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We present a novel learned image reconstruction method for accelerated cardiac MRI with multiple receiver coils based on deep convolutional neural networks (CNNs) and algorithm unrolling.
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Cheng, J., Wang, H., Ying, L., Liang, D.: Model Learning: Primal Dual Networks for Fast MR Imaging. In: Lecture Notes in Computer Science. vol. 11766 LNCS, pp. 21–29. Springer (2019). https://doi.org/10/gsmqdc
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Sriram, A., Zbontar, J., Murrell, T., Defazio, A., Zitnick, C.L., Yakubova, N., Knoll, F., Johnson, P.: End-to-End Variational Networks for Accelerated MRI Reconstruction. In: Lecture Notes in Computer Science. vol. 12262 LNCS, pp. 64–73. Springer (2020). https://doi.org/10/grqghn
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Yaman, B., Hosseini, S.A.H., Moeller, S., Ellermann, J., Uğurbil, K., Akçakaya, M.: Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data. MRM 84
2020
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Monga, V., Li, Y., Eldar, Y.C.: Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing. IEEE Signal Process Mag. 38
2021
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Muckley, M.J., Riemenschneider, B., Radmanesh, A.e.a.: Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. IEEE Trans. Med. Imaging 40
2021
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Duan, J., Schlemper, J., Qin, C., Ouyang, C., Bai, W., Biffi, C., Bello, G., Statton, B., O’Regan, D.P., Rueckert, D.: Vs-net: Variable splitting network for accelerated parallel MRI reconstruction. vol. 11767 LNCS, pp. 713–722. Springer (2019). https://doi.org/10/gsmqdd
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Gotmare, A., Shirish Keskar, N., Xiong, C., Socher, R.: A closer look at deep learning heuristics: Learning rate restarts, warmup and distillation. ICLR (2019). https://doi.org/10.48550/arXiv.1810.13243
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Hauptmann, A., Arridge, S., Lucka, F., Muthurangu, V., Steeden, J.A.: Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning. MRM 81
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Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. ICLR (2019). https://doi.org/10.48550/arXiv.1711.05101
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Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic Image Synthesis With Spatially-Adaptive Normalization. In: 2019 IEEE/CVF CVPR. pp. 2332–2341 (2019). https://doi.org/10.1109/CVPR.2019.00244
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Schwab, J., Antholzer, S., Haltmeier, M.: Deep null space learning for inverse problems: Convergence analysis and rates. Inverse Prob. 35
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Knoll, F., Murrell, T., Sriram, A., Yakubova, N., Zbontar, J., Rabbat, M., Defazio, A., Muckley, M.J., Sodickson, D.K., Zitnick, C.L., Recht, M.P.: Overview of the 2019 fastMRI challenge. MRM 84
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Nichol, A., Dhariwal, P.: Improved Denoising Diffusion Probabilistic Models. Proceedings of Machine Learning Research 139
2021
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Kofler, A., Wald, C., Schaeffter, T., Haltmeier, M., Kolbitsch, C.: Convolutional Dictionary Learning by End-To-End Training of Iterative Neural Networks. In: European Signal Processing Conference. vol. 2022-August, pp. 1213–1217. IEEE (2022). https://doi.org/10/gsmqdf
2022
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Yang, C., Zhao, Y., Huang, L., Xia, L., Tao, Q.: DisQ: Disentangling Quantitative MRI Mapping of the Heart. In: MICCAI. pp. 291–300 (2022). https://doi.org/10.1007/978-3-031-16446-0_28
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Yiasemis, G., Sonke, J.J., Sanchez, C., Teuwen, J.: Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction. In: Proceedings of the IEEE CVPR. vol. 2022-June, pp. 722–731 (2022). https://doi.org/10/gq8r55
2022
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Wang, C., Lyu, J., Wang, S., Qin, C., Guo, K., Zhang, X., Yu, X., Li, Y., Wang, F., Jin, J., et al.: CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction (2023). https://doi.org/10.48550/arXiv.2309.10836
2023
Closest in time.
Zimmermann, F.F., Kolbitsch, C., Schuenke, P., Kofler, A.: PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI Reconstruction pp. 1–20 (2023). https://doi.org/10.48550/arXiv.2306.11023
2023
Closest in time.