CMRx4DFlow@MICCAI 2026

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

🚀 CMRx4DFlow2026 Challenge is LIVE!
50× ultrafast 4D Flow MRI recon
🎯 Tasks
• Extreme acceleration reconstruction (10×–50×)
• Fast reconstruction under limited GPU resources
• Cross-center / cross-disease generalization
• Cross-anatomy generalization
💰 $6,400 prizes

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

📘 Excited to share that the STACOM proceedings , including both regular papers and CMRxRecon Challenge papers — has now been officially published!
🔗 https://t.co/Dg9rXR2AKR

#MICCAI #STACOM #CMRxRecon #MRI #MedicalAI #CardiacMRI

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

🔹 Submission Highlights
• Springer LNCS format
• 8 pages (+ 2 pages references)
• Double-blind peer review
• Original unpublished work only

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

📢 CMRx4DFlow Challenge Paper Submission is Open!
We warmly invite all participants and researchers to submit a challenge paper to the CMRx4DFlow Workshop @ MICCAI 2026 (https://t.co/ogEyrFm6Z0 ).

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

📢 New Post-Challenge Data Release for CMRxRecon2025
This update releases the previously unreleased evaluation datasets (https://t.co/yi9tezqZnO) that were originally reserved for challenge assessment, including disease labels and radiologist quality control (QC) annotations.

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

🔬 Model Variants
Small – 230M parameters
Base – 760M parameters (aligned with the winning configuration)
• • Large – 1.4B parameters

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

🚀In collaboration with NVIDIA, we are pleased to announce the official release of the Raw2Insights foundation model for cardiac MRI reconstruction. The largest model (1.4B parameters) was trained on 128× NVIDIA H100 GPUs.
🔗 GitHub:
https://t.co/qb9ViGpeVx

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

All training and validation data have been released — please refer to the Synapse instructions to download them (https://t.co/HOX5ajwJ4j)!

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

🚀 CMRx4DFlow2026 Challenge is LIVE!
50× ultrafast 4D Flow MRI recon
🎯 Tasks
• Extreme acceleration reconstruction (10×–50×)
• Fast reconstruction under limited GPU resources
• Cross-center / cross-disease generalization
• Cross-anatomy generalization
💰 $6,400 prizes

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

🌍Official MICCAI 2026 Challenge supported by SCMR and GE HealthCare. Organized by Fudan, Oxford, Imperial, KCL, Stanford, Northwestern, ETH Zurich, PolyU, SJTU, Tsinghua, ShanghaiTech, Xiamen, and more.

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

📊 Dataset
The largest 4D Flow raw k-space datasets
• 400+ cases
• 10+ centers
• GE / Siemens / Philips / United Imaging
• 1.5T / 3T / 5T

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

If you have any feedback regarding the website or the dataset during your experience, please let us know immediately to help us optimize and improve as soon as possible.

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

The CMRx4Dflow challenge has made its first public appearance: https://t.co/LMCvrhNKYj, we welcome everyone to get a sneak peek.
Our dataset will be released on March 1st. To enhance the participant experience, we'll to release a demo dataset before this Friday for early access.

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

The SCMR Annual Meeting kicks off next week (Feb 4–7)! We’re honored to host a joint session with MICCAI for the CMRx Challenges (https://t.co/cfkykhCtIL). On Feb 7 (local time), we’ll review past CMRx challenges, hold discussions, and introduce the upcoming 4D Flow Challenge.

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

We are very honored that our CMRxRecon summary paper has been published on TM.
I would like to thank my students Fanwen Wang, Zi Wang, and all the co-organizers and participants for their collective efforts.
We also hope everyone pay attention to our CMRxRecon2026 next year. 😊

[IEEE-TMI @IEEE_TMI]

🚨 New CHALLENGE paper alert!

🫀Deep learning models for Cardiac MRI (CMR) reconstruction often suffer from limited generalization!

🔥Discover solutions in the results of the CMRxRecon2024 challenge!
+ the largest multi-modality raw k-space dataset!

https://t.co/WH5NaTmUsp

[IEEE-TMI @IEEE_TMI]

🚨 New CHALLENGE paper alert!

🫀Deep learning models for Cardiac MRI (CMR) reconstruction often suffer from limited generalization!

🔥Discover solutions in the results of the CMRxRecon2024 challenge!
+ the largest multi-modality raw k-space dataset!

https://t.co/WH5NaTmUsp

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

Results will be announced immediately after the evaluation concludes. In addition, awards and presentations will take place during the SCMR Annual Meeting （https://t.co/UYVXJCRd6j）, February 4–6, 2026 — and we warmly welcome your participation there as well!

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

Time flies — our CMRxRecon2025 special tasks evaluation has now entered its final stage (10-day countdown)!
The Synpase platform is now open for special task Docker image submissions, and we warmly invite everyone to submit your results!

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

Please note that the prize pool for the special task is the same as that for the regular task, and the top five teams will also be invited to co-author this year’s summary paper. The submission deadline is October 30th.

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

At the same time, we encourage everyone to continue participating in our special tasks, which are open for exactly one more month (deadline: October 30, 2025).

Additionally, we warmly invite the participating teams to give invited talks at the SCMR Annual Meeting.

[CMRx4DFlow@MICCAI 2026 @CMRxRecon]

We’re delighted that our MICCAI CMRxRecon2025 workshop went smoothly—huge thanks to all the teams who participated in our challenge!

We’ve published the final results and winning teams for the regular tasks on Synapse: https://t.co/uzmbH7z2xp.