CMRx4DFlow2026

Join The Challenge! ​

Join the challenge! ​

  1. Apply to join the challenge on synapse to get full access to the challenge files.
  1. Submit your team information hereWJX Form (https://www.wjx.top/vm/rkAd42X.aspx#)

  • After clicking the Submit button, the password for extracting the data will be displayed upon submission, as shown in the figure on the right. Please save it carefully.

Download the data ​

Download mimic data here (for testing)Data (https://www.synapse.org/Synapse:syn73710887)

Download full data here (for the challenge) Data (https://www.synapse.org/Synapse:syn73936554)

Train the model ​

Participants are expected to train models in their local computational environments and submit docker containers on the 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 4D Flow data we release: GitHub Repository (https://github.com/CmrxRecon/CMRx4DFlow2026).

A brief description of the provided package is as follows:

  • ChallengeDataFormat/: Provides an overview of the 4D Flow MRI dataset and a detailed description of the data format used in the challenge.
  • CMRx4DFlowMaskGeneration/: Contains code to generate undersampling masks for training, validation, and test data.
  • CMRx4DFlowReconDemo/: Includes demos for undersampling, Compressed Sensing reconstruction, FlowVN reconstruction, post-processing, and evaluation.
  • Submission/: Provides instructions for submitting your final results.

Evaluation platform ​

Validation of the received docker will be performed on a cloud server with the following configuration:

  • 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: 20 hours/team for each task

Publication References ​

You are free to use and/or refer to the CMRx4DFlow2026 challenge and datasets in your own research after the embargo period (Dec. 2026), provided that you cite the following manuscripts:

References of the CMRx Series Dataset

  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. DOI
  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, 2025, 7(2): e240443. DOI
  3. Wang Z, Huang M, Shi Z, et al. Enabling Ultra-Fast Cardiovascular Imaging Across Heterogeneous Clinical Environments with a Generalist Foundation Model and Multimodal Database. arXiv preprint arXiv:2512.21652, 2025. DOI

CMRx Series Challenge Summary Papers

  1. 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, 2025, 101: 103485. DOI
  2. Wang K, Qin C, Shi Z, et al. Extreme cardiac MRI analysis under respiratory motion: Results of the CMRxMotion Challenge. Medical Image Analysis, 2025: 103883. DOI
  3. Wang F, Wang Z, Li Y, et al. Towards Modality-and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge. IEEE Transactions on Medical Imaging, 2025. DOI

References for Previously Developed Algorithms by Organizers

  1. 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. DOI
  2. 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. DOI
  3. 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. DOI
  4. Wang Z, Qian C, Guo D, et al. One-dimensional Deep Low-rank and Sparse Network for Accelerated MRI, IEEE Transactions on Medical Imaging, 42: 79-90, 2023. DOI
  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. DOI
  6. Lyu J, Wang S, Tian Y, et al. STADNet: Spatial-Temporal Attention-Guided Dual-Path Network for cardiac cine MRI super-resolution. Medical Image Analysis, 2024, 94: 103142. DOI
  7. Wang Z, Xiao M, Zhou Y, et al. Deep separable spatiotemporal learning for fast dynamic cardiac MRI. IEEE Transactions on Biomedical Engineering, 2025. DOI
  8. Huang J, Yang L, Wang F, et al. Enhancing global sensitivity and uncertainty quantification in medical image reconstruction with Monte Carlo arbitrary-masked mamba. Medical Image Analysis, 2025, 99: 103334. DOI
  9. Wang Z, Yu X, Wang C, et al. One for multiple: Physics-informed synthetic data boosts generalizable deep learning for fast MRI reconstruction. Medical Image Analysis, 2025, 103: 103616. DOI
  10. Lyu J, Wang G, Wang Z, et al. Diffusion-prior based implicit neural representation for arbitrary-scale cardiac cine MRI super-resolution. Information Fusion, 2025: 103510. DOI

References of the images cited on this website

  1. Wikimedia
  2. Sandino, Christopher M., et al. Accelerated abdominal 4D flow MRI using 3D golden-angle cones trajectory. Proceedings of the Proc Ann Mtg ISMRM, Honolulu, HI, USA (2017): 22-27.
  3. Rice J, et al. In Vitro 4D Flow MRI for the Analysis of Aortic Coarctation. Proc. Intl. Soc. Mag. Reson. Med. 30 (2022): 0088. DOI
  4. Peper, Eva S., et al. 10-fold accelerated 4D flow in the carotid arteries at high spatiotemporal resolution in 7 minutes using a novel 15 channel coil. Proceedings of the 24th Annual Meeting of ISMRM, Singapore. 2016.

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