Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction

Caballero, J., Bai, W., Price, A.N., Rueckert, D., Hajnal, J.V.: Application-driven mri: Joint reconstruction and segmentation from undersampled mri data. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. pp. 106–113 (2014)

2014

Earlier work this paper cites.

Hollingsworth, K.G.: Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction. Physics in Medicine and Biology 60

2015

Earlier work this paper cites.

Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. pp. 234–241 (2015)

2015

Earlier work this paper cites.

Wang, S., Su, Z., Ying, L., Peng, X., Zhu, S., Liang, F., Feng, D., Liang, D.: Accelerating magnetic resonance imaging via deep learning. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). pp. 514–517 (April 2016)

2016

Earlier work this paper cites.

Iizuka, S., Simo-Serra, E., Ishikawa, H.: Globally and locally consistent image completion. ACM Trans. Graph. 36

2017

Earlier work this paper cites.

Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5967–5976 (July 2017)

2017

Cited alongside, same era.

Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., et al.: Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: Is the problem solved? IEEE Transactions on Medical Imaging 37

2018

Cited alongside, same era.

Dedmari, M.A., Conjeti, S., Estrada, S., Ehses, P., Stöcker, T., Reuter, M.: Complex fully convolutional neural networks for mr image reconstruction. In: Machine Learning for Medical Image Reconstruction. pp. 30–38 (2018)

2018

Cited alongside, same era.

Quan, T.M., Nguyen-Duc, T., Jeong, W.: Compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss. IEEE Transactions on Medical Imaging 37

2018

Cited alongside, same era.

Yang, G., Yu, S., Dong, H., Slabaugh, G., Dragotti, P.L., Ye, X., Liu, F., Arridge, S., Keegan, J., Guo, Y., Firmin, D.: Dagan: Deep de-aliasing generative adversarial networks for fast compressed sensing mri reconstruction. IEEE Transactions on Medical Imaging 37

2018

Later among the works it cites.

Khened, M., Kollerathu, V.A., Krishnamurthi, G.: Fully convolutional multi-scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Medical Image Analysis 51

2019

Closest in time.

Li, Z., Zhang, T., Zhang, D.: SEGAN: structure-enhanced generative adversarial network for compressed sensing MRI reconstruction. CoRR abs/1902.06455

Original

2019

Closest in time.

Lundervold, A.S., Lundervold, A.: An overview of deep learning in medical imaging focusing on mri. Zeitschrift für Medizinische Physik 29

2019

Closest in time.