Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance. - Qinqin Yang; Zi Wang 005; Kunyuan Guo; Congbo Cai; Xiaobo Qu 001 (CoRR, 2022)

CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction. - Chengyan Wang; Jun Lyu; Shuo Wang 011; Chen Qin; Kunyuan Guo; Xinyu Zhang; Xiaotong Yu; Yan Li 064; Fanwen Wang; Jianhua Jin; Zhang Shi; Ziqiang Xu; et al. (CoRR, 2023)

Physics-Driven Synthetic Data Learning for Biomedical Magnetic Resonance: The imaging physics-based data synthesis paradigm for artificial intelligence. - Qinqin Yang; Zi Wang 005; Kunyuan Guo; Congbo Cai; Xiaobo Qu 001 (IEEE Signal Process. Mag., 2023)

Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI. - Yirong Zhou; Chengyan Wang; Mengtian Lu; Kunyuan Guo; Zi Wang 005; Dan Ruan; Rui Guo; Peijun Zhao; Jianhua Wang; Naiming Wu; Jianzhong Lin; Yinyin Chen; et al. (CoRR, 2024)

CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI. - Zi Wang 005; Fanwen Wang; Chen Qin; Jun Lyu; Ouyang Cheng; Shuo Wang 011; Yan Li 064; Mengyao Yu; Haoyu Zhang; Kunyuan Guo; Zhang Shi; Qirong Li; et al. (CoRR, 2024)

The state-of-the-art in cardiac MRI reconstruction: Results of the CMRxRecon challenge in MICCAI 2023. - Jun Lyu; Chen Qin; Shuo Wang 011; Fanwen Wang; Yan Li 064; Zi Wang 005; Kunyuan Guo; Ouyang Cheng; Michael Tänzer; Meng Liu; Longyu Sun; Mengting Sun; et al. (Medical Image Anal., 2025)