   . Periventricular gradient of T1 tissue alterations in multiple sclerosis. Neuroimage Clin 2022;34:103009 doi:10.1016/j.nicl.2022.103009 pmid:35561554

   CrossRefPubMed
5. 5.↵
   1. Marinero C,
   2. Louapre C,
   3. Govindarajan ST, et al

   . Gradient in cortical pathology in multiple sclerosis by in vivo quantitative 7 T imaging. Brain 2015;138(Pt 4):932–45 doi:10.1093/brain/awv011 pmid:25681411

   CrossRefPubMed
6. 6.↵
   1. Poirion E,
   2. Tonietto M,
   3. Lejeune FX, et al

   . Structural and clinical correlates of a periventricular gradient of neuroinflammation in multiple sclerosis. Neurology 2021;96:e1865–75 doi:10.1212/WNL.0000000000011700 pmid:33737372

   Abstract/FREE Full Text
7. 7.↵
   1. Gillen KM,
   2. Mubarak M,
   3. Park C, et al

   . QSM is an imaging biomarker for chronic glial activation in multiple sclerosis lesions. Ann Clin Transl Neurol 2021;8:877–86 doi:10.1002/acn3.51338 pmid:33704933

   CrossRefPubMed
8. 8.↵
   1. Rahmanzadeh R,
   2. Galbusera R,
   3. Lu PJ, et al

   . A new advanced MRI biomarker for remyelinated lesions in multiple sclerosis. Ann Neurol 2022;92:486–502 doi:10.1002/ana.26441 pmid:35713309

   CrossRefPubMed
9. 9.↵
   1. Ng Kee Kwong KC,
   2. Mollison D,
   3. Meijboom R, et al

   . The prevalence of paramagnetic rim lesions in multiple sclerosis: a systematic review and meta-analysis. PLoS One 2021;16:e0256845 doi:10.1371/journal.pone.0256845 pmid:34495999

   CrossRefPubMed
10. 10.↵
    1. Bagnato F,
    2. Sati P,
    3. Hemond CC, et al

    . Imaging chronic active lesions in multiple sclerosis: a consensus statement. Brain J Neurol 2024 Jan 16 [Epub ahead of print] doi:10.1093/brain/awae013 pmid:38226694

    CrossRefPubMed
11. 11.↵
    1. Gho SM,
    2. Liu C,
    3. Li W, et al

    . Susceptibility map-weighted imaging (SMWI) for neuroimaging. Magn Reson Med 2014;72:337–46 doi:10.1002/mrm.24920 pmid:24006248

    CrossRefPubMed
12. 12.↵
    1. Krajnc N,
    2. Schmidbauer V,
    3. Leinkauf J, et al

    . Paramagnetic rim lesions lead to pronounced diffuse periplaque white matter damage in multiple sclerosis. Mult Scler 2023;29:1406–17 doi:10.1177/13524585231197954 pmid:37712486

    CrossRefPubMed
13. 13.↵
    1. Kolb H,
    2. Absinta M,
    3. Beck ES, et al

    . 7T MRI differentiates remyelinated from demyelinated multiple sclerosis lesions. Ann Neurol 2021;90:612–26 doi:10.1002/ana.26194 pmid:34390015

    CrossRefPubMed
14. 14.↵
    1. Hu H,
    2. Ye L,
    3. Ding S, et al

    . The heterogeneity of tissue destruction between iron rim lesions and non-iron rim lesions in multiple sclerosis: a diffusion MRI study. Mult Scler Relat Disord 2022;66:104070 doi:10.1016/j.msard.2022.104070 pmid:35914471

    CrossRefPubMed
15. 15.↵
    1. Piredda GF,
    2. Hilbert T,
    3. Thiran JP, et al

    . Probing myelin content of the human brain with MRI: a review. Magn Reson Med 2021;85:627–52 doi:10.1002/mrm.28509 pmid:32936494

    CrossRefPubMed
16. 16.↵
    1. Mussard E,
    2. Hilbert T,
    3. Forman C, et al

    . Accelerated MP2RAGE imaging using Cartesian phyllotaxis readout and compressed sensing reconstruction. Magn Reson Med 2020;84:1881–94 doi:10.1002/mrm.28244 pmid:32176826

    CrossRefPubMed
17. 17.↵
    1. Piredda GF,
    2. Hilbert T,
    3. Canales-Rodríguez EJ, et al

    . Fast and high-resolution myelin water imaging: Accelerating multi-echo GRASE with CAIPIRINHA. Magn Reson Med 2021;85:209–22 doi:10.1002/mrm.28427 pmid:32720406

    CrossRefPubMed
18. 18.↵
    1. Sati P,
    2. Thomasson DM,
    3. Li N, et al

    . Rapid, high-resolution, whole-brain, susceptibility-based MRI of multiple sclerosis. Mult Scler 2014;20:1464–70 doi:10.1177/1352458514525868 pmid:24639479

    CrossRefPubMed
19. 19.↵
    1. Zhang F,
    2. Daducci A,
    3. He Y, et al

    . Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: a review. Neuroimage 2022;249:118870 doi:10.1016/j.neuroimage.2021.118870 pmid:34979249

    CrossRefPubMed
20. 20.↵
    1. Caruyer E,
    2. Lenglet C,
    3. Sapiro G, et al

    . Design of multishell sampling schemes with uniform coverage in diffusion MRI. Magn Reson Med 2013;69:1534–40 doi:10.1002/mrm.24736 pmid:23625329

    CrossRefPubMed
21. 21.↵
    1. von Elm E,
    2. Altman DG,
    3. Egger M, et al

    . The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies. Ann Intern Med 2007;147:573–77 doi:10.7326/0003-4819-147-8-200710160-00010 pmid:17938396

    CrossRefPubMedWeb of Science
22. 22.↵
    1. Schofield MA,
    2. Zhu Y

    . Fast phase unwrapping algorithm for interferometric applications. Opt Lett 2003;28:1194–96 doi:10.1364/ol.28.001194 pmid:12885018

    CrossRefPubMed
23. 23.↵
    1. Schweser F,
    2. Deistung A,
    3. Lehr BW, et al

    . Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: an approach to in vivo brain iron metabolism? Neuroimage 2011;54:2789–807 doi:10.1016/j.neuroimage.2010.10.070 pmid:21040794

    CrossRefPubMedWeb of Science
24. 24.↵
    1. Nam Y,
    2. Gho SM,
    3. Kim DH, et al

    . Imaging of nigrosome 1 in substantia nigra at 3T using multiecho susceptibility map-weighted imaging (SMWI). J Magn Reson Imaging 2017;46:528–36 doi:10.1002/jmri.25553 pmid:27859983

    CrossRefPubMed
25. 25.↵
    1. Bilgic B,
    2. Costagli M,
    3. Chan KS, et al

    ; ISMRM Electro-Magnetic Tissue Properties Study Group. Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: a consensus of the ISMRM electro-magnetic tissue properties study group. Magn Reson Med 2024;91:1834–62 doi:10.1002/mrm.30006 pmid:38247051

    CrossRefPubMed
26. 26.↵
    1. Jenkinson M,
    2. Beckmann CF,
    3. Behrens TEJ, et al

    . FSL. Neuroimage 2012;62:782–90 doi:10.1016/j.neuroimage.2011.09.015 pmid:21979382

    CrossRefPubMedWeb of Science
27. 27.↵
    1. Lim IA,
    2. Faria AV,
    3. Li X, et al

    . Human brain atlas for automated region of interest selection in quantitative susceptibility mapping: application to determine iron content in deep gray matter structures. Neuroimage 2013;82:449–69 doi:10.1016/j.neuroimage.2013.05.127 pmid:23769915

    CrossRefPubMed
28. 28.↵
    1. Lancione M,
    2. Tosetti M,
    3. Donatelli G, et al

    . The impact of white matter fiber orientation in single-acquisition quantitative susceptibility mapping. NMR Biomed 2017;30(11) doi:10.1002/nbm.3798 pmid:28902421

    CrossRefPubMed
29. 29.↵
    1. Canales-Rodríguez EJ,
    2. Pizzolato M,
    3. Piredda GF, et al

    . Comparison of non-parametric T2 relaxometry methods for myelin water quantification. Med Image Anal 2021;69:101959 doi:10.1016/j.media.2021.101959 pmid:33581618

    CrossRefPubMed
30. 30.↵
    1. Veraart J,
    2. Novikov DS,
    3. Christiaens D, et al

    . Denoising of diffusion MRI using random matrix theory. NeuroImage 2016;142:394–406 doi:10.1016/j.neuroimage.2016.08.016 pmid:27523449

    CrossRefPubMed
31. 31.↵
    1. Andersson JLR,
    2. Sotiropoulos SN

    . An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 2016;125:1063–78 doi:10.1016/j.neuroimage.2015.10.019 pmid:26481672

    CrossRefPubMed
32. 32.↵
    1. Avants BB,
    2. Tustison NJ,
    3. Song G, et al

    . A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 2011;54:2033–44 doi:10.1016/j.neuroimage.2010.09.025 pmid:20851191

    CrossRefPubMed
33. 33.↵
    1. Zhang H,
    2. Schneider T,
    3. 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
34. 34.↵
    1. Daducci A,
    2. Canales-Rodríguez EJ,
    3. 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
35. 35.↵
    1. Marques JP,
    2. Kober T,
    3. Krueger G, et al

    . MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. NeuroImage 2010;49:1271–81 doi:10.1016/j.neuroimage.2009.10.002 pmid:19819338

    CrossRefPubMedWeb of Science
36. 36.↵
    1. Hemond CC,
    2. Reich DS,
    3. Dundamadappa SK

    . Paramagnetic rim lesions in multiple sclerosis: comparison of visualization at 1.5-T and 3-T MRI. AJR Am J Roentgenol 2022;219:120–31 doi:10.2214/AJR.21.26777 pmid:34851712

    CrossRefPubMed
37. 37.↵
    1. Hamzaoui M,
    2. Garcia J,
    3. Boffa G, et al

    . Positron emission tomography with [¹⁸F]-DPA-714 unveils a smoldering component in most multiple sclerosis lesions which drives disease progression. Ann Neurol 2023;94:366–83 doi:10.1002/ana.26657 pmid:37039158

    CrossRefPubMed
38. 38.↵
    1. Tonietto M,
    2. Poirion E,
    3. Lazzarotto A, et al

    . Periventricular remyelination failure in multiple sclerosis: a substrate for neurodegeneration. Brain 2023;146:182–94 doi:10.1093/brain/awac334 pmid:36097347

    CrossRefPubMed
39. 39.↵
    1. Galbusera R,
    2. Bahn E,
    3. Weigel M, et al

    . Characteristics, prevalence, and clinical relevance of juxtacortical paramagnetic rims in patients with multiple sclerosis. Neurology 2024;102:e207966 doi:10.1212/WNL.0000000000207966 pmid:38165297

    CrossRefPubMed