Fetching the paper…
Reading the bibliography…
The core problem of Magnetic Resonance Imaging (MRI) is the trade off between acceleration and image quality.
Zhang, X., Lam, E.Y., Wu, E.X., Wong, K.K.: Application of tikhonov regularization to super-resolution reconstruction of brain mri images. In: International Conference on Medical Imaging and Informatics. pp. 51–56. Springer (2007)
2007
Earlier work this paper cites.
Collobert, R., Weston, J.: A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th international conference on Machine learning. pp. 160–167 (2008)
2008
Earlier work this paper cites.
Ravishankar, S., Bresler, Y.: Mr image reconstruction from highly undersampled k-space data by dictionary learning. IEEE transactions on medical imaging 30
2010
Earlier work this paper cites.
Shin, P.J., Larson, P.E., Ohliger, M.A., Elad, M., Pauly, J.M., Vigneron, D.B., Lustig, M.: Calibrationless parallel imaging reconstruction based on structured low-rank matrix completion. Magnetic resonance in medicine 72
2014
Earlier work this paper cites.
Wang, Y.H., Qiao, J., Li, J.B., Fu, P., Chu, S.C., Roddick, J.F.: Sparse representation-based mri super-resolution reconstruction. Measurement 47
2014
Earlier work this paper cites.
Zhan, Z., Cai, J.F., Guo, D., Liu, Y., Chen, Z., Qu, X.: Fast multiclass dictionaries learning with geometrical directions in mri reconstruction. IEEE Transactions on biomedical engineering 63
2015
Earlier work this paper cites.
Lai, Z., Qu, X., Liu, Y., Guo, D., Ye, J., Zhan, Z., Chen, Z.: Image reconstruction of compressed sensing mri using graph-based redundant wavelet transform. Medical image analysis 27
2016
Earlier work this paper cites.
Oktay, O., Bai, W., Lee, M., Guerrero, R., Kamnitsas, K., Caballero, J., de Marvao, A., Cook, S., O’Regan, D., Rueckert, D.: Multi-input cardiac image super-resolution using convolutional neural networks. In: International conference on medical image computing and computer-assisted intervention. pp. 246–254. Springer (2016)
2016
Earlier work this paper cites.
Yang, Y., Sun, J., Li, H., Xu, Z.: Deep admm-net for compressive sensing mri. In: Proceedings of the 30th international conference on neural information processing systems. pp. 10–18 (2016)
2016
Earlier work this paper cites.
Kim, S., Hori, T., Watanabe, S.: Joint ctc-attention based end-to-end speech recognition using multi-task learning. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP). pp. 4835–4839. IEEE (2017)
2017
Earlier work this paper cites.
Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp. 136–144 (2017)
2017
Earlier work this paper cites.
Nakarmi, U., Wang, Y., Lyu, J., Liang, D., Ying, L.: A kernel-based low-rank (klr) model for low-dimensional manifold recovery in highly accelerated dynamic mri. IEEE transactions on medical imaging 36
2017
Earlier work this paper cites.
Yang, G., Yu, S., Dong, H., Slabaugh, G., Dragotti, P.L., Ye, X., Liu, F., Arridge, S., Keegan, J., Guo, Y., et al.: Dagan: Deep de-aliasing generative adversarial networks for fast compressed sensing mri reconstruction. IEEE transactions on medical imaging 37
2017
Cited alongside, same era.
Chaudhari, A.S., Fang, Z., Kogan, F., Wood, J., Stevens, K.J., Gibbons, E.K., Lee, J.H., Gold, G.E., Hargreaves, B.A.: Super-resolution musculoskeletal mri using deep learning. Magnetic resonance in medicine 80
2018
Cited alongside, same era.
Chen, Y., Shi, F., Christodoulou, A.G., Xie, Y., Zhou, Z., Li, D.: Efficient and accurate mri super-resolution using a generative adversarial network and 3d multi-level densely connected network. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 91–99. Springer (2018)
2018
Cited alongside, same era.
Hammernik, K., Klatzer, T., Kobler, E., Recht, M.P., Sodickson, D.K., Pock, T., Knoll, F.: Learning a variational network for reconstruction of accelerated mri data. Magnetic resonance in medicine 79
Liu, S., Johns, E., Davison, A.J.: End-to-end multi-task learning with attention. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1871–1880 (2019)
2019
Later among the works it cites.
Lyu, Q., You, C., Shan, H., Zhang, Y., Wang, G.: Super-resolution mri and ct through gan-circle. In: Developments in X-Ray Tomography XII. vol. 11113, p. 111130X. International Society for Optics and Photonics (2019)
2019
Later among the works it cites.
Mahapatra, D., Bozorgtabar, B., Garnavi, R.: Image super-resolution using progressive generative adversarial networks for medical image analysis. Computerized Medical Imaging and Graphics 71
2019
Later among the works it cites.
Lyu, Q., Shan, H., Wang, G.: Mri super-resolution with ensemble learning and complementary priors. IEEE Transactions on Computational Imaging 6
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
Mardani, M., Gong, E., Cheng, J.Y., Vasanawala, S.S., Zaharchuk, G., Xing, L., Pauly, J.M.: Deep generative adversarial neural networks for compressive sensing mri. IEEE transactions on medical imaging 38
2018
Cited alongside, same era.
Qin, C., Schlemper, J., Caballero, J., Price, A.N., Hajnal, J.V., Rueckert, D.: Convolutional recurrent neural networks for dynamic mr image reconstruction. IEEE transactions on medical imaging 38
2018
Cited alongside, same era.
Shi, J., Liu, Q., Wang, C., Zhang, Q., Ying, S., Xu, H.: Super-resolution reconstruction of mr image with a novel residual learning network algorithm. Physics in Medicine & Biology 63
2018
Cited alongside, same era.
Zhang, M., Liu, W., Ma, H.: Joint license plate super-resolution and recognition in one multi-task gan framework. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1443–1447. IEEE (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Cai, J., Han, H., Shan, S., Chen, X.: Fcsr-gan: Joint face completion and super-resolution via multi-task learning. IEEE Transactions on Biometrics, Behavior, and Identity Science 2
2019
Cited alongside, same era.
Huang, Q., Yang, D., Wu, P., Qu, H., Yi, J., Metaxas, D.: Mri reconstruction via cascaded channel-wise attention network. In: International Symposium on Biomedical Imaging (ISBI 2019). pp. 1622–1626. IEEE (2019)
2019
Cited alongside, same era.
Wang, S., Cheng, H., Ying, L., Xiao, T., Ke, Z., Zheng, H., Liang, D.: Deepcomplexmri: Exploiting deep residual network for fast parallel mr imaging with complex convolution. Magnetic Resonance Imaging 68
2020
Later among the works it cites.
Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5791–5800 (2020)
2020
Later among the works it cites.
Feng, C.M., Fu, H., Yuan, S., Xu, Y.: Multi-contrast mri super-resolution via a multi-stage integration network. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (2021)
2021
Closest in time.
Feng, C.M., Wang, K., Lu, S., Xu, Y., Li, X.: Brain mri super-resolution using coupled-projection residual network. Neurocomputing (2021)
2021
Closest in time.
Feng, C.M., Yan, Y., Chen, G., Fu, H., Xu, Y., Shao, L.: Accelerated multi-modal mr imaging with transformers. arXiv e-prints pp. arXiv–2106 (2021)
2021
Closest in time.
Feng, C.M., Yang, Z., Chen, G., Xu, Y., Shao, L.: Dual-octave convolution for accelerated parallel mr image reconstruction. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI) (2021)
2021
Closest in time.
Feng, C.M., Yang, Z., Fu, H., Xu, Y., Yang, J., Shao, L.: Donet: Dual-octave network for fast mr image reconstruction. IEEE Transactions on Neural Networks and Learning Systems (2021)
2021
Closest in time.