Learning a global descriptor of cardiac motion from a large cohort of 1000+ normal subjects

Abstract

Motion, together with shape, reflect important aspects of cardiac function. In this work, a new method is proposed for learning of a cardiac motion descriptor from a data-driven perspective. The resulting descriptor can characterise the global motion pattern of the left ventricle with a much lower dimension than the original motion data. It has demonstrated its predictive power on two exemplar classification tasks on a large cohort of 1093 normal subjects.