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International audience; Whereas the availability of data increases exponentially fast, the current visualization tools available today in Python do not scale gracefully to big data. The major plotting library in Python is Matplotlib and is more focused on the generation of static publication-ready figures than interactive visualization. These are really two different, and nearly orthogonal goals. For the former, high display quality is the major objective, whereas speed and reactivity is much more important for the latter. Matplotlib can be used for interactive visualization, but it has not been primarily designed for this. Consequently, the frame rate tends to be low on medium-size data sets, and million-points data sets can not be decently visualized in this way. Our goal is thus to create the foundations for the next-generation interactive visualization software in Python.

Endovascular aortic repair (EVAR) is a technique which uses stent grafts to treat aortic aneurysms in patients at risk of aneurysm rupture. Although this technique has been shown to be very successful on the short term, the long term results are less optimistic due to failure of the stent graft. The pulsating blood flow applies stresses and forces to the stent graft, which can cause problems such as breakage, leakage, and migration. Therefore it is of importance to gain more insight into the in vivo motion behavior of these devices. If we know more about the motion patterns in well-behaved stent graft as well as ill-behaving devices, we shall be better able to distinguish between these type of behaviors These insights will enable us to detect stent-related problems and might even be used to predict problems beforehand. Further, these insights will help in designing the next generation stent grafts. Firstly, this work discusses the applicability of ECG-gated CT for measuring the motions of stent grafts in AAA. Secondly, multiple methods to segment the stent graft from these data are discussed. Thirdly, this work proposes a method that uses image registration to apply motion to the segmented stent model

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.

For the purpose of obtaining a geometrical model of the wire frame of stent grafts, we propose three tracking methods to segment the stent's wire, and compare them in an experiment. A 2D test image was created by obtaining a projection of a 3D volume containing a stent. The image was modified to connect the parts of the stent's frame and thus create a single path. Ten versions of this image were obtained by adding different noise realizations. Each algorithm was started at the start of each of the ten images, after which the traveled paths were compared to the known correct path to determine the performance. Additionally, the algorithms were applied to 3D clinical data and visually inspected. The method based on the minimum cost path algorithm scored excellent in the experiment and showed good results on the 3D data. Future research will focus on establishing a geometrical model by determining the corner points and the crossings from the results of this method.

Purpose: ECG-gated CT enables the visualization of motions caused by the beating of the heart. Although ECG gating is frequently used in cardiac CT imaging, this technique is also very promising for evaluating vessel wall motion of the aortic artery and the motions of (stent grafts inside) abdominal aortic aneurysms (AAA). 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, these data can be valuable in predicting stent graft failure in patients. To be able to reliably quantify the motion, however, it is of importance to know the performance and limitations of ECG gating, especially when the motions are small, as is the case in AAA. Since the details of the reconstruction algorithms are proprietary information on the CT manufacturers and not in the public domain, empirical experiments are required. The goal of this study is to investigate as to what extent the motions in AAA can be measured using ECG-gated CT. The authors quantitatively investigate four aspects of motion in ECG-gated CT: The detectability of the motion of objects at different amplitudes and different periodic motions, the temporal resolution, and the volume gaps that occur as a function of heart rate. Methods: They designed an experiment on a standard static phantom to empirically determine temporal resolution. To investigate motion amplitude and frequency, as well as patient heart rate, they designed dynamic experiments in which a home-made phantom driven by a motion unit moves in a predetermined pattern. Results: The duration of each ECG-gated phase was found to be 185 ms, which corresponds to half of the rotation time and is thus in accordance with half scan reconstruction applied by the scanner. By using subpixel localization, motions become detectable from amplitudes of as small as 0.4 mm in the x direction and 0.7 mm in the z direction. With the rotation time used in this study, motions up to 2.7 Hz can be reliably detected. The reconstruction algorithm fills volume gaps with noisy data using interpolation, but objects within these gaps remain hidden. Conclusions: This study gives insight into the possibilities and limitations for measuring small motions using ECG-gated CT. Application of the experimental method is not restricted to the CT scanner of a single manufacturer. From the results, they conclude that ECG-gated CTA is a suitable technique for studying the expected motions of the stent graft and vessel wall in AAA. (C) 2009 American Association of Physicists in Medicine. [DOI: 10.1118/1.3213530]

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.

This paper addresses the problem of aligning the images of feet taken at different instances in time. We propose to use SIFT keypoints to find the geometric deformation between two photo’s. We then have a set of landmarks for each image. By finding the corresponding landmarks (i.e. matching the keypoints), we know the deformation in a sparse set of points. Using these points, we calculate a B-spline deformation model, which allows us to describe the deformation in every point in the image. The algorithm that we have developed matches the keypoints, and updates the deformation model iteratively from coarse to fine. This significantly increases the performance in terms of speed and robustness. In order to obtain enough reliable keypoints, we propose a debayering algorithm to realize a relatively high resolution in the green channel. In addition we apply processing to the image to enhance its local contrast.