CMRxRecon: An open cardiac MRI dataset for the competition of
  accelerated image reconstruction
Chengyan Wang Jun Lyu Shuo Wang Chen Qin Kunyuan Guo Xinyu Zhang Xiaotong Yu Yan Li Fanwen Wang Jianhua Jin Zhang Shi Ziqiang Xu Yapeng Tian Sha Hua Zhensen Chen Meng Liu Mengting Sun Xutong Kuang Kang Wang Haoran Wang Hao Li Yinghua Chu Guang Yang Wenjia Bai Xiahai Zhuang He Wang Jing Qin Xiaobo Qu
Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic
tool for cardiac diseases. However, a limitation of CMR is its slow imaging
speed, which causes patient discomfort and introduces artifacts in the images.
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 not been 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. Manual segmentations of the myocardium and chambers
of all the subjects are also provided within the dataset. Scripts of
state-of-the-art reconstruction algorithms were also provided as a point of
reference. 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. Researchers can access
the dataset at https://www.synapse.org/#!Synapse:syn51471091/wiki/.