Department of Radiology, Leiden University Medical Center, Postbox 9600, 2300 RC Leiden, Netherlands(1).
Comput Med Imaging Graph. 2014 Mar;38(2):113-22. doi: 10.1016/j.compmedimag.2013.08.007. Epub 2013 Sep 7.
We present a semi-automatic approach to assess the maximum circular unsupported surface area (MCUSA) of selected stent cells and the side branch access through stent cells in intravascular optical coherence tomography (IVOCT) pullback runs. Such 3D information may influence coronary interventions, stent design, blood flow analysis or prognostic evaluation. First, the stent struts are detected automatically and stent cells are reconstructed with users' assistance. Using cylinder fitting, a 2D approximation of the stent cell is generated for MCUSA detection and measurement. Next, a stent surface is reconstructed and stent-covered side branches are detected. Both the stent cell contours and side branch lumen contours are projected onto the stent surface to indicate their areas, and the overlapping regions are measured as the side branch access through these stent cells. The method was evaluated on phantom data sets and the accuracy of the MCUSA and side branch access was found to be 95% and 91%, respectively. The usability of this approach for clinical research was proved on 12 in vivo IVOCT pullback runs.
我们提出了一种半自动方法来评估血管内光学相干断层扫描（IVOCT）拉回运行中选定的支架细胞的最大圆形无支撑表面积（MCUSA）和通过支架细胞的侧支进入。这种 3D 信息可能会影响冠状动脉介入、支架设计、血流分析或预后评估。首先，自动检测支架支柱，并在用户的帮助下重建支架细胞。使用圆柱拟合，生成支架细胞的 2D 近似值，以进行 MCUSA 检测和测量。接下来，重建支架表面并检测支架覆盖的侧支。将支架细胞轮廓和侧支管腔轮廓都投影到支架表面上，以指示它们的面积，并测量重叠区域作为通过这些支架细胞的侧支进入。该方法在体模数据集上进行了评估，MCUSA 和侧支进入的准确性分别为 95%和 91%。该方法在 12 次体内 IVOCT 拉回运行中的临床研究中的可用性得到了证明。
