  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.
