Join the challenge!

1. Sign up and apply to join the challenge on the Synapse website.
2. Submit your team information here.

Download the data

Download data here.

Train the model

Participants are expected to train models in their local computational environments and to submit docker containers on Synapse platform. A leaderboard will be maintained on the Synapse platform during the validation phase.

Code availability

We provide the code to facilitate the use of the data we release at https://github.com/CmrxRecon/CMRxRecon2025.
A brief description of the provided package is as follows: 

- CMRxReconDemo: contains parallel imaging reconstruction code
- ChallengeDataFormat: explains the challenge data and the rules for data submission
- CMRxReconMaskGeneration: contains code for varied undersampling mask generation in different tasks
- Evaluation: contains image quality evaluation code for validation and testing
- Submission: contains the structure for challenge submission

Evaluation platform

Validation of the received docker on unseen test set will be performed on a cloud server with a configuration as follows:

• OS: Linux (RockyOS 9); 
• CPU: 2.0GHz, 112 cores; 
• RAM: 64 GB; 
• GPU: A6000 (48 GB VRAM, single GPU); 
• GPU Driver Version: 550;
• CUDA Version: 12.4;
• Time Limitation: 40 hours/team for each task. 

Publication references

You are free to use and/or refer to the CMRxRecon2025 challenge and datasets in your own research after the embargo period (Dec. 2025), provided that you cite the following manuscripts: 

Reference of the CMR imaging acquisition protocol:
1.Wang C, Lyu J, Wang S, et al. CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI. Scientific Data, 2024, 11(1): 687.
2.Wang Z, Wang F, Qin C, et al. CMRxRecon2024: A Multimodality, Multiview k-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI, Radiology: Artificial Intelligence. 7(2): e240443, 2025. doi: https://pubs.rsna.org/doi/10.1148/ryai.240443.
3.Lyu J, Qin C, Wang S, et al. The state-of-the-art in cardiac MRI reconstruction: Results of the CMRxRecon challenge in MICCAI 2023. Medical Image Analysis. 101: 103485, 2025. doi: https://doi.org/10.1016/j.media.2025.103485.
4.Wang C, Li Y, Lv J, et al. Recommendation for Cardiac Magnetic Resonance Imaging-Based Phenotypic Study: Imaging Part. Phenomics. 2021, 1(4): 151-170.
5.Wang S, Qin C, Wang C, et al. The Extreme Cardiac MRI Analysis Challenge under Respiratory Motion (CMRxMotion). arXiv preprint arXiv:2210.06385, 2022.

Reference for previously developed reconstruction algorithms:
1.Wang C, Jang J, Neisius U, et al. Black blood myocardial T2 mapping. Magnetic resonance in medicine. 2019, 81(1): 153-166.
2.Lyu J, Wang S, Tian Y, Zou J, Dong S, Wang C, Aviles-Rivero AI, Qin J. STADNet: Spatial-Temporal Attention-Guided Dual-Path Network for cardiac cine MRI super-resolution. Medical Image Analysis, 2024;94:103142.
3.Lyu J, Li G, Wang C, et al. Region-focused multi-view transformer-based generative adversarial network for cardiac cine MRI reconstruction. Medical Image Analysis, 2023: 102760.
4.Lyu J, Tian Y, Cai Q, Wang C*, Qin J. Adaptive channel-modulated personalized federated learning for magnetic resonance image reconstruction. Computers in Biology and Medicine, 2023, 165: 107330.
5.Qin C, Schlemper J, Caballero J, et al. Convolutional recurrent neural networks for dynamic MR image reconstruction. IEEE transactions on medical imaging, 2018, 38(1): 280-290.
6.Qin C, Duan J, Hammernik K, et al. Complementary time-frequency domain networks for dynamic parallel MR image reconstruction. Magnetic Resonance in Medicine, 2021, 86(6): 3274-3291.
7.Lyu J, Tian Y, Cai Q, et al. Adaptive channel-modulated personalized federated learning for magnetic resonance image reconstruction. Computers in Biology and Medicine, 2023, 165: 107330.
8.Lyu J, Tong X, Wang C. Parallel Imaging With a Combination of SENSE and Generative Adversarial Networks (GAN). Quantitative Imaging in Medicine and Surgery. 2020, 10(12): 2260-2273.
9.Lyu J, Sui B, Wang C, et al. DuDoCAF: Dual-Domain Cross-Attention Fusion with Recurrent Transformer for Fast Multi-contrast MR Imaging. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2022: 474-484.
10.Ouyang C, Schlemper K, et al. Generalizing Deep Learning MRI Reconstruction across Different Domains, arXiv preprint arXiv: 1902.10815, 2019.
11. Wang Z, Qian C, Guo D, Sun H, Li R, Zhao B, Qu X, One-dimensional Deep Low-rank and Sparse Network for Accelerated MRI, IEEE Transactions on Medical Imaging, 42: 79-90, 2023. https://doi.org/10.1109/TMI.2022.3203312
12. Wang Z, et al., Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI, arXiv preprint arXiv:2402.15939, 2024. https://arxiv.org/abs/2402.15939