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Purpose: ECG-gated CTA allows visualization of the aneurysm and stentgraft during the different phases of the cardiac cycle, although with a lower SNR per cardiac phase than without ECG gating using the same dose. In our institution, abdominal aortic aneurysm (AAA) is evaluated using non-ECG-gated CTA. Some common CT scanners cannot reconstruct a non-gated volume from ECG-gated acquired data. In order to obtain the same diagnostic image quality, we propose off-line temporal averaging of the ECG-gated data. This process, though straightforward, is fundamentally different from taking a non-gated scan, and its result will certainly differ as well. The purpose of this study is to quantitatively investigate how good off-line averaging approximates a non-gated scan. Method: Non-gated and ECG-gated CT scans have been performed on a phantom (Catphan 500). Afterwards the phases of the ECG-gated CTA data were averaged to create a third dataset. The three sets are compared with respect to noise prop-erties (NPS) and frequency response (MTF). To study motion artifacts identical scans were acquired on a programmable dynamic phantom. Results and Conclusions: The experiments show that the spatial frequency content is not affected by the averaging process. The minor differences observed for the noise properties and motion artifacts are in favor of the averaged data. Therefore the averaged ECG-gated phases can be used for diagnosis. This enables the use of ECG-gating for research on stentgrafts in AAA, without impairing clinical patient care.

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.