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

TL;DR

This paper introduces the CMRxMotion challenge, a dataset and benchmark for evaluating deep learning models' robustness in cardiac MRI analysis under respiratory motion artifacts, highlighting the impact on image quality and clinical biomarkers.

Contribution

It provides a curated dataset, challenge tasks, and evaluation framework for assessing and improving deep learning methods' robustness to motion artifacts in cardiac MRI.

Findings

Top algorithms achieved varying accuracy in quality assessment and segmentation.

Motion artifacts significantly affect the accuracy of clinical biomarkers.

The challenge promotes development of more robust cardiac MRI analysis methods.

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