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Automatic segmentation of the wire frame of stent grafts from CT data.

机译：根据CT数据自动分割支架移植物的线框。

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Endovascular aortic replacement (EVAR) is an established technique, which uses stent grafts to treat aortic aneurysms in patients at risk of aneurysm rupture. 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, we propose an automatic method to segment stent grafts from CT data, consisting of three steps: the detection of seed points, finding the connections between these points to produce a graph, and graph processing to obtain the final geometric model in the form of an undirected graph. Using annotated reference data, the method was optimized and its accuracy was evaluated. The experiments were performed using data containing the AneuRx and Zenith stent grafts. The algorithm is robust for noise and small variations in the used parameter values, does not require much memory according to modern standards, and is fast enough to be used in a clinical setting (65 and 30s for the two stent types, respectively). Further, it is shown that the resulting graphs have a 95% (AneuRx) and 92% (Zenith) correspondence with the annotated data. The geometric model produced by the algorithm allows incorporation of high level information and material properties. This enables us to study the in vivo motions and forces that act on the frame of the stent. We believe that such studies will provide new insights into the behavior of the stent graft in vivo, enables the detection and prediction of stent failure in individual patients, and can help in designing better stent grafts in the future.
机译：血管内主动脉置换术（EVAR）是一项成熟的技术，该技术使用支架移植物治疗有动脉瘤破裂风险的患者的主动脉瘤。晚期支架移植失败是主动脉瘤血管内修复的严重并发症。更好地了解支架移植物的运动特性将有助于设计未来的设备。另外，分析个体患者体内的支架移植物运动对于预测这些患者的支架移植物失败可能是有价值的。为了能够快速，可靠地收集有关支架移植物运动的信息，我们提出了一种自动方法，可从CT数据中分割支架移植物，该方法包括以下三个步骤：种子点的检测，寻找这些点之间的连接以产生支架图形和图形处理以获得无向图形式的最终几何模型。使用带注释的参考数据，对该方法进行了优化，并评估了其准确性。使用包含AneuRx和Zenith支架移植物的数据进行实验。该算法在噪声和所用参数值的微小变化方面均很健壮，根据现代标准不需要太多内存，并且足够快，可用于临床环境（两种支架类型分别为65s和30s）。此外，示出了所得图形与注释数据具有95％（AneuRx）和92％（Zenith）的对应性。该算法产生的几何模型允许合并高级信息和材料属性。这使我们能够研究作用在支架框架上的体内运动和力。我们相信，此类研究将为深入了解支架植入物的体内行为提供新的见解，能够检测和预测个别患者的支架衰竭，并有助于将来设计更好的支架植入物。

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