面向加速心血管成像的模态与抽样通用学习策略：CMRxRecon2024 挑战综述
图像与视频处理
2025-12-05 v3
摘要
心血管健康对于人类福祉至关重要，心脏磁共振（CMR）成像被视为诊断心血管疾病的临床参考标准。然而，其应用受限于扫描时间长、对比复杂以及质量不一致等因素。虽然深度学习方法在特定 CMR 成像序列上表现良好，但常常难以在不同模态和抽样方案之间推广。缺乏高质量快速 CMR 图像重建的基准测试进一步限制了技术比较和采纳。CMRxRecon2024 挑战吸引了来自 18 个国家、超过 200 支团队，针对这一问题设立了两个任务：对未见模态的泛化以及对多样抽样模式的鲁棒性。我们引入了最大公开的多模态 CMR 原始数据集，搭建了开放基准平台，并共享了代码。对最佳解决方案的分析显示，基于提示的适应性和增强的物理驱动一致性实现了强大的跨情景性能。这一发现为构建可通用的重建模型奠定了原则，推动了心血管成像领域临床可转化的人工智能技术的发展。
引用
@article{arxiv.2503.03971,
title = {Towards Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge},
author = {Fanwen Wang and Zi Wang and Yan Li and Jun Lyu and Chen Qin and Shuo Wang and Kunyuan Guo and Mengting Sun and Mingkai Huang and Haoyu Zhang and Michael Tänzer and Qirong Li and Xinran Chen and Jiahao Huang and Yinzhe Wu and Haosen Zhang and Kian Anvari Hamedani and Yuntong Lyu and Longyu Sun and Qing Li and Tianxing He and Lizhen Lan and Qiong Yao and Ziqiang Xu and Bingyu Xin and Dimitris N. Metaxas and Narges Razizadeh and Shahabedin Nabavi and George Yiasemis and Jonas Teuwen and Zhenxi Zhang and Sha Wang and Chi Zhang and Daniel B. Ennis and Zhihao Xue and Chenxi Hu and Ruru Xu and Ilkay Oksuz and Donghang Lyu and Yanxin Huang and Xinrui Guo and Ruqian Hao and Jaykumar H. Patel and Guanke Cai and Binghua Chen and Yajing Zhang and Sha Hua and Zhensen Chen and Qi Dou and Xiahai Zhuang and Qian Tao and Wenjia Bai and Jing Qin and He Wang and Claudia Prieto and Michael Markl and Alistair Young and Hao Li and Xihong Hu and Lianming Wu and Xiaobo Qu and Guang Yang and Chengyan Wang},
journal= {arXiv preprint arXiv:2503.03971},
year = {2025}
}
备注
15 pages