Measures of atherosclerosis burden like coronary artery calcification are performed using non-contrast heart CT. However, additional information can be derived from these studies, looking beyond the coronary arteries without exposing the patients to further radiation. We present a semi-automated method to assess ascending, arch and descending aorta geometry from non-contrast CT datasets in 250 normotensive patients. We investigated the effect of aging on thoracic aorta morphometry. The algorithm identifies the aortic centerline coordinates following a toroidal path for the curvilinear portion and axial planes for descending aorta. Then it reconstructs oblique planes orthogonal to the centerline direction and a circle fitting process estimates the vessel cross-section. Finally, global thoracic aorta dimensions (diameter, volume and length) and shape (vessel curvature and tortuosity, aortic arch width and height) are calculated. From a multivariate analysis, adjusted for gender and body-size area, aortic volume and arch width were the descriptors that better represented the aortic size and shape alterations with aging. The thoracic aorta suffers an expanding and unfolding process with aging that deserves further attention to prevent aortic aneurisms.