CMRxRecon2024: A Multimodality, Multiview k-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI
The released CMRxRecon2024 dataset is currently the largest and most protocol-diverse publicly available k-space dataset including multimodality and multiview cardiac MRI data from 330 healthy volunteers, and each one covers standardized and commonly used clinical protocols.
Authors
- Lian‐Ming Wu (ORCID: https://orcid.org/0000-0001-7381-5436)
- Claudia Prieto (ORCID: https://orcid.org/0000-0003-4602-2523)
- Xiahai Zhuang (ORCID: https://orcid.org/0000-0003-4351-4979)
- Mengyao Yu (ORCID: https://orcid.org/0000-0002-5346-535X)
- Fanwen Wang (ORCID: https://orcid.org/0000-0001-5491-8333)
- Zhang Shi (ORCID: https://orcid.org/0000-0001-7739-7497)
- Michael Markl (ORCID: https://orcid.org/0000-0002-7686-1128)
- Alistair A. Young (ORCID: https://orcid.org/0000-0001-5702-4220)
- Mengting Sun (ORCID: https://orcid.org/0000-0001-8022-0510)
- Wenjia Bai (ORCID: https://orcid.org/0000-0003-2943-7698)
- Xiaobo Qu (ORCID: https://orcid.org/0000-0002-8675-5820)
- Ying‐Hua Chu (ORCID: https://orcid.org/0000-0002-5574-4053)
- Q Li
- Binghua Chen (ORCID: https://orcid.org/0000-0002-2718-8416)
- Zi Wang (ORCID: https://orcid.org/0000-0001-8635-8334)
- Jun Lyu (ORCID: https://orcid.org/0009-0001-4630-1568)
- Sha Hua
- Cheng Ouyang (ORCID: https://orcid.org/0000-0002-3069-8708)
- Hao Li (ORCID: https://orcid.org/0000-0002-7712-0890)
- He Wang (ORCID: https://orcid.org/0000-0002-2053-9439)
- Chen Qin (ORCID: https://orcid.org/0009-0000-6558-6477)
- Yan Li (ORCID: https://orcid.org/0009-0000-9658-0545)
- Haoyu Zhang (ORCID: https://orcid.org/0009-0009-8851-0630)
- Guang Yang (ORCID: https://orcid.org/0000-0001-7344-7733)
- Yajing Zhang (ORCID: https://orcid.org/0000-0003-4034-0686)
- Xu Ziqiang (ORCID: https://orcid.org/0009-0001-7121-700X)
- Jing Qin (ORCID: https://orcid.org/0000-0002-7059-0929)
- Shuo Wang (ORCID: https://orcid.org/0000-0002-2947-8783)
- Li Qin (ORCID: https://orcid.org/0000-0002-7882-8970)
- Chengyan Wang (ORCID: https://orcid.org/0000-0002-8890-4973)
- Kunyuan Guo (ORCID: https://orcid.org/0000-0003-1943-3132)
- Longyu Sun (ORCID: https://orcid.org/0009-0007-3687-7997)
Institutions
- Northwestern University (US)
- Shanghai International Studies University (CN)
- Brigham and Women's Hospital (US)
- Shanghai Medical College of Fudan University (CN)
- Harvard University (US)
- Hong Kong Polytechnic University (HK)
- University of Shanghai for Science and Technology (CN)
- Sun Yat-sen University (CN)
- Pontificia Universidad Católica de Chile (CL)
- Shanghai Jiao Tong University (CN)
- King's College London (GB)
- Yantai University (CN)
- General Electric (Spain) (ES)
- Xiamen University (CN)
- Fudan University (CN)
- Renji Hospital (CN)
- Ruijin Hospital (CN)
- Millennium Science Initiative (CL)
- Shanghai Institute for Science of Science (CN)
- First Affiliated Hospital of Xiamen University (CN)
- Royal Brompton Hospital (GB)
- NIHR Royal Brompton Cardiovascular Biomedical Research Unit (GB)
- Zhongshan Hospital (CN)
- Pudong Medical Center (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Shanghai Center for Brain Science and Brain-Inspired Technology (CN)
- Millennium Institute for Integrative Biology (CL)
- Imperial College London (GB)
- Siemens (China) (CN)
Publication Details
- Journal
- Radiology Artificial Intelligence
- Published
- 2025-01-29
- DOI
- https://doi.org/10.1148/ryai.240443
- Citations
- 7
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 9.52
Funders
- Engineering and Physical Sciences Research Council