A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.

The study of associations between an individual's age and imaging and non-imaging data is an active research area that attempts to aid understanding of the effects and patterns of aging. In this work, we have conducted a supervoxel-wise association study between both volumetric and tissue attenuation features in coronary computed tomography angiograms (CCTA) and the chronological age of a subject, to understand the localized changes in morphology and CT attenuation (as a measure of tissue density) with age. To enable a supervoxel-wise correlation study, we developed a novel method based on image segmentation, inter-subject image registration, and robust supervoxel-based correlation analysis to achieve a statistical association study between the images and age. We evaluated the registration methodology in terms of the Dice coefficient for the heart chambers and myocardium, and the inverse consistency of the transformations, showing that the method works well in most cases with high overlap and inverse consistency. In a sex-stratified study conducted on a subset of [Formula: see text] images from the SCAPIS study, the supervoxel-wise analysis was able to find localized associations with age outside of the commonly segmented and analyzed sub-regions, and several substantial differences between the sexes in the association of age and volume.