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- Title
CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI.
- Authors
Wang, Chengyan; Lyu, Jun; Wang, Shuo; Qin, Chen; Guo, Kunyuan; Zhang, Xinyu; Yu, Xiaotong; Li, Yan; Wang, Fanwen; Jin, Jianhua; Shi, Zhang; Xu, Ziqiang; Tian, Yapeng; Hua, Sha; Chen, Zhensen; Liu, Meng; Sun, Mengting; Kuang, Xutong; Wang, Kang; Wang, Haoran
- Abstract
Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The 'CMRxRecon' dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community.
- Subjects
CARDIAC magnetic resonance imaging; DEEP learning; IMAGE reconstruction
- Publication
Scientific Data, 2024, Vol 11, Issue 1, p1
- ISSN
2052-4463
- Publication type
Article
- DOI
10.1038/s41597-024-03525-4