L. Wang, J. Wang, and B. Peng, China stroke prevention report 2016 summary, Chinese Journal of Cerebrovascular Diseases, vol.14, pp.217-224, 2016.

, Guidelines for the diagnosis and treatment of acute ischemic stroke in China, Chinese Medical Association Cerebrovascular Disease Group, vol.51, pp.666-682, 2018.

R. Krishnamoorthi, N. Ramarajan, N. Wang, B. Newman, E. Rubesova et al., Effectiveness of a staged US and CT protocol for the diagnosis of pediatric appendicitis: reducing radiation exposure in the age of ALARA, Radiology, vol.259, pp.231-239, 2011.

J. Liu, Y. Hu, J. Yang, Y. Chen, H. Shu et al., 3D Feature Constrained Reconstruction for Low Dose CT Imaging, IEEE Transactions on Circuits and Systems for Video Technology, vol.28, pp.1232-1247, 2018.

J. Bian, J. Wang, X. Han, E. Sidky, L. Shao et al., Optimization-based image reconstruction from sparse-view data in offset-detector CBCT, Physics in Medicine & Biology, vol.58, p.205, 2013.

S. Cho, E. Pearson, C. Pelizzari, and X. Pan, Region-of-interest image reconstruction with intensity weighting in circular cone-beam CT for image-guided radiation therapy, Medical physics, vol.36, pp.1184-1192, 2009.

M. Kalke and S. Siltanen, Sinogram interpolation method for sparse-angle tomography, Applied Mathematics, vol.5, p.423, 2014.

H. Zhang and J. Sonke, Directional sinogram interpolation for sparse angular acquisition in cone-beam computed tomography, Journal of X-ray science and technology, vol.21, pp.481-496, 2013.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016.

L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. Yuille, Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, vol.40, pp.834-848, 2018.

O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical image computing and computer-assisted intervention, pp.234-241, 2015.

C. Dong, C. Loy, K. He, and X. Tang, Image super-resolution using deep convolutional networks, vol.38, pp.295-307, 2016.

H. Chen, Y. Zhang, Y. Chen, J. Zhang, W. Zhang et al., LEARN: Learned experts' assessment-based reconstruction network for sparse-data CT, IEEE transactions on medical imaging, vol.37, pp.1333-1347, 2018.

D. Lee, S. Choi, and H. J. Kim, High quality imaging from sparsely sampled computed tomography data with deep learning and wavelet transform in various domains, Medical physics, vol.46, pp.104-115, 2018.

H. Lee, J. Lee, and S. Cho, View-interpolation of sparsely sampled sinogram using convolutional neural network, Image Processing. International Society for Optics and Photonics, 2017.

J. Wolterink, T. Leiner, M. Viergever, and I. I. , Generative adversarial networks for noise reduction in low-dose CT, IEEE transactions on medical imaging, vol.36, pp.2536-2545, 2017.

K. Kudo, M. Sasaki, K. Yamada, S. Momoshima, H. Utsunomiya et al., Differences in CT perfusion maps generated by different commercial software: quantitative analysis by using identical source data of acute stroke patients, Radiology, vol.254, pp.200-209, 2009.

G. Wang, A Perspective on Deep Imaging, IEEE Access, vol.4, pp.8914-8924, 2016.

J. Long, E. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.3431-3440, 2015.

A. Krizhevsky, I. Sutskever, and G. Hinton, Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012.

G. Petnehá, Recurrent neural networks for time series forecasting, 2019.

H. Chen, Y. Zhang, M. Kalra, L. Feng, C. Yang et al., Low-dose CT with a residual encoder-decoder convolutional neural network, IEEE transactions on medical imaging, vol.36, pp.2524-2535, 2017.

W. Yang, H. Zhang, J. Yang, J. Wu, X. Yin et al., Improving low-dose ct image using residual convolutional network, IEEE Access, vol.5, pp.24698-24705, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01685725

X. Yin, Q. Zhao, J. Liu, W. Yang, J. Yang et al., Domain Progressive 3D Residual Convolution Network to Improve Low Dose CT Imaging, Transactions on Medical Imaging, 2019.

D. Kingma and J. Ba, Adam: A method for stochastic optimization, 2014.

A. Radford, L. Metz, and S. Chintala, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, International Conference on Learning Representations, 2016.

I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courvile, Improved training of wasserstein gans, Advances in neural information processing systems (NIPS), 2017.

K. S. Andrew and Z. , Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations, 2015.

F. Austein, C. Riedel, T. Kerby, J. Meyne, A. Binder et al., Comparison of perfusion CT software to predict the final infarct volume after thrombectomy, Stroke, vol.47, pp.2311-2317, 2016.

K. Kudo, S. Christensen, M. Sasaki, L. Ostergaard, H. Shirato et al., Accuracy and reliability assessment of CT and MR perfusion analysis software using a digital phantom, Radiology, vol.267, pp.201-211, 2013.

J. Liu, J. Ma, Y. Zhang, Y. Chen, J. Yang et al., Discriminative feature representation to improve projection data inconsistency for low dose CT imaging, IEEE transactions on medical imaging, vol.36, pp.2499-2509, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01685727