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Purpose:To provide insight into the evolution of the saddle-shaped proximal sealing rings of the Anaconda stent-graft after endovascular aneurysm repair (EVAR). Methods:Eighteen abdominal aortic aneurysm patients were consecutively enrolled in a single-center, prospective, observational cohort study (LSPEAS; Trialregister.nlidentifier NTR4276). The patients were treated electively using an Anaconda stent-graft with a mean 31% oversizing (range 17–47). According to protocol, participants were to be followed for 2 years, during which 5 noncontrast electrocardiogram-gated computed tomography scans would be conducted. Three patients were eliminated within 30 days (1 withdrew, 1 died, and a third was converted before stent-graft deployment), leaving 15 patients (mean age 72.8±3.7 years; 14 men) for this analysis. Evolution in size and shape (symmetry) of both proximal infrarenal sealing rings were assessed from discharge to 24 months using dedicated postprocessing algorithms. Results:At 24 months, the mean diameters of the first and second ring stents had increased significantly (first ring: 2.2±1.0 mm, p<0.001; second ring: 2.7±1.1 mm, p<0.001). At 6 months, the first and second rings had expanded to a mean 96.6%±2.1% and 94.8%±2.7%, respectively, of their nominal diameter, after which the rings expanded slowly; ring diameters stabilized to near nominal size (first ring, 98.3%±1.1%; second ring, 97.2%±1.4%) at 24 months irrespective of initial oversizing. No type I or III endoleaks or aneurysm-, device-, or procedure-related adverse events were noted in follow-up. The difference in the diametric distances between the peaks and valleys of the saddle-shaped rings was marked at discharge but became smaller after 24 months for both rings (first ring: median 2.0 vs 1.2 mm, p=0.191; second ring: median 2.8 vs 0.8 mm; p=0.013). Conclusion:Irrespective of initial oversizing, the Anaconda proximal sealing rings radially expanded to near nominal size within 6 months after EVAR. Initial oval-shaped rings conformed symmetrically and became nearly circular through 24 months. These findings should be taken into account in planning and follow-up.

Late stent graft failure is a serious complication in endovascular repair of aortic aneurysms. Better understanding of the motion characteristics of stent grafts will be beneficial for designing future devices. In addition, analysis of stent graft movement in individual patients in vivo can be valuable for predicting stent graft failure in these patients. To be able to gather information on stent graft motion in a quick and robust fashion, an automatic segmentation method is required. In this work we compare two segmentation methods that produce a geometric model in the form of an undirected graph. The first method tracks along the centerline of the stent and segments the stent in 2D slices sampled orthogonal to it. The second method used a modified version of the minimum cost path (MCP) method to segment the stent directly in 3D. Using annotated reference data both methods were evaluated in an experiment. The results show that the centerline-based method and the MCP-based method have an accuracy of approximately 65% and 92%, respectively. The difference in accuracy can be explained by the fact that the centerline method makes assumptions about the topology of the stent which do not always hold in practice. This causes difficulties that are hard and sometimes impossible to overcome. In contrast, the MCP-based method works directly in 3D and is capable of segmenting a large variety of stent shapes and stent types.

Multi modal image registration enables images from different modalities to be analyzed in the same coordinate system. The class of B-spline-based methods that maximize the Mutual Information between images produce satisfactory result in general, but are often complex and can converge slowly. The popular Demons algorithm, while being fast and easy to implement, produces unrealistic deformation fields and is sensitive to illumination differences between the two images, which makes it unsuitable for multi-modal registration in its original form. We propose a registration algorithm that combines a B-spline grid with deformations driven by image forces. The algorithm is easy to implement and is robust against large differences in the appearance between the images to register. The deformation is driven by attraction-forces between the edges in both images, and a B-spline grid is used to regularize the sparse deformation field. The grid is updated using an original approach by weighting the deformation forces for each pixel individually with the edge strengths. This approach makes the algorithm perform well even if not all corresponding edges are present. We report preliminary results by applying the proposed algorithm to a set of (multi-modal) test images. The results show that the proposed method performs well, but is less accurate than state of the art registration methods based on Mutual Information. In addition, the algorithm is used to register test images to manually drawn line images in order to demonstrate the algorithm's robustness.