Foundation Model for Cardiac MRI Reconstruction: Meeting the Real-world Challenge of Multi-center, Multi-vendor, and Multiple Diseases Challenge (CMRxRecon2025)
Creators: name: Wang, Chengyan
name: Markl, Michael
name: Prieto, Claudia
name: Qin, Chen
name: Wang, Shuo
name: Kim, Daniel
name: Young, Alistair
name: Lyu, Jun
name: Wang, He
name: Li, Hao
name: Chen, Zhensen
name: Yang, Guang
name: Wang, Fanwen
name: Qu, Xiaobo
name: Wang, Zi
name: Ouyang, Cheng
name: Zhu, Jiayu
name: Zhang, Yajing
name: Qin, Jing
name: Zhuang, Xiahai
name: Li, Yan
name: Hua, Sha
name: Wu, Lianming
name: Xu, Ziqiang
name: Chu, Ying-Hua
name: Chen, Weibo
Publisher: name: Zenodo
Year: 2024

description: Cardiac MRI (CMR) has become an essential tool for diagnosing and evaluating cardiovascular diseases, offering multi-parametric, high-resolution anatomical and functional data. CMR reconstruction from highly under-sampled k-space data has gained significant attention in recent research. Numerous AI-based image reconstructionalgorithms have shown potential in improving imaging performance and patient experience in recovering high-quality images from aggressively undersampled k-space measurements. However, the complexity and diversity of CMR scans in real-world applications, involving various image contrasts, sampling trajectories, equipment vendors, anatomical structures, and disease types, present a great challenge for existing AI-based reconstruction methods, which are usually developed for only one or a few specific scanning settings. In practice, there are often inevitable distribution mismatches between the training data and target data, due to the diversities listed above. Therefore, building and validating universal and robust reconstruction models for handling these diversities remain to be critical technical challenges for multi-parametric CMR imaging.

In our past MICCAI "CMRxRecon" challenge series (CMRxRecon2023 and CMRxRecon2024), we have released fully sampled k-space data for multi-parametric cardiac imaging from a total of 600 individuals in a single medical center, and provided relevant technical infrastructure as well as a baseline model for CMR reconstruction.

However, this dataset did not cover multi-center, multi-vendor, and multiple diseases, which are the most valued targets in real-world clinical applications. Therefore, in this proposed challenge, we aim to make an important leap towards real-world clinical scenarios by extending the challenge scope in two directions:



1) To evaluate the robustness and generalization performance of reconstruction foundation models in more than 5 centers and 10 scanners, all those data are unseen during training stage.

To evaluate the clinical performance of reconstruction foundation models under no less than 5 cardiovascular diseases (e.g., hypertrophic cardiomyopathy, dilated cardiomyopathy, myocardial infarction, coronary artery disease, arrhythmias), all those data are unseen during training stage.


A total of 600 new volunteers with multi-parametric CMR imaging (200 cases for training, 100 validation cases and 300 test cases) will be included in the proposed CMRxRecon2025 challenge, with no less than 100 cases per center. We have preliminarily set up the Synapse platform for CMRxRecon2025 and uploaded 1% of the data for review (https://www.synapse.org/#!Synapse:syn59814210/wiki/628454). For Task 1, we will release data from two centers as the training set. We will retain data from three undisclosed centers as the validation set and test set, to better evaluate the model's generalization performance across different centers. For Task 2, we will only release data from healthy volunteers as the training set and retain all five disease types as the validation set and test set, to better evaluate the model's generalization performance on previously unseen diseases.