Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
360-02-013 ISMRM Abstract

An Online Re-parameterization Enhanced Convolutional Recurrent Neural Network for MRI Reconstruction

Primary: Interventional - MR-Guided Interventions
Secondary: Acquisition & Reconstruction - Image Reconstruction: AI
360-02-013 · Machine Learning for Image Reconstruction · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row A
Keywords: Reconstruction Intervention imaging Structural re-parameterization Convolutional recurrent neural network
Accepted
Sijie Zhong 1, Zhiyong Zhang1
1National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai JiaoTong University, Shanghai, China
Presenting Author: Sijie Zhong

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. C. Qin, J. Schlemper, J. Caballero, A. N. Price, J. V. Hajnal, and D. Rueckert, “Convolutional recurrent neural networks for dynamic mr image reconstruction,” IEEE transactions on medical imaging, vol. 38, no. 1, pp. 280–290, 2018, doi:10.1109/TMI.2018.2863670 [doi]
2. X. Ding, X. Zhang, J. Han, and G. Ding, “Diverse branch block: Building a convolution as an inception-like unit,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 10 886–10 895.
3. M. Hu, J. Feng, J. Hua, B. Lai, J. Huang, X. Gong, and X.-S. Hua, “Online convolutional re-parameterization,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, June 2022, pp. 568–577.
4. M. Mardani, Q. Sun, D. Donoho, V. Papyan, H. Monajemi, S. Vasanawala, and J. Pauly, “Neural proximal gradient descent for compressive imaging,” Advances in Neural Information Processing Systems, vol. 31, 2018.
5. X. Ding, X. Zhang, N. Ma, J. Han, G. Ding, and J. Sun, “Repvgg: Making vgg-style convnets great again,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 13 733–13 742.
6. H. El-Rewaidy, A. S. Fahmy, F. Pashakhanloo, X. Cai, S. Kucukseymen, I. Csecs, U. Neisius, H. Haji-Valizadeh, B. Menze, and R. Nezafat, “Multi-domain convolutional neural network (md-cnn) for radial reconstruction of dynamic cardiac mri,” Magnetic Resonance in Medicine, vol. 85, no. 3, pp. 1195–1208, 2021, doi: 10.1002/mrm.28485. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/360-02-013