Hemodynamic and mechanical parameters are assumed to play an important role in explaining the initiation, growth and rupture of cerebral aneurysms. Pulsatile deformation of the vascular system due to the changes in pressure during the cardiac cycle are of high interest. Typical spatial and temporal resolution of the image data causes the quantification of geometric deformation to be challenging. In addition, flow velocity changes and the inflow of contrast agents cause vessel intensity variations. These variations in intensity can be mistaken as geometric deformations, leading to an overestimation of the true geometric deformation. In this work, a novel flow phantom is designed to generate ground-truth datasets, which are used to further investigate the relationship between intensity variations and the estimation of geometric deformation. The ground truth image data is used to investigate feasibility and limits of pulsatile deformation estimation using non-linear registration.
Hemodynamic and mechanical parameters are assumed to play an important role in explaining the initiation, growth and rupture of cerebral aneurysms. Pulsatile deformation of the vascular system due to the changes in pressure during the cardiac cycle are of high interest. Typical spatial and temporal resolution of the image data causes the quantification of geometric deformation to be challenging. In addition, flow velocity changes and the inflow of contrast agents cause vessel intensity variations. These variations in intensity can be mistaken as geometric deformations, leading to an overestimation of the true geometric deformation. In this work, a novel flow phantom is designed to generate ground-truth datasets, which are used to further investigate the relationship between intensity variations and the estimation of geometric deformation. The ground truth image data is used to investigate feasibility and limits of pulsatile deformation estimation using non-linear registration.