, Agence Nationale de la Recherche-11-Initiative d'Excellence-004, project LearnPETMR number SU-16-R-EMR-16), and from the

A. Compston and A. Coles, Multiple sclerosis, Lancet, vol.372, issue.9648, pp.1502-1517, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00996686

D. W. Paty, J. J. Oger, and L. F. Kastrukoff, MRI in the diagnosis of MS: a prospective study with comparison of clinical evaluation, evoked potentials, oligoclonal banding, and CT, Neurology, vol.38, pp.180-185, 1988.

F. Barkhof, M. Filippi, and D. H. Miller, Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis, Brain, vol.120, pp.2059-2069, 1997.

J. H. Woo, L. P. Henry, and J. Krejza, Detection of simulated multiple sclerosis lesions on t2-weighted and flair images of the brain: Observer performance, Radiology, vol.241, issue.1, pp.206-212, 2006.

S. G. Mueller, M. W. Weiner, and L. J. Thal, The alzheimer's disease neuroimaging initiative, Neuroimaging clinics of North America, vol.15, p.869, 2005.

J. E. Iglesias, E. Konukoglu, and D. Zikic, Is synthesizing mri contrast useful for intermodality analysis?, Medical Image Computing and Computer-Assisted InterventionMICCAI 2013, vol.8149, 2013.

G. Van-tulder and M. De-bruijne, Why does synthesized data improve multi-sequence classification?, Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015, vol.9349, 2015.

S. Roy, A. Carass, and N. Shiee, MR contrast synthesis for lesion segmentation, Proc IEEE Int Symp Biomed Imaging, pp.932-935, 2010.

A. Jog, A. Carass, and D. L. Pham, Random Forest FLAIR Reconstruction from T1, T2, and PD-Weighted MRI, Proc IEEE Int Symp Biomed Imaging, pp.1079-1082, 2014.

T. Huynh, Y. Gao, and J. Kang, Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model, IEEE Trans Med Imaging, vol.35, issue.1, pp.174-183, 2016.

N. Burgos, M. J. Cardoso, and K. Thielemans, Attenuation correction synthesis for hybrid pet-mr scanners: Application to brain studies, IEEE Transactions on Medical Imaging, vol.33, issue.12, pp.2332-2341, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01827217

K. He, X. Zhang, and S. Ren, Deep residual learning for image recognition, 2016 IEEE Conference on CVPR, pp.770-778, 2016.

G. Chen, W. Choi, and X. Yu, Learning efficient object detection models with knowledge distillation, Advances in Neural Information Processing Systems, vol.30

S. Luxburg and . Bengio, , pp.742-751, 2017.

E. Shelhamer, J. Long, and T. Darrell, Fully convolutional networks for semantic segmentation, IEEE Trans. Pattern Anal. Mach. Intell, vol.39, issue.4, pp.640-651, 2017.

S. Zhou, H. Greenspan, and D. Shen, Deep Learning for Medical Image Analysis, 2017.

K. Bahrami, F. Shi, and I. Rekik, Convolutional neural network for reconstruction of 7t-like images from 3t mri using appearance and anatomical features, Deep Learning and Data Labeling for Medical Applications, LABELS 2016, vol.10008, 2016.

D. Nie, X. Cao, and Y. Gao, Estimating ct image from mri data using 3d fully convolutional networks, Deep Learning and Data Labeling for Medical Applications-LABELS 2016, vol.10008, pp.170-178, 2016.

R. Li, W. Zhang, and H. Suk, Deep learning based imaging data completion for improved brain disease diagnosis, Medical Image Computing and Computer-Assisted Intervention-MICCAI 2014, vol.8675, pp.305-312, 2014.

V. Sevetlidis, M. V. Giuffrida, and S. A. Tsaftaris, Whole image synthesis using a deep encoder-decoder network, Simulation and Synthesis in Medical Imaging, vol.9968, pp.127-137, 2016.

Y. Lecun, B. Boser, and J. S. Denker, Backpropagation applied to handwritten zip code recognition, Neural Comput, vol.1, pp.541-551, 1989.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, NIPS 25, pp.1097-1105, 2012.

K. Simonyan, A. Vedaldi, and A. Zisserman, Deep inside convolutional networks: Visualising image classification models and saliency maps, 2013.

N. J. Tustison, B. B. Avants, and P. A. Cook, N4itk: Improved n3 bias correction, IEEE Transactions on Medical Imaging, vol.29, pp.1310-1320, 2010.

D. N. Greve and B. Fischl, Accurate and robust brain image alignment using boundary-based registration, NeuroImage, vol.48, issue.1, pp.63-72, 2009.

T. Development-team, Theano: A Python framework for fast computation of mathematical expressions, 2016.

F. Chollet, Keras, p.21, 2015.

D. H. Ye, D. Zikic, and B. Glocker, Modality propagation: Coherent synthesis of subjectspecific scans with data-driven regularization, Medical Image Computing and ComputerAssisted Intervention-MICCAI 2013, vol.8149, 2013.

O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, Medical Image Computing and Computer-Assisted Intervention (MICCAI), vol.9351, pp.234-241, 2015.

P. Coupé, T. Tourdias, and P. Linck, Lesionbrain: An online tool for white matter lesion segmentation, International Workshop on Patch-based Techniques in Medical ImagingPatch-MI 2018, 2018.

P. Isola, J. Zhu, and T. Zhou, Image-to-image translation with conditional adversarial networks, 2016.

J. Koikkalainen, H. Rhodius-meester, and A. Tolonen, Differential diagnosis of neurodegenerative diseases using structural mri data, NeuroImage: Clinical, vol.11, pp.435-449, 2016.

W. Wei, MSc at University of Paris XI, is a PhD student in ARAMIS Laboratory and EPIONE