A framework for combining a motion atlas with non‐motion information to learn clinically useful biomarkers: Application to cardiac resynchronisation therapy response prediction

&NA; We present a framework for combining a cardiac motion atlas with non‐motion data. The atlas represents cardiac cycle motion across a number of subjects in a common space based on rich motion descriptors capturing 3D displacement, velocity, strain and strain rate. The non‐motion data are derived from a variety of sources such as imaging, electrocardiogram (ECG) and clinical reports. Once in the atlas space, we apply a novel supervised learning approach based on random projections and ensemble learning to learn the relationship between the atlas data and some desired clinical output. We apply our framework to the problem of predicting response to Cardiac Resynchronisation Therapy (CRT). Using a cohort of 34 patients selected for CRT using conventional criteria, results show that the combination of motion and non‐motion data enables CRT response to be predicted with 91.2% accuracy (100% sensitivity and 62.5% specificity), which compares favourably with the current state‐of‐the‐art in CRT response prediction. HighlightsFramework to combine cardiac motion and deformation data with non‐motion data.Motion atlas use for comparison of LV displacement, velocity, strain and strain rate.Random projections and multiple kernel learning are used to extract relevant features.Application to response prediction in cardiac resynchronisation therapy.94% and 91% classification accuracy of super‐responders and non‐responders. Graphical abstract Figure. No caption available.