CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

Chengyan Wang, Jun Lyu, Shuo Wang, Chen Qin, Kunyuan Guo +23 more
9/19/2023
cs.CV

No official code found. Best implementation: CmrxRecon/CMRxRecon (70 stars, MIT, last commit 2026-01-26).

Abstract

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/.

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Code Implementations(1)

CmrxRecon/CMRxReconCommunity100%
MIT

Code for the Cardiac MRI Reconstruction Challenge (CMRxRecon)

7017C, ShellMay 9, 20238 months agoMIT

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Cite this paper

@article{wang2023cmrxrecon,
  title  = {CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction},
  author = {Chengyan Wang and Jun Lyu and Shuo Wang and Chen Qin and Kunyuan Guo and Xinyu Zhang and Xiaotong Yu and Yan Li and Fanwen Wang and Jianhua Jin and Zhang Shi and Ziqiang Xu and Yapeng Tian and Sha Hua and Zhensen Chen and Meng Liu and Mengting Sun and Xutong Kuang and Kang Wang and Haoran Wang and Hao Li and Yinghua Chu and Guang Yang and Wenjia Bai and Xiahai Zhuang and He Wang and Jing Qin and Xiaobo Qu},
  year   = {2023},
  eprint = {2309.10836},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.10836v1}
}

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