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/CMRxRecon2024.
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 Task 1&2
- 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:

• CPU: Intel(R) E5-2698 @ 3.60GHz, 40 cores; 
• RAM: 128 GB; GPU: NVIDIA Tesla V100 (32 GB VRAM, single GPU); 
• GPU DRIVER: NVIDIA-DRIVER Version 545.23.06; 
• GPU CUDA: Version 12.3;
• Validation Cases: 60 cases;
• Time Limitation: 12 hours / team for each task 

Publication references

You are free to use and/or refer to the CMRxRecon2024 challenge and datasets in your own research after the embargo period (Dec 2024), provided that you cite the following manuscripts: 

Reference of the imaging acquisition protocol:
1. Wang C, Lyu J, Wang S, et al. CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction[J]. arXiv preprint arXiv:2309.10836, 2023.
2. 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.https://doi.org/10.1007/s43657-021-00018-x

Other reference (optional for citation):
1. Wang C, Jang J, Neisius U, et al. Black blood myocardial T2 mapping. Magnetic resonance in medicine. 2019, 81(1): 153-166. https://doi.org/10.1002/mrm.27360
2. Lyu J, Li G, Wang C, et al. Region-focused multi-view transformer-based generative adversarial network for cardiac cine MRI reconstruction[J]. Medical Image Analysis, 2023: 102760. https://doi.org/10.1016/j.media.2023.102760
3. 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. https://doi.org/10.1109/TMI.2018.2863670
4. 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. https://doi.org/10.1002/mrm.28917
5. 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. https://doi.org/10.21037/qims-20-518
6. 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.
7. Wang S, Qin C, Wang C, et al. The Extreme Cardiac MRI Analysis Challenge under Respiratory Motion (CMRxMotion). arXiv preprint arXiv:2210.06385, 2022.
8. Shangqi Gao, Hangqi Zhou, Yibo Gao, Xiahai Zhuang. BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability. Medical Image Analysis Volume 89, 102889, 2023 (Elsevier-MedIA 1st Prize & MICCAl Best Paper Award 2023)
9. 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
10. 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

Principal of participation

Note: Participants are not required to upload the complete training code. But teams willing to upload the original training code will be automatically entered into the code-sharing pool.

Awards

We will provide monetary awards for the top 5 winners of each task. The prize pool is exclusively sponsored by Philips.

Task 1: Multi-contrast CMR reconstruction

Task 2: Random sampling CMR reconstruction

All submissions will be reported in the leaderboard. Each participating team can participate in both tasks. However, we only present the higher reward among the two tasks to each team.
Prize-winning methods will be announced publicly as part of a scientific session at the MICCAI annual meeting.