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The availability of large scale databases containing imaging and non-imagingdata, such as the UK Biobank, represents an opportunity to improve ourunderstanding of healthy and diseased bodily function. Cardiac motion atlasesprovide a space of reference in which the motion fields of a cohort of subjectscan be directly compared. In this work, a cardiac motion atlas is built fromcine MR data from the UK Biobank (~ 6000 subjects). Two automated qualitycontrol strategies are proposed to reject subjects with insufficient imagequality. Based on the atlas, three dimensionality reduction algorithms areevaluated to learn data-driven cardiac motion descriptors, and statisticalmethods used to study the association between these descriptors and non-imagingdata. Results show a positive correlation between the atlas motion descriptorsand body fat percentage, basal metabolic rate, hypertension, smoking status andalcohol intake frequency. The proposed method outperforms the ability toidentify changes in cardiac function due to these known cardiovascular riskfactors compared to ejection fraction, the most commonly used descriptor ofcardiac function. In conclusion, this work represents a framework for furtherinvestigation of the factors influencing cardiac health.

Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas