The quality of cardiac magnetic resonance (CMR) imaging is susceptible to
respiratory motion artifacts. The model robustness of automated segmentation
techniques in face of real-world respiratory motion artifacts is unclear. This
manuscript describes the design of extreme cardiac MRI analysis challenge under
respiratory motion (CMRxMotion Challenge). The challenge aims to establish a
public benchmark dataset to assess the effects of respiratory motion on image
quality and examine the robustness of segmentation models. The challenge
recruited 40 healthy volunteers to perform different breath-hold behaviors
during one imaging visit, obtaining paired cine imaging with artifacts.
Radiologists assessed the image quality and annotated the level of respiratory
motion artifacts. For those images with diagnostic quality, radiologists
further segmented the left ventricle, left ventricle myocardium and right
ventricle. The images of training set (20 volunteers) along with the
annotations are released to the challenge participants, to develop an automated
image quality assessment model (Task 1) and an automated segmentation model
(Task 2). The images of validation set (5 volunteers) are released to the
challenge participants but the annotations are withheld for online evaluation
of submitted predictions. Both the images and annotations of the test set (15
volunteers) were withheld and only used for offline evaluation of submitted
containerized dockers. The image quality assessment task is quantitatively
evaluated by the Cohen's kappa statistics and the segmentation task is
evaluated by the Dice scores and Hausdorff distances.

该研究设计了一项心脏 MRI 分析挑战赛（CMRxMotion Challenge），以评估呼吸运动对图像质量的影响，并检验分割模型的稳健性，招募了 40 名志愿者进行不同的呼气行为，采集了具有伪影的短时成像，并通过定量评估绩效和对比实验，考察自动化影像质量评估和分割任务的算法效果。