Deep multiple sclerosis lesion phenotyping using multimodal quantitative MRI

被引:0
|
作者
Boffa, Giacomo [1 ]
Schiavi, Simona [1 ]
Tazza, Francesco [1 ]
Lapucci, Caterina [1 ]
Piredda, Gian Franco [2 ,3 ,4 ,5 ]
Zaca, Domenico [6 ]
Roccatagliata, Luca [7 ]
Hilbert, Tom [2 ,3 ,4 ,5 ]
Kober, Tobias [2 ,3 ,4 ,5 ]
Inglese, Matilde [1 ]
Costagli, Mauro [1 ]
机构
[1] Univ Genoa, Dept Neurol Rehabil Ophthalmol Genet Maternal & C, Genoa, Italy
[2] Siemens Healthineers Int AG, Adv Clin Imaging Technol, Lausanne, Switzerland
[3] Ecole Polytech Fed Lausanne EPFL, LTS5, Lausanne, Switzerland
[4] Univ Lausanne Hosp, Lausanne, Switzerland
[5] Univ Lausanne, Dept Radiol, Luasanne, Switzerland
[6] Siemens Healthcare, Milan, Italy
[7] Univ Genoa, Dept Neuroradiol, Hlth Sci, Genoa, Italy
关键词
D O I
暂无
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
P195/2077
引用
收藏
页码:273 / 274
页数:2
相关论文
共 50 条
  • [41] The neural basis of fatigue in Multiple Sclerosis: a multimodal MRI approach
    Batista, S.
    Novo, A. M.
    Alves, C.
    d'Almeida, O. C.
    Marques, I. B.
    Macario, C.
    Sousa, L.
    Castelo-Branco, M.
    Santana, I.
    Cunha, L.
    MULTIPLE SCLEROSIS JOURNAL, 2016, 22 : 209 - 210
  • [42] Lesion evolution on MRI in multiple sclerosis with separation of myelin and iron
    Zhu, Z.
    Esfahani, J. Hamidi
    Naji, N.
    Strei, T.
    Seres, P.
    Emery, D.
    Blevins, G.
    Smyth, P.
    Wilman, A.
    MULTIPLE SCLEROSIS JOURNAL, 2022, 28 (3_SUPPL) : 255 - 256
  • [43] Modelling new enhancing MRI lesion counts in multiple sclerosis
    Sormani, MP
    Bruzzi, P
    Rovaris, M
    Barkhof, F
    Comi, G
    Miller, DH
    Cutter, GR
    Filippi, M
    MULTIPLE SCLEROSIS, 2001, 7 (05): : 298 - 304
  • [44] Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI
    Balasrinivasa Rao Sajja
    Sushmita Datta
    Renjie He
    Meghana Mehta
    Rakesh K. Gupta
    Jerry S. Wolinsky
    Ponnada A. Narayana
    Annals of Biomedical Engineering, 2006, 34 : 142 - 151
  • [45] Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI
    Wu, Ying
    Warfield, Simon K.
    Tan, I. Leng
    Wells, William M., III
    Meier, Dominik S.
    van Schijndel, Ronald A.
    Barkhof, Frederik
    Guttmann, Charles R. G.
    NEUROIMAGE, 2006, 32 (03) : 1205 - 1215
  • [46] Unified approach for multiple sclerosis lesion segmentation on brain MRI
    Sajja, BR
    Datta, S
    He, RJ
    Mehta, M
    Gupta, RK
    Wolinsky, JS
    Narayana, PA
    ANNALS OF BIOMEDICAL ENGINEERING, 2006, 34 (01) : 142 - 151
  • [47] Perfusion MRI in automatic classification of multiple sclerosis lesion subtypes
    Homayouny, Ehsan
    Khayati, Rasoul Mahdavifar
    Nabavi, Seyed Massood
    Karami, Vania
    IET SIGNAL PROCESSING, 2022, 16 (04) : 377 - 390
  • [48] SCANNER INVARIANT MULTIPLE SCLEROSIS LESION SEGMENTATION FROM MRI
    Aslani, Shahab
    Murino, Vittorio
    Dayan, Michael
    Tam, Roger
    Sona, Diego
    Hamarneh, Ghassan
    2020 IEEE 17TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2020), 2020, : 781 - 785
  • [49] Quantitative follow-up of patients with multiple sclerosis using MRI: Reproducibility
    Guttmann, CRG
    Kikinis, R
    Anderson, MC
    Jakab, M
    Warfield, SK
    Killiany, RJ
    Weiner, HL
    Jolesz, FA
    JMRI-JOURNAL OF MAGNETIC RESONANCE IMAGING, 1999, 9 (04): : 509 - 518
  • [50] Multimodal skin lesion classification using deep learning
    Yap, Jordan
    Yolland, William
    Tschandl, Philipp
    EXPERIMENTAL DERMATOLOGY, 2018, 27 (11) : 1261 - 1267