References
1. M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: The application of compressed sensing for rapid MR imaging,” Magnetic Resonance in Medicine, vol. 58, no. 6, pp. 1182–1195, Oct. 2007, doi: 10.1002/mrm.21391.
[doi]
2. H. K. Aggarwal, M. P. Mani, and M. Jacob, “MoDL: Model-Based Deep Learning Architecture for Inverse Problems,” IEEE Transactions on Medical Imaging, vol. 38, no. 2, pp. 394–405, Aug. 2018, doi: 10.1109/tmi.2018.2865356.
[doi]
3. B. Yaman, S. A. H. Hosseini, S. Moeller, J. Ellermann, K. Uğurbil, and M. Akçakaya, “Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data,” Magnetic Resonance in Medicine, vol. 84, no. 6, pp. 3172–3191, Jul. 2020, doi: 10.1002/mrm.28378.
[doi]
4. K. Hammernik et al., “Learning a variational network for reconstruction of accelerated MRI data,” Magnetic Resonance in Medicine, vol. 79, no. 6, pp. 3055–3071, Nov. 2017, doi: 10.1002/mrm.26977.
[doi]
5. M. Mardani et al., “Deep Generative Adversarial Neural Networks for Compressive Sensing MRI,” IEEE Transactions on Medical Imaging, vol. 38, no. 1, pp. 167–179, Jul. 2018, doi: 10.1109/tmi.2018.2858752.
[doi]
6. J. Schlemper, J. Caballero, J. V. Hajnal, A. Price, and D. Rueckert, “A deep cascade of convolutional neural networks for MR image reconstruction,” in Lecture notes in computer science, 2017, pp. 647–658. doi: 10.1007/978-3-319-59050-9_51.
[doi]
7. S. A. H. Hosseini, B. Yaman, S. Moeller, M. Hong, and M. Akcakaya, “Dense Recurrent neural networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms,” IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 6, pp. 1280–1291, Jun. 2020, doi: 10.1109/jstsp.2020.3003170.
[doi]
8. A. Jalal, M. Arvinte, G. Daras, E. Price, A. G. Dimakis, and J. I. Tamir, “Robust Compressed Sensing MRI with Deep Generative Priors,” Neural Information Processing Systems, vol. 34, Dec. 2021.
9. Y. Korkmaz, S. U. H. Dar, M. Yurt, M. Ozbey, and T. Cukur, “Unsupervised MRI reconstruction via Zero-Shot learned adversarial transformers,” IEEE Transactions on Medical Imaging, vol. 41, no. 7, pp. 1747–1763, Jan. 2022, doi: 10.1109/tmi.2022.3147426.
[doi]
10. P. Guo, Y. Mei, J. Zhou, S. Jiang, and V. M. Patel, “ReconFormer: Accelerated MRI Reconstruction using Recurrent Transformer,” IEEE Transactions on Medical Imaging, vol. 43, no. 1, pp. 582–593, Sep. 2023, doi: 10.1109/tmi.2023.3314747.
[doi]
11. B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, and M. S. Rosen, “Image reconstruction by domain-transform manifold learning,” Nature, vol. 555, no. 7697, pp. 487–492, Mar. 2018, doi: 10.1038/nature25988.
[doi]
12. G. Oh, B. Sim, H. Chung, L. Sunwoo, and J. C. Ye, “Unpaired deep learning for accelerated MRI using optimal transport driven CycleGAN,” IEEE Transactions on Computational Imaging, vol. 6, pp. 1285–1296, Jan. 2020, doi: 10.1109/tci.2020.3018562.
[doi]
13. K. Lei, M. Mardani, J. M. Pauly, and S. S. Vasanawala, “Wasserstein GANs for MR Imaging: From paired to Unpaired training,” IEEE Transactions on Medical Imaging, vol. 40, no. 1, pp. 105–115, Sep. 2020, doi: 10.1109/tmi.2020.3022968.
[doi]
14. D. Narnhofer, K. Hammernik, F. Knoll, and T. Pock, “Inverse GANs for accelerated MRI reconstruction,” in Wavelets and Sparsity XVIII, 2019, vol. 11138, pp. 381–392, doi: 10.1117/12.2527753
[doi]
15. S. U. H. Dar, M. Özbey, A. B. Çatlı, and T. Çukur, “A Transfer‐Learning approach for accelerated MRI using deep neural networks,” Magnetic Resonance in Medicine, vol. 84, no. 2, pp. 663–685, Jan. 2020, doi: 10.1002/mrm.28148.
[doi]
16. S. Wang et al., “Accelerating magnetic resonance imaging via deep learning,” 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 514–517, Apr. 2016, doi: 10.1109/isbi.2016.7493320.
[doi]
17. G. Yang et al., “DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction,” IEEE Transactions on Medical Imaging, vol. 37, no. 6, pp. 1310–1321, Dec. 2017, doi: 10.1109/tmi.2017.2785879.
[doi]
18. P. Guo, P. Wang, J. Zhou, S. Jiang, and V. M. Patel, “Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning,” 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2021, doi: 10.1109/cvpr46437.2021.00245.
[doi]
19. X. Peng, B. P. Sutton, F. Lam, and Z. Liang, “DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning,” Magnetic Resonance in Medicine, vol. 87, no. 4, pp. 1894–1902, Nov. 2021, doi: 10.1002/mrm.29085.
[doi]
20. T. Küstner et al., “CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions,” Scientific Reports, vol. 10, no. 1, Aug. 2020, doi: 10.1038/s41598-020-70551-8.
[doi]
21. D. Polak et al., “Joint multi‐contrast variational network reconstruction (jVN) with application to rapid 2D and 3D imaging,” Magnetic Resonance in Medicine, vol. 84, no. 3, pp. 1456–1469, Mar. 2020, doi: 10.1002/mrm.28219.
[doi]
22. T. Eo, Y. Jun, T. Kim, J. Jang, H. Lee, and D. Hwang, “KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images,” Magnetic Resonance in Medicine, vol. 80, no. 5, pp. 2188–2201, Apr. 2018, doi: 10.1002/mrm.27201.
[doi]
23. F. Liu, L. Feng, and R. Kijowski, “MANTIS: Model‐Augmented Neural neTwork with Incoherent k‐space Sampling for efficient MR parameter mapping,” Magnetic Resonance in Medicine, vol. 82, no. 1, pp. 174–188, Mar. 2019, doi: 10.1002/mrm.27707.
[doi]
24. Q. Liu, Q. Yang, H. Cheng, S. Wang, M. Zhang, and D. Liang, “Highly undersampled magnetic resonance imaging reconstruction using autoencoding priors,” Magnetic Resonance in Medicine, vol. 83, no. 1, pp. 322–336, Aug. 2019, doi: 10.1002/mrm.27921.
[doi]
25. G. Luo, M. Blumenthal, M. Heide, and M. Uecker, “Bayesian MRI reconstruction with joint uncertainty estimation using diffusion models,” Magnetic Resonance in Medicine, vol. 90, no. 1, pp. 295–311, Mar. 2023, doi: 10.1002/mrm.29624.
[doi]
26. J. Huang et al., “Swin transformer for fast MRI,” Neurocomputing, vol. 493, pp. 281–304, Apr. 2022, doi: 10.1016/j.neucom.2022.04.051.
[doi]
27. C.-M. Feng et al., “Multimodal transformer for accelerated MR imaging,” IEEE Transactions on Medical Imaging, vol. 42, no. 10, pp. 2804–2816, Jun. 2022, doi: 10.1109/tmi.2022.3180228.
[doi]
28. B. Zhou et al., “DSFormer: a dual-domain self-supervised transformer for accelerated multi-contrast MRI reconstruction,” 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 4955–4964, Jan. 2023, doi: 10.1109/wacv56688.2023.00494.
[doi]
29. Y. Korkmaz, T. Cukur, and V. M. Patel, “Self-supervised MRI Reconstruction with Unrolled Diffusion Models,” in Lecture notes in computer science, 2023, pp. 491–501. doi: 10.1007/978-3-031-43999-5_47.
[doi]
30. L. Zhu, B. Liao, Q. Zhang, X. Wang, W. Liu, and X. Wang, “Vision mamba: efficient visual representation learning with bidirectional state space model,” in International Conference on Machine Learning, 2024, doi: 10.5555/3692070.3694654
[doi]
31. Y. Liu et al., “Vmamba: Visual state space model,” Neural Information Processing Systems, vol. 37, pp. 103031–103063, 2024.
32. J. Huang et al., “Enhancing global sensitivity and uncertainty quantification in medical image reconstruction with Monte Carlo arbitrary-masked mamba,” Medical Image Analysis, vol. 99, p. 103334, Sep. 2024, doi: 10.1016/j.media.2024.103334.
[doi]
33. Y. Korkmaz and V. M. Patel, “MambaRecon: MRI Reconstruction with Structured State Space Models,” 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 4142–4152, Feb. 2025, doi: 10.1109/wacv61041.2025.00407.
[doi]
34. F. Knoll et al., “FastMRI: a publicly available raw K-Space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning,” Radiology Artificial Intelligence, vol. 2, no. 1, p. e190007, Jan. 2020, doi: 10.1148/ryai.2020190007.
[doi]