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

• URL: http://arxiv.org/abs/2309.10836v1
• Date: Tue, 19 Sep 2023 15:14:42 GMT
• Title: CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction
• Authors: 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
• Abstract summary: There has been growing interest in deep learning-based CMR imaging algorithms. Deep learning methods require large training datasets. This dataset includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects.
• Score: 62.61209705638161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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