Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
431-03-007 / 431-03-007 ISMRM Abstract

Physics-guided Hierarchical Markovian Transformer for MRI Reconstruction

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - AI methods
431-03-007 · Image Reconstruction · Tuesday, 12 May, 4:00 PM–5:36 PM · Roof Terrace
431-03-007 · Image Reconstruction · Tuesday, 12 May, 4:00 PM–5:36 PM · Roof Terrace
Keywords: Unrolled Networks and Reconstruction Physics-driven Deep Learning Transformer Accelerated MRI reconstruction
Accepted
Valiyeh Ansarian Nezhad 1,2, Tolga Cukur1,2,3
1Dept. of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey
2National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey
3Dept. of Neuroscience, Bilkent University, Ankara, Turkey
Presenting Author: Valiyeh Ansarian Nezhad

Synopsis

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References

1. Nakarmi U, Cheng JY, Rios EP, Mardani M, Pauly JM, Ying L, Vasanawala SS, “Multi-scale unrolled deep learning framework for accelerated magnetic resonance imaging,” Proc. IEEE Int. Symp. Biomed. Imaging (ISBI), pp. 1056–1059, 2020. doi:10.1109/ISBI45749.2020.9098684 [doi]
2. Lustig M, Donoho D, Pauly JM, “Sparse MRI: The application of compressed sensing for rapid MR imaging,” Magn. Reson. Med., vol. 58, no. 6, pp. 1182–1195, 2007. doi:10.1109/isbi45749.2020.9098684 [doi]
3. Guo P, Mei Y, Zhou J, Jiang S, Patel VM, “Reconformer: Accelerated MRI reconstruction using recurrent transformer,” IEEE Trans. Med. Imaging, vol. 43, no. 1, pp. 582–593, 2023. doi:10.1109/TMI.2023.3314747 [doi]
4. Kabas B, Arslan F, Nezhad VA, Ozturk S, Saritas EU, Cukur T, “Physics-driven autoregressive state space models for medical image reconstruction,” arXiv preprint arXiv:2412.09331, 2024.
5. Hammernik K, Klatzer T, Kobler R, Recht MP, Sodickson DK, Pock T, Knoll F, “Learning a variational network for reconstruction of accelerated MRI data,” Magn. Reson. Med., vol. 79, no. 6, pp. 3055–3071, 2018. doi:10.1002/mrm.26977 [doi]
6. Zhu B, Liu JZ, Rosen BR, Rosen MS, “Image reconstruction by domain transform manifold learning,” Nature, vol. 555, no. 7697, pp. 487–492, 2018. doi:10.1038/nature25988 [doi]
7. Qin C, Schlemper J, Caballero J, Price AN, Hajnal JV, Rueckert D, “Convolutional recurrent neural networks for dynamic MR image reconstruction,” IEEE Trans. Med. Imaging, vol. 38, no. 1, pp. 280–290, 2018. doi:10.1109/TMI.2018.2863670 [doi]
8. Mardani M, Gong E, Cheng JY, Vasanawala SS, Zaharchuk G, Xing L, Pauly JM, “Deep Generative Adversarial Neural Networks for Compressive Sensing MRI,” IEEE Trans. Med. Imaging, vol. 38, no. 1, pp. 167-179, 2019. doi:10.1109/TMI.2018.2858752 [doi]
9. Akçakaya M, Moeller S, Weingärtner S, Uğurbil K, “Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction Database-free deep learning for fast imaging,” Magn. Reson. Med., vol. 81, pp. 439–453, 2019. doi:10.1002/mrm.27420 [doi]
10. Aggarwal HK, Mani MP, Jacob M, “MoDL: Model-Based Deep Learning Architecture for Inverse Problems,” IEEE Trans. Med. Imaging, vol. 38, no. 2, pp. 394–405, 2019. doi:10.1109/TMI.2018.2865356 [doi]
11. Peng X, Sutton BP, Lam F, Liang ZP, “DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning,” Magn. Reson. Med., vol. 87, no. 4, pp. 1894–1902, 2020. doi:10.1002/mrm.29085 [doi]
12. Kuestner T, Fuin N, Hammernik K, Bustin A, Qi H, Hajhosseiny R, Masci PG, Neji R, Rueckert D, Botnar RM, Prieto C, “CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions,” Sci. Rep., vol. 10, no. 1, 2020. doi:10.1038/s41598-020-70551-8 [doi]
13. Polak D, Cauley S, Bilgic B, Gong E, Bachert P, Adalsteinsson E, Setsompop K, “Joint multi-contrast variational network reconstruction (jVN) with application to rapid 2D and 3D imaging,” Magn. Reson. Med., vol. 84, no. 3, pp. 1456–1469, 2020. doi:10.1002/mrm.28219 [doi]
14. Eo T, Jun Y, Kim T, Jang J, Lee HJ, Hwang D, “KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images,” Magn. Reson. Med., vol. 80, no. 5, pp. 2188–2201, 2018. doi:10.1002/mrm.27201 [doi]
15. Dar SU, Yurt M, Shahdloo M, Ildız ME, Tınaz B, Cukur T, “Prior-Guided Image Reconstruction for Accelerated Multi-Contrast MRI via Generative Adversarial Networks,” IEEE J. Sel. Top. Signal Process., vol. 14, no. 6, pp. 1072–1087, 2020. doi:10.1109/JSTSP.2020.3001737 [doi]
16. Liu F, Feng L, Kijowski R, “MANTIS: Model-Augmented Neural neTwork with Incoherent k-space Sampling for efficient MR parameter mapping,” Magn. Reson. Med., vol. 82, no. 1, pp. 174–188, 2019. doi:10.1002/mrm.27707 [doi]
17. Oord A, Vinyals O, Kavukcuoglu K, “Neural Discrete Representation Learning,” arXiv:1711.00937, 2017.
18. Knoll F, et al., “fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning,” Rad. Artif. Intell., vol. 2, no. 1, p. e190007, 2020. doi:10.1148/ryai.2020190007 [doi]
19. Uecker M, Lai P, Murphy MJ, Virtue P, Elad M, Pauly JM, Vasanawala SS, Lustig M, “ESPIRiT--an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA,” Magn. Reson. Med., vol. 71, no. 3, pp. 990–1001, 2014. doi:10.1002/mrm.24751 [doi]
20. Schlemper J, Caballero J, Hajnal JV, Price A, Rueckert D, “A deep cascade of convolutional neural networks for MR image reconstruction,” Int. Conf. Inf. Process. Med. Imaging, pp. 647–658, 2017. doi: 10.1109/TMI.2017.2760978 [doi]
21. Guo P, et al., “Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning,” IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 2423–2432, 2021. doi:10.1109/cvpr46437.2021.00245 [doi]
22. Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y, “TransUNet: Transformers make strong encoders for medical image segmentation,” arXiv preprint arXiv:2102.04306, 2021.
23. Korkmaz Y, et al., “Self-supervised MRI reconstruction with unrolled diffusion models,” Med. Image Comput. Comput.-Assist. Interv. (MICCAI), Springer, pp. 491–501, 2023. doi:10.1007/978-3-031-43999-5_47 [doi]

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http://echo.ismrm.org/p/ISMRM2026/431-03-007