Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
561-04-013 ISMRM Abstract

Cross-Cascade Feature Aggregation for Improved Spatio-Temporal Reconstruction in Cardiac Cine MRI

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - Subsampling: Parallel imaging, compressed sensing and low-rank modeling
561-04-013 · Deep Learning Meets k-Space: New Frontiers in Reconstruction · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row B
Keywords: Cardiac cine MRI reconstruction Cross-cascade feature aggregation Feature bank Convolutional recurrent neural network Spatiotemporal information
Accepted
Donghang Lyu 1, Marius Staring1, Yiming Dong2, Jochen Keupp3, Hildo J Lamb4, Mariya Doneva3
1Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
2Department of Radiology, C.J. Gorter MRI Center, Leiden University Medical Center, Netherlands
3Philips Innovative Technologies, Hamburg, Germany
4Department of Radiology, Cardio Vascular Imaging Group, Leiden University Medical Center, Leiden, Netherlands
Presenting Author: Donghang Lyu

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References

1. Kramer, C. M., Barkhausen, J., Bucciarelli-Ducci, C., Flamm, S. D., Kim, R. J., & Nagel, E. (2020). Standardized cardiovascular magnetic resonance imaging (CMR) protocols: 2020 update. Journal of Cardiovascular Magnetic Resonance, 22(1), 17.
2. Qin, C., Schlemper, J., Caballero, J., Price, A. N., Hajnal, J. V., & Rueckert, D. (2018). Convolutional recurrent neural networks for dynamic MR image reconstruction. IEEE transactions on medical imaging, 38(1), 280-290.
3. Xin, B., Ye, M., Axel, L., & Metaxas, D. N. (2023, October). Fill the k-space and refine the image: Prompting for dynamic and multi-contrast MRI reconstruction. In International Workshop on Statistical Atlases and Computational Models of the Heart (pp. 261-273). Cham: Springer Nature Switzerland.
4. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin: Springer; 2015. p 234–241.
5. Wang, C., Lyu, J., Wang, S., Qin, C., Guo, K., Zhang, X., ... & Qu, X. (2024). CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI. Scientific Data, 11(1), 687.
6. Oppelt, A., Graumann, R., Barfuss, H., Fischer, H., Hartl, W., & Schajor, W. (1986). FISP—a new fast MRI sequence. Electromedica, 54(1), 15-18.
7. Ding, K., Ma, K., Wang, S., & Simoncelli, E. P. (2020). Image quality assessment: Unifying structure and texture similarity. IEEE transactions on pattern analysis and machine intelligence, 44(5), 2567-2581.
8. Reisenhofer, R., Bosse, S., Kutyniok, G., & Wiegand, T. (2018). A Haar wavelet-based perceptual similarity index for image quality assessment. Signal Processing: Image Communication, 61, 33-43.
9. Otazo, R., Candes, E., & Sodickson, D. K. (2015). Low‐rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magnetic resonance in medicine, 73(3), 1125-1136.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/561-04-013