1.
Filippi, M, Preziosa, P, Banwell, BL, et al. Assessment of lesions on magnetic resonance imaging in multiple sclerosis: Practical guidelines. Brain 2019; 142(7): 1858–1875.
Google Scholar |
Crossref |
Medline2.
Rovira, A, Wattjes, MP, Tintore, M, et al. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis-clinical implementation in the diagnostic process. Nat Rev Neurol 2015; 11(8): 471–482.
Google Scholar |
Crossref |
Medline |
ISI3.
Wattjes, MP, Rovira, A, Miller, D, et al. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis-establishing disease prognosis and monitoring patients. Nat Rev Neurol 2015; 11: 597–606.
Google Scholar |
Crossref |
Medline |
ISI4.
Fahrbach, K, Huelin, R, Martin, AL, et al. Relating relapse and T2 lesion changes to disability progression in multiple sclerosis: A systematic literature review and regression analysis. BMC Neurol 2013; 13: 180.
Google Scholar |
Crossref |
Medline |
ISI5.
Río, J, Auger, C, Rovira, À. MR imaging in monitoring and predicting treatment response in multiple sclerosis. Neuroimaging Clin N Am 2017; 27(2): 277–287.
Google Scholar |
Crossref |
Medline6.
Altay, E, Fisher, E, Jones, SE, et al. Reliability of classifying multiple sclerosis disease activity using magnetic resonance imaging in a multiple sclerosis clinic. JAMA Neurol 2013; 70: 338–344.
Google Scholar |
Crossref |
Medline7.
Patriarche, J, Erickson, B. A review of the automated detection of change in serial imaging studies of the brain. J Digit Imaging 2004; 17(3): 158–174.
Google Scholar |
Crossref |
Medline8.
Moraal, B, Wattjes, MP, Geurts, JJ, et al. Improved detection of active multiple sclerosis lesions: 3D subtraction imaging. Radiology 2010; 255(1): 154–163.
Google Scholar |
Crossref |
Medline9.
Lladó, X, Ganiler, O, Oliver, A, et al. Automated detection of multiple sclerosis lesions in serial brain MRI. Neuroradiology 2012; 54(8): 787–807.
Google Scholar |
Crossref |
Medline10.
Elliott, C, Arnold, DL, Collins, DL, et al. Temporally consistent probabilistic detection of new multiple sclerosis lesions in brain MRI. IEEE Trans Med Imaging 2013; 32(8): 1490–1503.
Google Scholar |
Crossref |
Medline11.
Sweeney, EM, Shinohara, RT, Shea, CD, et al. Automatic lesion incidence estimation and detection in multiple sclerosis using multisequence longitudinal MRI. Am J Neuroradiol 2013; 34(1): 68–73.
Google Scholar |
Crossref |
Medline12.
Battaglini, M, Rossi, F, Grove, RA, et al. Automated identification of brain new lesions in multiple sclerosis using subtraction images. J Magn Reson Imaging 2014; 39(6): 1543–1549.
Google Scholar |
Crossref |
Medline13.
Danelakis, A, Theoharis, T, Verganelakis, DA. Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging. Comput Med Imaging Graph 2018; 70: 83–100.
Google Scholar |
Crossref |
Medline14.
Cabezas, M, Corral, JF, Oliver, A, et al. Improved automatic detection of new T2 lesions in multiple sclerosis using deformation fields. Am J Neuroradiol 2016; 37(10): 1816–1823.
Google Scholar |
Crossref |
Medline15.
Salem, M, Valverde, S, Cabezas, M, et al. A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis. Neuroimage Clin 2020; 25: 102149.
Google Scholar |
Crossref |
Medline16.
Iglesias, J, Liu, C, Thompson, PM, et al. Robust brain extraction across datasets and comparison with publicly available methods. IEEE Trans Med Imaging 2011; 30(9): 1617–1634.
Google Scholar |
Crossref |
Medline17.
Tustison, N, Avants, B, Cook, P, et al. N4ITK: Improved N3 bias correction. IEEE Trans Med Imaging 2010; 29(6): 1310–1320.
Google Scholar |
Crossref |
Medline18.
Modat, M, Cash, D, Daga, P, et al. Global image registration using a symmetric block-matching approach. J Med Imaging 2014; 1(2): 024003.
Google Scholar |
Crossref19.
Nyúl, LG, Udupa, JK, Zhang, X. New variants of a method of MRI scale standardization. IEEE Trans Med Imaging 2000; 19(2): 143–150.
Google Scholar |
Crossref |
Medline20.
Havrdova, E, Galetta, S, Hutchinson, M, et al. Effect of natalizumab on clinical and radiological disease activity in multiple sclerosis: A retrospective analysis of the Natalizumab Safety and Efficacy in Relapsing-Remitting Multiple Sclerosis (AFFIRM) study. Lancet Neurol 2009; 8(3): 254–260.
Google Scholar |
Crossref |
Medline |
ISI21.
Stangel, M, Penner, IK, Kallmann, BA, et al. Towards the implementation of “no evidence of disease activity” in multiple sclerosis treatment: The multiple sclerosis decision model. Ther Adv Neurol Disord 2015; 8(1): 3–13.
Google Scholar |
SAGE Journals |
ISI22.
Gasperini, C, Prosperini, L, Tintoré, M, et al. Unraveling treatment response in multiple sclerosis: A clinical and MRI challenge. Neurology 2019; 92: 180–192.
Google Scholar |
Crossref |
Medline23.
Río, J, Castilló, J, Rovira, A, et al. Measures in the first year of therapy predict the response to interferon beta in MS. Mult Scler 2009; 15(7): 848–853.
Google Scholar |
SAGE Journals |
ISI24.
Sormani, M, Bruzzi, P. MRI lesions as a surrogate for relapses in multiple sclerosis: A meta-analysis of randomised trials. Lancet Neurol 2013; 12(7): 669–676.
Google Scholar |
Crossref |
Medline |
ISI25.
Tintore, M, Vidal-Jordana, A, Sastre-Garriga, J. Treatment of multiple sclerosis—Success from bench to bedside. Nat Rev Neurol 2019; 15(1): 53–58.
Google Scholar |
Crossref |
Medline26.
Molyneux, PD, Miller, DH, Filippi, M, et al. Visual analysis of serial T2-weighted MRI in multiple sclerosis: Intra- and interobserver reproducibility. Neuroradiology 1999; 41(12): 882–888.
Google Scholar |
Crossref |
Medline27.
Tan, IL, van Schijndel, RA, Fazekas, F, et al. Image registration and subtraction to detect active T(2) lesions in MS: An interobserver study. J Neurol 2002; 249(6): 767–773.
Google Scholar |
Crossref |
Medline28.
Krüger, J, Opfer, R, Gessert, N, et al. Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks. Neuroimage Clin 2020; 28: 102445.
Google Scholar |
Crossref |
Medline
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