The application of endovascular aortic aneurysm repair has expanded over the last decade. However, the long-term performance of stent grafts, in particular durable fixation and sealing to the aortic wall, remains the main concern of this treatment. The sealing and fixation are challenged at every heartbeat due to downward and radial pulsatile forces. Yet knowledge on cardiac-induced dynamics of implanted stent grafts is sparse, as it is not measured in routine clinical follow-up. Such knowledge is particularly relevant to perform fatigue tests, to predict failure in the individual patient and to improve stent graft designs. Using a physical dynamic stent graft model in an anthropomorphic phantom, we have evaluated the performance of our previously proposed segmentation and registration algorithm to detect periodic motion of stent grafts on ECG-gated (3D+t) CT data. Abdominal aortic motion profiles were simulated in two series of Gaussian based patterns with different amplitudes and frequencies. Experiments were performed on a 64-slice CT scanner with a helical scan protocol and retrospective gating. Motion patterns as estimated by our algorithm were compared to motion patterns obtained from optical camera recordings of the physical stent graft model in motion. Absolute errors of the patterns' amplitude were smaller than 0.28 mm. Even the motion pattern with an amplitude of 0.23 mm was measured, although the amplitude of motion was overestimated by the algorithm with 43%. We conclude that the algorithm performs well for measurement of stent graft motion in the mm and sub-mm range. This ultimately is expected to aid in patient-specific risk assessment and improving stent graft designs.
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
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.
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 offline
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 properties
(NPS) and frequency response (MTF). To study motion artifacts identical scans were acquired on a programmable
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
The proposed method can detect several stentgraft types, and is robust against noise and streak artifacts.