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Endovascular aortic replacement (EVAR) is an established technique, which uses stentgrafts to treat aortic aneurysms in patients at risk of aneurysm rupture. The long-term durability of a stentgraft is affected by the stresses and hemodynamic forces applied to it, and may be reflected by the movements of the stentgraft itself during the cardiac cycle. A conventional CT scan (which results in a 3D volume) is not able to visualize these movements. However, applying ECG-gating does provide insight in the motion of the stentgraft caused by hemodynamic forces at different phases of the cardiac cycle. The amount of data obtained is a factor of ten larger compared to conventional CT, but the radiation dose is kept similar for patient safety. This causes the data to be noisy, and streak artifacts are more common. Algorithms for automatic stentgraft detection must be able to cope with this. Segmentation of the stentgraft is performed by examining slices perpendicular to the centreline. Regions with high CT-values exist at the locations where the metallic frame penetrates the slice. These regions are well suited for detection and sub-pixel localization. Spurious points can be removed by means of a clustering algorithm, leaving only points on the contour of the stent. We compare the performance of several different point detection methods and clustering algorithms. The position of the stent’s centreline is calculated by fitting a circle through these points. The proposed method can detect several stentgraft types, and is robust against noise and streak artifacts.