Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
467-04-002 ISMRM Abstract

Unsupervised Patch-Based Cardiac Cine MRI Reconstruction Using Learnable Tensor Function with Implicit Neural Representation

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Analysis Methods - Data Processing
467-04-002 · Cardiovascular Imaging and Motion Correction Strategies · Tuesday, 12 May, 2:35 PM–3:30 PM · Digital Posters Row H
Keywords: Dynamic MRI Reconstruction Cardiac cine MRI reconstruction Low-rank Tensor Function Implicit Neural Representation
Accepted
Yuanbiao Yang1, Yuanyuan Liu 1, Jing Cheng1, Zhuoxu Cui2, Qingyong Zhu2, Congcong Liu2, Yuliang zhu2, Jingran Xu1, Hairong Zheng2,3, Dong Liang2, Yining Wang4, Yanjie Zhu1
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
2Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
3Nanjing University, Najing, China
4Peking Union Medical College Hospital, Beijing, China
Presenting Author: Yuanyuan Liu

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References

1. [1] Kolda T.G., Bader B.W. Tensor decompositions and applications. SIAM Review, 2009, 51(3): 455–500.
2. [2] Dabov K., Foi A., Katkovnik V., Egiazarian K. Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Transactions on Image Processing, 2007, 16: 2080–2095.
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4. [4] Lingala S.G., Hu Y., DiBella E., Jacob M. Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR. IEEE Transactions on Medical Imaging, 2011, 30(5): 1042–1054.
5. [5] Otazo R., Candès E., Sodickson D.K. Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magnetic Resonance in Medicine, 2015, 73: 1125–1136.
6. [6] Chen C., et al. OCMR (v1.0) – Open-access multi-coil k-space dataset for cardiovascular magnetic resonance imaging. arXiv preprint, 2020, arXiv:2008.03410.
7. [7] Cheng J., et al. Learning data consistency and its application to dynamic MR imaging. IEEE Transactions on Medical Imaging, 2021, 40: 3140–3153.
8. [8] Al-Haj Hemidi Z., Vogt N., Quillien L., Weihsbach C., Heinrich M.P., Oster J. CineJENSE: Simultaneous cine MRI image reconstruction and sensitivity map estimation using neural representations. In: Statistical Atlases and Computational Models of the Heart (STACOM Workshop), Springer, 2023: 467–478.
9. [9] Gottwald L.M., van Ooij P., et al. Pseudo-spiral sampling and compressed sensing reconstruction provides flexibility of temporal resolution in accelerated aortic 4D flow MRI: a comparison with k-t PCA. NMR in Biomedicine, 2020, 33(7): e4255.
10. [10] Feng L., Grimm R., Block K.T., et al. Golden-angle radial sparse parallel MRI: Combination of compressed sensing, parallel imaging, and golden-angle radial sampling. Magnetic Resonance in Medicine, 2014, 72(3): 707–717.

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http://echo.ismrm.org/p/ISMRM2026/467-04-002