Extreme Cardiac MRI Analysis Under Respiratory Motion: Results of the CMRxMotion Challenge
Deep learning models achieve state-of-the-art performance in automated cardiac MRI (CMR) analysis, but their efficacy depends heavily on high-quality, artifact-free images, and their robustness to the respiratory motion artifacts common in clinical practice remains underexplored. We organized the MICCAI CMRxMotion challenge, curating and releasing a dataset of 320 CMR cine series from 40 healthy volunteers who followed controlled breathing protocols to induce a spectrum of motion artifacts. The challenge comprised two tasks, automated image-quality classification by motion severity and myocardial segmentation robust to motion, with 22 algorithms submitted and evaluated. This paper gives a comprehensive overview of the challenge design and dataset, reports evaluation results for the top-performing methods, and investigates how motion artifacts affect five clinically relevant biomarkers. All resources and code are public.
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: automated image quality assessment to classify images based on motion severity, and 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.