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The quality of cardiac magnetic resonance (CMR) imaging is susceptible torespiratory motion artifacts. The model robustness of automated segmentationtechniques in face of real-world respiratory motion artifacts is unclear. Thismanuscript describes the design of extreme cardiac MRI analysis challenge underrespiratory motion (CMRxMotion Challenge). The challenge aims to establish apublic benchmark dataset to assess the effects of respiratory motion on imagequality and examine the robustness of segmentation models. The challengerecruited 40 healthy volunteers to perform different breath-hold behaviorsduring one imaging visit, obtaining paired cine imaging with artifacts.Radiologists assessed the image quality and annotated the level of respiratorymotion artifacts. For those images with diagnostic quality, radiologistsfurther segmented the left ventricle, left ventricle myocardium and rightventricle. The images of training set (20 volunteers) along with theannotations are released to the challenge participants, to develop an automatedimage quality assessment model (Task 1) and an automated segmentation model(Task 2). The images of validation set (5 volunteers) are released to thechallenge participants but the annotations are withheld for online evaluationof submitted predictions. Both the images and annotations of the test set (15volunteers) were withheld and only used for offline evaluation of submittedcontainerized dockers. The image quality assessment task is quantitativelyevaluated by the Cohen's kappa statistics and the segmentation task isevaluated by the Dice scores and Hausdorff distances.

The Extreme Cardiac MRI Analysis Challenge under Respiratory Motion (CMRxMotion)