Towards Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge
Abstrak
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.
Topik & Kata Kunci
Penulis (63)
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
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓