Wang Ancong, Nakatani Shimpei, Eggermont Jeroen, Onuma Yoshi, Garcia-Garcia Hector M, Serruys Patrick W, Reiber Johan H C, Dijkstra Jouke
Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.
Cardialysis B.V. Rotterdam, Netherlands.
Biomed Opt Express. 2014 Sep 12;5(10):3589-602. doi: 10.1364/BOE.5.003589. eCollection 2014 Oct 1.
Bioresorbable vascular scaffolds (BVS) have gained significant interest in both the technical and clinical communities as a possible alternative to metallic stents. For accurate BVS analysis, intravascular optical coherence tomography (IVOCT) is currently the most suitable imaging technique due to its high resolution and the translucency of polymeric BVS struts for near infrared light. However, given the large number of struts in an IVOCT pullback run, quantitative analysis is only feasible when struts are detected automatically. In this paper, we present an automated method to detect and measure BVS struts based on their black cores in IVOCT images. Validated using 3 baseline and 3 follow-up data sets, the method detected 93.7% of 4691 BVS struts correctly with 1.8% false positives. In total, the Dice's coefficient for BVS strut areas was 0.84. It concludes that this method can detect BVS struts accurately and robustly for tissue coverage measurement, malapposition detection, strut distribution analysis or 3D scaffold reconstruction.
生物可吸收血管支架（BVS）作为金属支架的一种可能替代方案，在技术和临床领域都引起了极大关注。对于精确的BVS分析，血管内光学相干断层扫描（IVOCT）由于其高分辨率以及聚合物BVS支柱对近红外光的半透明性，目前是最合适的成像技术。然而，考虑到IVOCT回撤扫描中有大量支柱，只有在支柱能够自动检测时，定量分析才可行。在本文中，我们提出了一种基于IVOCT图像中BVS支柱黑色核心来检测和测量BVS支柱的自动化方法。使用3个基线数据集和3个随访数据集进行验证，该方法正确检测出4691个BVS支柱中的93.7%，误报率为1.8%。总体而言，BVS支柱面积的Dice系数为0.84。研究得出结论，该方法可准确、稳健地检测BVS支柱，用于组织覆盖测量、贴壁不良检测、支柱分布分析或三维支架重建。
