Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge
By: Kang Wang , Chen Qin , Zhang Shi and more
Potential Business Impact:
Helps doctors see heart problems in shaky scans.
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
Similar Papers
Deformable Image Registration for Self-supervised Cardiac Phase Detection in Multi-View Multi-Disease Cardiac Magnetic Resonance Images
CV and Pattern Recognition
Finds heart beats better in medical scans.
CardioMorphNet: Cardiac Motion Prediction Using a Shape-Guided Bayesian Recurrent Deep Network
CV and Pattern Recognition
Helps doctors see heart movement better.
Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction
CV and Pattern Recognition
Cleans up blurry MRI scans for better pictures.