mlsanityData to 14 Oct 2026

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

arXiv 2507.19165 ↗First posted 25 Jul 2025eess.IV cs.CVcited by 1

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Abstract (arXiv)

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

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 38 of its 79 references that are in the map. First posted on arXiv 25 Jul 2025.

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38 of 79 references.

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  1. 2026CLEAR: Complex Learned Explicit Analytical Regularization for Ultra-Accelerated 4D Flow CMR Reconstructioncited by 0