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arXiv · 2503.03971

Towards Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge

Fanwen Wang·Zi Wang·Yan Li·Jun Lyu·Chen Qin·Shuo Wang·Kunyuan Guo·Mengting Sun·Mingkai Huang·Haoyu Zhang·Michael Tänzer·Qirong Li·Xinran Chen·Jiahao Huang·Yinzhe Wu·Haosen Zhang·Kian Anvari Hamedani·Yuntong Lyu·Longyu Sun·Qing Li·Tianxing He·Lizhen Lan·Qiong Yao·Ziqiang Xu·Bingyu Xin·Dimitris N. Metaxas·Narges Razizadeh·Shahabedin Nabavi·George Yiasemis·Jonas Teuwen·Zhenxi Zhang·Sha Wang·Chi Zhang·Daniel B. Ennis·Zhihao Xue·Chenxi Hu·Ruru Xu·Ilkay Oksuz·Donghang Lyu·Yanxin Huang·Xinrui Guo·Ruqian Hao·Jaykumar H. Patel·Guanke Cai·Binghua Chen·Yajing Zhang·Sha Hua·Zhensen Chen·Qi Dou·Xiahai Zhuang·Qian Tao·Wenjia Bai·Jing Qin·He Wang·Claudia Prieto·Michael Markl·Alistair Young·Hao Li·Xihong Hu·Lianming Wu·Xiaobo Qu·Guang Yang·Chengyan Wang
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Abstract

Cardiovascular health is vital to human well-being, and cardiac magnetic resonance (CMR) imaging is considered the {clinical reference standard} for diagnosing cardiovascular disease. However, its adoption is hindered by long scan times, complex contrasts, and inconsistent quality. While deep learning methods perform well on specific CMR imaging {sequences}, they often fail to generalize across modalities and sampling schemes. The lack of benchmarks for high-quality, fast CMR image reconstruction further limits technology comparison and adoption. The CMRxRecon2024 challenge, attracting over 200 teams from 18 countries, addressed these issues with two tasks: generalization to unseen {modalities} and robustness to diverse undersampling patterns. We introduced the largest public multi-{modality} CMR raw dataset, an open benchmarking platform, and shared code. Analysis of the best-performing solutions revealed that prompt-based adaptation and enhanced physics-driven consistency enabled strong cross-scenario performance. These findings establish principles for generalizable reconstruction models and advance clinically translatable AI in cardiovascular imaging.

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Fanwen Wang, Zi Wang, Yan Li, Jun Lyu, Chen Qin, Shuo Wang, Kunyuan Guo, Mengting Sun, Mingkai Huang, Haoyu Zhang, Michael Tänzer, Qirong Li, Xinran Chen, Jiahao Huang, Yinzhe Wu, Haosen Zhang, Kian Anvari Hamedani, Yuntong Lyu, Longyu Sun, Qing Li, Tianxing He, Lizhen Lan, Qiong Yao, Ziqiang Xu, Bingyu Xin, Dimitris N. Metaxas, Narges Razizadeh, Shahabedin Nabavi, George Yiasemis, Jonas Teuwen, Zhenxi Zhang, Sha Wang, Chi Zhang, Daniel B. Ennis, Zhihao Xue, Chenxi Hu, Ruru Xu, Ilkay Oksuz, Donghang Lyu, Yanxin Huang, Xinrui Guo, Ruqian Hao, Jaykumar H. Patel, Guanke Cai, Binghua Chen, Yajing Zhang, Sha Hua, Zhensen Chen, Qi Dou, Xiahai Zhuang, Qian Tao, Wenjia Bai, Jing Qin, He Wang, Claudia Prieto, Michael Markl, Alistair Young, Hao Li, Xihong Hu, Lianming Wu, Xiaobo Qu, Guang Yang, Chengyan Wang. 2025-12-04. Towards Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge. https://arxiv.org/abs/2503.03971

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