Evaluation of Ultrafast Wave–Controlled Aliasing in Parallel Imaging 3D-FLAIR in the Visualization and Volumetric Estimation of Cerebral White Matter Lesions

References

1. 1.↵
   1. Filippi M,
   2. Yousry T,
   3. Baratti C, et al

   . Quantitative assessment of MRI lesion load in multiple sclerosis. a comparison of conventional spin-echo with fast fluid-attenuated inversion recovery. Brain 1996;119(Pt 4):1349–55 doi:10.1093/brain/119.4.1349 pmid:8813296

   CrossRefPubMed
2. 2.↵
   1. Schmidt P

   . Bayesian inference for structured additive regression models for large-scale problems with applications to medical imaging. LMU München; 2017. https://edoc.ub.uni-muenchen.de/20373/. Accessed July 22, 2020
3. 3.↵
   1. Schmidt P,
   2. Gaser C,
   3. 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
4. 4.↵
   1. Schmidt P,
   2. Pongratz V,
   3. Kuster P, et al

   . Automated segmentation of changes in FLAIR-hyperintense white matter lesions in multiple sclerosis on serial magnetic resonance imaging. Neuroimage Clin 2019;23:101849 doi:10.1016/j.nicl.2019.101849 pmid:31085465

   CrossRefPubMed
5. 5.↵
   1. Weeda MM,
   2. Brouwer I,
   3. de Vos ML, et al

   . Comparing lesion segmentation methods in multiple sclerosis: Input from one manually delineated subject is sufficient for accurate lesion segmentation. Neuroimage Clin 2019;24:102074 doi:10.1016/j.nicl.2019.102074 pmid:31734527

   CrossRefPubMed
6. 6.↵
   1. Valverde S,
   2. Oliver A,
   3. Roura E, et al

   . Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling. Neuroimage Clin 2015;9:640–47 doi:10.1016/j.nicl.2015.10.012 pmid:26740917

   CrossRefPubMed
7. 7.↵
   1. Egger C,
   2. Opfer R,
   3. 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
8. 8.↵
   1. Moriguchi H,
   2. Duerk JL

   . Bunched phase encoding (BPE): a new fast data acquisition method in MRI. Magn Reson Med 2006;55:633–48 doi:10.1002/mrm.20819 pmid:16470597

   CrossRefPubMed
9. 9.↵
   1. Cauley SF,
   2. Setsompop K,
   3. Bilgic B, et al

   . Autocalibrated wave-CAIPI reconstruction; Joint optimization of k-space trajectory and parallel imaging reconstruction. Magn Reson Med 2017;78:1093–99 doi:10.1002/mrm.26499 pmid:27770457

   CrossRefPubMed
10. 10.↵
    1. Bilgic B,
    2. Gagoski BA,
    3. Cauley SF, et al

    . Wave-CAIPI for highly accelerated 3D imaging. Magn Reson Med 2015;73:2152–62 doi:10.1002/mrm.25347 pmid:24986223

    CrossRefPubMed
11. 11.↵
    1. Polak D,
    2. Setsompop K,
    3. Cauley SF, et al

    . Wave-CAIPI for highly accelerated MP-RAGE imaging. Magn Reson Med 2018;79:401–06 doi:10.1002/mrm.26649 pmid:28220617

    CrossRefPubMed
12. 12.↵
    1. Polak D,
    2. Cauley S,
    3. Huang SY, et al

    . Highly-accelerated volumetric brain examination using optimized wave-CAIPI encoding. J Magn Reson Imaging 2019;50:961–74 doi:10.1002/jmri.26678 pmid:30734388

    CrossRefPubMed
13. 13.↵
    1. Filippi M,
    2. Preziosa P,
    3. Banwell BL, et al

    . Assessment of lesions on magnetic resonance imaging in multiple sclerosis: practical guidelines. Brain 2019;142:1858–75 doi:10.1093/brain/awz144 pmid:31209474

    CrossRefPubMed
14. 14.↵
    1. Reuter M,
    2. Schmansky NJ,
    3. Rosas HD, et al

    . Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 2012;61:1402–18 doi:10.1016/j.neuroimage.2012.02.084 pmid:22430496

    CrossRefPubMedWeb of Science
15. 15.↵
    1. Jain S,
    2. Sima DM,
    3. Ribbens A, et al

    . Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images. Neuroimage Clin 2015;8:367–75 doi:10.1016/j.nicl.2015.05.003 pmid:26106562

    CrossRefPubMed
16. 16.↵
    1. Le M,
    2. Tang LY,
    3. Hernandez-Torres E, et al

    . FLAIR(2) improves LesionTOADS automatic segmentation of multiple sclerosis lesions in non-homogenized, multi-center, 2D clinical magnetic resonance images. Neuroimage Clin 2019;23:101918 doi:10.1016/j.nicl.2019.101918 pmid:31491827

    CrossRefPubMed
17. 17.↵
    1. Dice LR

    . Measures of the amount of ecological association between species. Ecology 1945;26:297–302

    CrossRefWeb of Science
18. 18.↵
    1. Ahn S,
    2. Park SH,
    3. Lee KH

    . How to demonstrate similarity by using noninferiority and equivalence statistical testing in radiology research. Radiology 2013;267:328–38 doi:10.1148/radiol.12120725 pmid:23610094

    CrossRefPubMedWeb of Science
19. 19.↵
    1. Conklin J,
    2. Longo MG,
    3. Cauley SF, et al

    . Validation of highly accelerated wave–CAIPI SWI compared with conventional SWI and T2*-weighted gradient recalled-echo for routine clinical brain MRI at 3T. AJNR Am J Neuroradiol 2019;40:2073–80 doi:10.3174/ajnr.A6295] pmid:31727749

    Abstract/FREE Full Text
20. 20.↵
    1. Chow SS,
    2. Shao J.
    3. Wang H

    . Sample Size Calculation in Clinical Research. Marcel Dekker; 2003
21. 21.↵
    1. Shiee N,
    2. Bazin PL,
    3. Ozturk A, et al

    . A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. Neuroimage 2010;49:1524–35 doi:10.1016/j.neuroimage.2009.09.005 pmid:19766196

    CrossRefPubMed
22. 22.↵
    1. Roura E,
    2. Oliver A,
    3. Cabezas M, et al

    . A toolbox for multiple sclerosis lesion segmentation. Neuroradiology 2015;57:1031–43 doi:10.1007/s00234-015-1552-2 pmid:26227167

    CrossRefPubMed
23. 23.↵
    1. Sweeney EM,
    2. Shinohara RT,
    3. Shiee N, et al

    . OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI. Neuroimage Clin 2013;2:402–13 doi:10.1016/j.nicl.2013.03.002 pmid:24179794

    CrossRefPubMed
24. 24.↵
    1. Lecler A,
    2. El Sanharawi I,
    3. El Methni J, et al

    . Improving detection of multiple sclerosis lesions in the posterior fossa using an optimized 3D-FLAIR sequence at 3T. AJNR Am J Neuroradiol 2019;40:1170–76 doi:10.3174/ajnr.A6107 pmid:31248862

    Abstract/FREE Full Text
25. 25.↵
    1. Wang KY,
    2. Uribe TA,
    3. Lincoln CM

    . Comparing lesion detection of infratentorial multiple sclerosis lesions between T2-weighted spin-echo, 2D-FLAIR, and 3D-FLAIR sequences. Clin imaging 2018;51:229–34 doi:10.1016/j.clinimag.2018.05.017 pmid:29879598

    CrossRefPubMed
26. 26.↵
    1. Prakkamakul S,
    2. Witzel T,
    3. Huang S, et al

    . Ultrafast brain MRI: clinical deployment and comparison to conventional brain MRI at 3T. J Neuroimaging 2016;26:503–10 doi:10.1111/jon.12365 pmid:27273370

    CrossRefPubMed
27. 27.↵
    1. Longo MG,
    2. Conklin J,
    3. Cauley SF, et al

    . Evaluation of ultrafast wave-CAIPI MPRAGE for visual grading and automated measurement of brain tissue volume. AJNR Am J Neuroradiol 2020;41:1388–96 doi:10.3174/ajnr.A6703 pmid:32732274

    Abstract/FREE Full Text
28. 28.↵
    1. Sati P,
    2. Oh J,
    3. Constable RT, et al

    . NAIMS Cooperative. The central vein sign and its clinical evaluation for the diagnosis of multiple sclerosis: a consensus statement from the North American Imaging in Multiple Sclerosis Cooperative. Nat Rev Neurol 2016;12:714–22 doi:10.1038/nrneurol.2016.166 pmid:27834394

    CrossRefPubMed
29. 29.
    1. Absinta M,
    2. Sati P,
    3. Fechner A, et al

    . Identification of chronic active multiple sclerosis lesions on 3T MRI. AJNR Am J Neuroradiol 2018;39:1233–38 doi:10.3174/ajnr.A5660 pmid:29724768

    Abstract/FREE Full Text
30. 30.↵
    1. Clarke MA,
    2. Pareto D,
    3. Pessini-Ferreira L, et al

    . Value of 3T susceptibility-weighted imaging in the diagnosis of multiple sclerosis. AJNR Am J Neuroradiol 2020;41:1001–08 doi:10.3174/ajnr.A6547 pmid:32439639

    Abstract/FREE Full Text
31. 31.↵
    1. Absinta M,
    2. Sati P,
    3. Masuzzo F, et al

    . Association of chronic active multiple sclerosis lesions with disability in vivo. JAMA Neurol 2019;76:1474–83 doi:10.1001/jamaneurol.2019.2399 pmid:31403674

    CrossRefPubMed