B. Bodini, M. Veronese, D. García-lorenzo, M. Battaglini, E. Poirion et al., Dynamic imaging of individual remyelination profiles in multiple sclerosis, Annals of Neurology, vol.79, issue.5, pp.726-738, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01282002

P. Patrikios, C. Stadelmann, A. Kutzelnigg, H. Rauschka, M. Schmidbauer et al., Remyelination is extensive in a subset of multiple sclerosis patients, Brain, vol.129, issue.12, pp.3165-3172, 2006.

A. Petiet, I. Adanyeguh, M. Aigrot, E. Poirion, B. Nait-oumesmar et al., Ultrahigh field imaging of myelin disease models: Toward specific markers of myelin integrity?, Journal of Comparative Neurology, vol.527, issue.13, pp.2179-2189, 2019.

Z. Wang, C. Vandersteen, T. Demarcy, D. Gnansia, C. Raffaelli et al., Deep Learning Based Metal Artifacts Reduction in Post-operative Cochlear Implant CT Imaging, Medical Image Computing and Computer Assisted Intervention -MICCAI 2019, pp.121-129, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02196557

Y. Chen, F. Yu, L. Luo, and C. Toumoulin, Improving abdomen tumor low-dose CT images using dictionary learning based patch processing and unsharp filtering, 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.4014-4017, 2013.

Q. Xu, H. Yu, X. Mou, L. Zhang, J. Hsieh et al., Low-Dose X-ray CT Reconstruction via Dictionary Learning, IEEE Transactions on Medical Imaging, vol.31, issue.9, pp.1682-1697, 2012.

Y. Zhang, X. Mou, G. Wang, and H. Yu, Tensor-Based Dictionary Learning for Spectral CT Reconstruction, IEEE Transactions on Medical Imaging, vol.36, issue.1, pp.142-154, 2017.

Y. Liu, J. Ma, Y. Fan, and Z. Liang, Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction, Physics in Medicine and Biology, vol.57, issue.23, pp.7923-7956, 2012.

Z. Tian, X. Jia, K. Yuan, T. Pan, and S. B. Jiang, Low-dose CT reconstruction via edge-preserving total variation regularization, Physics in Medicine and Biology, vol.56, issue.18, pp.5949-5967, 2011.

K. Bahrami, F. Shi, X. Zong, H. W. Shin, H. An et al., Reconstruction of 7T-Like Images From 3T MRI, IEEE Transactions on Medical Imaging, vol.35, issue.9, pp.2085-2097, 2016.

S. Kaplan and Y. Zhu, Full-Dose PET Image Estimation from Low-Dose PET Image Using Deep Learning: a Pilot Study, Journal of Digital Imaging, vol.32, issue.5, pp.773-778, 2019.

A. Hagiwara, Y. Otsuka, M. Hori, Y. Tachibana, K. Yokoyama et al., Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation

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

D. H. Ye, D. Zikic, B. Glocker, A. Criminisi, and E. Konukoglu, Modality propagation: coherent synthesis of subject-specific scans with data-driven regularization, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.606-613, 2013.

V. Sevetlidis, M. V. Giuffrida, and S. A. Tsaftaris, Whole Image Synthesis Using a Deep Encoder-Decoder Network, in: Simulation and Synthesis in Medical Imaging, pp.127-137, 2016.

A. Chartsias, T. Joyce, M. V. Giuffrida, and S. A. Tsaftaris, Multimodal MR Synthesis via Modality-Invariant Latent Representation, IEEE Transactions on Medical Imaging, vol.37, issue.3, pp.803-814, 2018.

W. Wei, E. Poirion, B. Bodini, S. Durrleman, O. Colliot et al., Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using threedimensional fully convolutional networks for multiple sclerosis, Journal of Medical Imaging, vol.6, issue.1, pp.1-9, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02042526

N. Burgos, M. J. Cardoso, K. Thielemans, M. Modat, S. Pedemonte et al.,

J. S. Schott, D. Duncan, S. R. Atkinson, B. F. Arridge, S. Hutton et al., 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

J. Lee, A. Carass, A. Jog, C. Zhao, and J. L. Prince, Multi-atlasbased CT synthesis from conventional MRI with patch-based refinement for MRI-based radiotherapy planning, Medical Imaging 2017: Image Processing, vol.10133, pp.434-439, 2017.

T. Huynh, Y. Gao, J. Kang, L. Wang, P. Zhang et al., 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.

H. Van-nguyen, K. Zhou, and R. Vemulapalli, Cross-domain synthesis of medical images using efficient location-sensitive deep network, Medical Image Computing and Computer-Assisted Intervention -MICCAI, pp.677-684, 2015.

H. Zhang, I. J. Goodfellow, D. N. Metaxas, and A. Odena, Self-Attention Generative Adversarial Networks

P. Isola, J. Zhu, T. Zhou, and A. A. Efros, Image-to-Image Translation with Conditional Adversarial Networks, arxiv

J. Zhu, T. Park, P. Isola, and A. A. Efros, Unpaired Imageto-Image Translation using Cycle-Consistent Adversarial Networks, Computer Vision (ICCV, 2017.

H. Choi and D. S. Lee, Generation of Structural MR Images from Amyloid PET: Application to MR-Less Quantification, Journal of Nuclear Medicine, vol.59, issue.7, pp.1111-1117, 2018.

L. Bi, J. Kim, A. Kumar, D. Feng, M. ;. Fulham et al., Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment, Synthesis of Positron Emission Tomography (PET) Images via Multichannel Generative Adversarial Networks (GANs), pp.43-51, 2017.

P. Costa, A. Galdran, M. I. Meyer, M. Niemeijer, M. Abràmoff et al., End-to-End Adversarial Retinal Image Synthesis, IEEE Transactions on Medical Imaging, vol.37, issue.3, pp.781-791, 2018.

H. Zhao, H. Li, S. Maurer-stroh, and L. Cheng, Synthesizing retinal and neuronal images with generative adversarial nets, Medical Image Analysis, vol.49, pp.14-26, 2018.

Y. Hu, E. Gibson, L. Lee, W. Xie, D. C. Barratt et al., Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks, Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment, pp.105-115, 2017.

F. Mahmood, R. Chen, and N. J. Durr, Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training, IEEE Transactions on Medical Imaging, vol.37, issue.12

A. Sikka, S. V. Peri, and D. R. Bathula, MRI to FDG-PET: Cross-Modal Synthesis Using 3D U-Net for Multi-modal Alzheimer's Classification, pp.80-89, 2018.

R. Li, W. Zhang, H. Suk, L. Wang, J. Li et al., Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis, MICCAI 2014, vol.8675, pp.305-312, 2014.

Y. Pan, M. Liu, C. Lian, T. Zhou, Y. Xia et al., Synthesizing Missing PET from MRI with Cycle-consistent Generative Adversarial Networks for Alzheimer's Disease Diagnosis, Medical Image Computing and Computer Assisted Intervention, pp.455-463, 2018.

Y. Wang, L. Zhou, L. Wang, B. Yu, C. Zu et al., Locality Adaptive Multi-modality GANs for High-Quality PET Image Synthesis, pp.329-337, 2018.

W. Wei, E. Poirion, B. Bodini, S. Durrleman, N. Ayache et al., Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple sclerosis, Medical Image Analysis, vol.58, p.101546, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02276634

W. Wei, E. Poirion, B. Bodini, S. Durrleman, N. Ayache et al., Learning Myelin Content in Multiple Sclerosis from Multimodal MRI Through Adversarial Training, pp.514-522, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01810822

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones et al., Advances in Neural Information Processing Systems, vol.30, pp.5998-6008, 2017.

T. Shen, J. Jiang, T. Zhou, S. Pan, G. Long et al., DiSAN: directional self-attention network for RNN/CNN-free language understanding, The Thirty-Second AAAI Conference on Artificial Intelligence, pp.5446-5455, 2018.

A. Ambartsoumian and F. Popowich, Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp.130-139, 2018.

X. Wang, R. Girshick, A. Gupta, and K. He, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.7794-7803, 2018.

J. Fu, J. Liu, H. Tian, Y. Li, Y. Bao et al., Dual attention network for scene segmentation, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

H. Tang, D. Xu, N. Sebe, Y. Wang, J. J. Corso et al., Multi-Channel Attention Selection GAN With Cascaded Semantic Guidance for Cross-View Image Translation, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

N. Kodali, J. Abernethy, J. Hays, and Z. Kira, On Convergence and Stability of GANs, 2017.

L. Xiang, Q. Wang, D. Nie, L. Zhang, X. Jin et al., Deep embedding convolutional neural network for synthesizing CT image from t1-weighted MR image, Medical Image Analysis, vol.47, pp.31-44, 2018.

J. F. Kurtzke, Rating neurologic impairment in multiple sclerosis, Neurology, vol.33, issue.11, pp.1444-1444, 1983.

R. H. Roxburgh, S. R. Seaman, T. Masterman, A. E. Hensiek, S. J. Sawcer et al., Neurology, vol.64, issue.7, pp.1144-1151, 2005.

J. Logan, J. S. Fowler, N. D. Volkow, G. Wang, Y. Ding et al., Distribution Volume Ratios without Blood Sampling from Graphical Analysis of PET Data, Journal of Cerebral Blood Flow & Metabolism, vol.16, issue.5, pp.834-840, 1996.

M. Jenkinson, C. F. Beckmann, T. E. Behrens, M. W. Woolrich, and S. M. Smith, NeuroImage, vol.62, issue.2, pp.782-790, 2012.

B. Fischl and F. , Neuroimage, vol.62, issue.2, pp.774-781, 2012.

O. Ronneberger, P. Fischer, T. Brox, and U. , Medical Image Computing and Computer-Assisted Intervention (MICCAI), vol.9351, pp.234-241, 2015.

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2016.

S. M. Smith, Fast robust automated brain extraction, Human Brain Mapping, vol.17, issue.3, pp.143-155, 2002.

N. J. Tustison, B. B. Avants, P. A. Cook, Y. Zheng, A. Egan et al., N4itk: Improved n3 bias correction, vol.29, pp.1310-1320, 2010.

M. Veronese, B. Bodini, D. García-lorenzo, M. Battaglini, S. Bongarzone et al., Quantification of [11C]PIB PET for Imaging Myelin in the Human Brain: A Test-Retest Reproducibility Study in High-Resolution Research Tomography, Journal of Cerebral Blood Flow & Metabolism, vol.35, issue.11, pp.1771-1782, 2015.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen et al., TensorFlow: Large-scale machine learning on heterogeneous systems, 2015.

T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford et al., Advances in Neural Information Processing Systems, vol.29, pp.2234-2242, 2016.

Y. Wang, L. Zhang, and J. Van-de-weijer, Ensembles of Generative Adversarial Networks

D. Nie, R. Trullo, J. Lian, L. Wang, C. Petitjean et al., Medical Image Synthesis with Deep Convolutional Adversarial Networks, IEEE Transactions on Biomedical Engineering, vol.65, issue.12, pp.2720-2730, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02054415