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Deep learning has shown the capability to substantially accelerate MRI reconstruction while acquiring fewer measurements.
Schlemper, J., Caballero, J., Hajnal, J.V., Price, A., Rueckert, D.: A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction. In: Information Processing in Medical Imaging. pp. 647–658. Springer International Publishing, Cham (2017)
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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
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Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 586–595 (2018)
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Song, Y., Ermon, S.: Generative Modeling by Estimating Gradients of the Data Distribution. In: Advances in Neural Information Processing Systems. vol. 32. Curran Associates, Inc. (2019)
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Ho, J., Jain, A., Abbeel, P.: Denoising Diffusion Probabilistic Models. In: Advances in Neural Information Processing Systems. vol. 33. Curran Associates, Inc. (2020)
2020
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2020
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Guo, P., Valanarasu, J.M.J., Wang, P., Zhou, J., Jiang, S., Patel, V.M.: Over-and-Under Complete Convolutional RNN for MRI Reconstruction. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. pp. 13–23. Springer International Publishing, Cham (2021)
2021
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2021
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2022
Cited alongside, same era.
2022
Chung, H., Ye, J.C.: Score-based diffusion models for accelerated MRI. Medical Image Analysis 80
2022
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2022
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Huang, J., Fang, Y., Wu, Y., Wu, H., Gao, Z., Li, Y., Ser, J.D., Xia, J., Yang, G.: Swin transformer for fast MRI. Neurocomputing 493
2022
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Korkmaz, Y., Dar, S.U.H., Yurt, M., Özbey, M., Çukur, T.: Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers. IEEE Transactions on Medical Imaging 41
2022
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Peng, C., Guo, P., Zhou, S.K., Patel, V.M., Chellappa, R.: Towards Performant and Reliable Undersampled MR Reconstruction via Diffusion Model Sampling. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. pp. 623–633. Springer Nature Switzerland, Cham (2022)
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Cited alongside, same era.
Cao, Y., Wang, L., Zhang, J., Xia, H., Yang, F., Zhu, Y.: Accelerating multi-echo MRI in k-space with complex-valued diffusion probabilistic model. In: 2022 16th IEEE International Conference on Signal Processing (ICSP). vol. 1, pp. 479–484 (2022)
2022
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Chen, E.Z., Wang, P., Chen, X., Chen, T., Sun, S.: Pyramid Convolutional RNN for MRI Image Reconstruction. IEEE Transactions on Medical Imaging 41
2022
Cited alongside, same era.
Chen, Y., Schönlieb, C.B., Liò, P., Leiner, T., Dragotti, P.L., Wang, G., Rueckert, D., Firmin, D., Yang, G.: AI-Based Reconstruction for Fast MRI-A Systematic Review and Meta-Analysis. Proceedings of the IEEE 110
2022
Cited alongside, same era.
Chung, H., Sim, B., Ye, J.C.: Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems Through Stochastic Contraction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 12413–12422 (June 2022)
2022
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2022
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Shimron, E., Tamir, J.I., Wang, K., Lustig, M.: Implicit data crimes: Machine learning bias arising from misuse of public data. Proceedings of the National Academy of Sciences 119
2022
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