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arXiv · 2507.19165

Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge

Kang Wang·Chen Qin·Zhang Shi·Haoran Wang·Xiwen Zhang·Chen Chen·Cheng Ouyang·Chengliang Dai·Yuanhan Mo·Chenchen Dai·Xutong Kuang·Ruizhe Li·Xin Chen·Xiuzheng Yue·Song Tian·Alejandro Mora-Rubio·Kumaradevan Punithakumar·Shizhan Gong·Qi Dou·Sina Amirrajab·Yasmina Al Khalil·Cian M. Scannell·Lexiaozi Fan·Huili Yang·Xiaowu Sun·Rob van der Geest·Tewodros Weldebirhan Arega·Fabrice Meriaudeau·Caner Özer·Amin Ranem·John Kalkhof·İlkay Öksüz·Anirban Mukhopadhyay·Abdul Qayyum·Moona Mazher·Steven A Niederer·Carles Garcia-Cabrera·Eric Arazo·Michal K. Grzeszczyk·Szymon Płotka·Wanqin Ma·Xiaomeng Li·Rongjun Ge·Yongqing Kou·Xinrong Chen·He Wang·Chengyan Wang·Wenjia Bai·Shuo Wang
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Abstract

Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis. However, the efficacy of these models is highly dependent on the availability of high-quality, artifact-free images. In clinical practice, CMR acquisitions are frequently degraded by respiratory motion, yet the robustness of deep learning models against such artifacts remains an underexplored problem. To promote research in this domain, we organized the MICCAI CMRxMotion challenge. We curated and publicly released a dataset of 320 CMR cine series from 40 healthy volunteers who performed specific breathing protocols to induce a controlled spectrum of motion artifacts. The challenge comprised two tasks: 1) automated image quality assessment to classify images based on motion severity, and 2) robust myocardial segmentation in the presence of motion artifacts. A total of 22 algorithms were submitted and evaluated on the two designated tasks. This paper presents a comprehensive overview of the challenge design and dataset, reports the evaluation results for the top-performing methods, and further investigates the impact of motion artifacts on five clinically relevant biomarkers. All resources and code are publicly available at: https://github.com/CMRxMotion

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Kang Wang, Chen Qin, Zhang Shi, Haoran Wang, Xiwen Zhang, Chen Chen, Cheng Ouyang, Chengliang Dai, Yuanhan Mo, Chenchen Dai, Xutong Kuang, Ruizhe Li, Xin Chen, Xiuzheng Yue, Song Tian, Alejandro Mora-Rubio, Kumaradevan Punithakumar, Shizhan Gong, Qi Dou, Sina Amirrajab, Yasmina Al Khalil, Cian M. Scannell, Lexiaozi Fan, Huili Yang, Xiaowu Sun, Rob van der Geest, Tewodros Weldebirhan Arega, Fabrice Meriaudeau, Caner Özer, Amin Ranem, John Kalkhof, İlkay Öksüz, Anirban Mukhopadhyay, Abdul Qayyum, Moona Mazher, Steven A Niederer, Carles Garcia-Cabrera, Eric Arazo, Michal K. Grzeszczyk, Szymon Płotka, Wanqin Ma, Xiaomeng Li, Rongjun Ge, Yongqing Kou, Xinrong Chen, He Wang, Chengyan Wang, Wenjia Bai, Shuo Wang. 2025-07-25. Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge. https://doi.org/10.1016/j.media.2025.103883

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