Fluder-Wlodarczyk Joanna, Schneider Zofia, Pawłowski Tomasz, Wojakowski Wojciech, Gasior Pawel, Pociask Elżbieta
Division of Cardiology and Structural Heart Diseases, Medical University of Silesia in Katowice, 40-635 Katowice, Poland.
Faculty of Geology, Geophysics and Environmental Protection, AGH University of Kraków, 30-059 Krakow, Poland.
J Clin Med. 2024 Jul 25;13(15):4336. doi: 10.3390/jcm13154336.
Due to its high resolution, optical coherence tomography (OCT) is the most suitable modality for neointimal coverage assessments. Evaluation of stent healing seems crucial to accurately define their safety profile since delayed healing is connected with stent thrombosis. This study aimed to present an algorithm for automated quantitative analysis of stent strut coverage at the early stages of vessel healing in intravascular OCT. A set of 592 OCT frames from 24 patients one month following drug-eluting stent implantation was used to assess the algorithm's effectiveness. Struts not covered on any side or covered but only on one side were categorized as uncovered. The algorithm consists of several key steps: preprocessing, vessel lumen segmentation, automatic strut detection, and measurement of neointimal thickness. The proposed algorithm proved its efficiency in lumen and stent area estimation versus manual reference. It showed a high positive predictive value (PPV) (89.7%) and true positive rate (TPR) (91.4%) in detecting struts. A qualitative assessment for covered and uncovered struts was characterized by high TPR (99.1% and 80%, respectively, for uncovered and covered struts) and PPV (77.3% and 87%). The proposed algorithm demonstrated good agreement with manual measurements. Automating the stent coverage assessment might facilitate imaging analysis, which might be beneficial in experimental and clinical settings.
由于其高分辨率,光学相干断层扫描(OCT)是进行新生内膜覆盖评估最合适的方式。评估支架愈合情况对于准确界定其安全性似乎至关重要,因为愈合延迟与支架内血栓形成有关。本研究旨在提出一种算法,用于在血管愈合早期对血管内OCT图像中的支架支柱覆盖情况进行自动定量分析。使用来自24例患者在药物洗脱支架植入后一个月的592帧OCT图像来评估该算法的有效性。任何一侧未被覆盖或仅一侧被覆盖的支柱被归类为未覆盖。该算法包括几个关键步骤:预处理、血管腔分割、自动支柱检测和新生内膜厚度测量。与手动参考相比,所提出的算法在管腔和支架面积估计方面证明了其有效性。在检测支柱方面,它显示出高阳性预测值(PPV)(89.7%)和真阳性率(TPR)(91.4%)。对于覆盖和未覆盖支柱的定性评估具有高TPR(未覆盖和覆盖支柱分别为99.1%和80%)和PPV(77.3%和87%)。所提出的算法与手动测量结果显示出良好的一致性。实现支架覆盖评估的自动化可能会促进成像分析,这在实验和临床环境中可能是有益的。