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首页> 外文会议>Visualization, Image-Guided Procedures pt.2; Progress in Biomedical Optics and Imaging; vol.8 no.28; Proceedings of SPIE-The International Society for Optical Engineering; vol.6509 pt.2 >Planning Image-Guided Endovascular Interventions: Guidewire Simulation using Shortest Path Algorithms
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Planning Image-Guided Endovascular Interventions: Guidewire Simulation using Shortest Path Algorithms

机译：规划图像引导的血管内干预：使用最短路径算法的导线模拟

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Endovascular interventional procedures are being used more frequently in cardiovascular surgery. Unfortunately, procedural failure, e.g., vessel dissection, may occur and is often related to improper guidewire and/or device selection. To support the surgeon's decision process and because of the importance of the guidewire in positioning devices, we propose a method to determine the guidewire path prior to insertion using a model of its elastic potential energy coupled with a representative graph construction. The 3D vessel centerline and sizes are determined for a specified vessel. Points in planes perpendicular to the vessel centerline are generated. For each pair of consecutive planes, a vector set is generated which joins all points in these planes. We construct a graph representing these vector sets as nodes. The nodes representing adjacent vector sets are joined by edges with weights calculated as a function of the angle between the corresponding vectors (nodes). The optimal path through this weighted directed graph is then determined using shortest path algorithms, such as topological sort based shortest path algorithm or Dijkstra's algorithm. Volumetric data of an internal carotid artery phantom (O 3.5mm) were acquired. Several independent guidewire (O 0.4mm) placements were performed, and the 3D paths were determined using rotational angiography. The average RMS distance between the actual and the average simulated guidewire path was 0.7mm; the computation time to determine the path was 3 seconds. The ability to predict the guidewire path inside vessels may facilitate calculation of vessel-branch access and force estimation on devices and the vessel wall.
机译：在心血管外科手术中，血管内介入手术的使用频率更高。不幸的是，可能发生程序故障，例如血管解剖，并且通常与导丝和/或装置选择不当有关。为了支持外科医生的决策过程，并且由于导丝在定位设备中的重要性，我们提出了一种在插入之前使用其弹性势能模型和代表性图形构造确定导丝路径的方法。确定3D容器的中心线和尺寸以用于指定的容器。生成垂直于血管中心线的平面中的点。对于每对连续平面，将生成一个矢量集，该矢量集将这些平面中的所有点连接在一起。我们构造一个表示这些向量集为节点的图。代表相邻向量集的节点由边连接，边的权重是根据相应向量（节点）之间的角度计算得出的。然后使用最短路径算法（例如基于拓扑排序的最短路径算法或Dijkstra算法）确定通过该加权有向图的最佳路径。获取颈内动脉幻影（O 3.5mm）的体积数据。进行了几个独立的导丝（O 0.4mm）放置，并使用旋转血管造影术确定了3D路径。实际和模拟的导线平均路径之间的平均RMS距离为0.7毫米；确定路径的计算时间为3秒。预测血管内导丝路径的能力可以促进血管分支进入的计算以及装置和血管壁上的力估计。

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