A segmentation method for stentgrafts in the abdominal aorta from ECG-gated CTA data

Abstract

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.

Segmentation of the Retinal Vasculature within Spectral-Domain Optical Coherence Tomography Volumes of Mice

“…In the baseline method, only features generated using Gaussian filter banks are used for the process according to [15]. In single projection method, we use all features from Gaussian, Hessian, and Gabor-based features.…”

“…Gaussian filters including Gaussian derivatives are commonly used to generate features and were also adopted in the human approach [10]. In particular, we applied Gaussian filters from 0 to For Hessian-based features, a method based on point detection in [15] was used for creating filters. The filters were generated based on 2 nd order derivative Gaussian components (L xy , L xx , L yy ) in Hessian matrix and angles of rotation.…”

“…Gaussian-based features including Gaussian derivatives can be used as detecting vesselness. It is also adopted in the human approach [15]. The Gaussianbased features would apply Gaussian filters from 0 to 2 [12].…”

Motion Calculations on Stent Grafts in AAA

“…The result is a dense set of points that lie on the frame of the stent. Unfortunately, the quality of the data-defined as how well the frame of the stent is distinguishable in the data-is not always sufficient for such a method to fully segment the stent's frame (Klein et al, 2008). This quality depends on the combination of used dose, stent wire diameter, material properties of the stent (i.e.…”

“…absorption coefficient), and patient anatomy. The stent can consist of CT values as low as 300 HU (Klein et al, 2008). There are also reports of some stent types being barely distinguishable, whereas other stent types are well visible in data obtained using the same scanner settings [Submitted to Medical Image Analysis].…”