冠心病是高发病率、高死亡率的疾病，目前病理学发现引发急性冠心病事件的主要元凶是“易损斑块”的破裂。然而由于传统影像方式分辨率低，无法看清斑块组成成分，其对斑块稳定性的影响及冠心病的发病机理尚未明确；而且介入治疗(PCI)中，由于无法看清植入支架的贴壁情况，容易发生支架贴壁不良而导致术后支架血栓。血管内光学相干断层扫描（IVOCT）是一种新型的空间分辨率达微米级的成像方式，有望成为分析斑块组成及评价植入支架的标准。本项目拟构建一套完整的IVOCT图像自动分析系统，首先提出高可分特征的提取方法以及高判别性分类器，对斑块组成成分的比例、形态等进行分析，为研究冠心病的发病机理提供精确有效的信息；其次提出结合底层特征和3D空间特征的高精度支架检测方法，对植入支架进行评价，为临床手术中评价支架贴壁情况提供有效的评估依据。本项目为明确冠心病的发病机理及提高手术中植入支架的准确性，探索新方法、新技术。

Coronary artery disease has very high mortality and morbidity rate. The current pathology study provides evidence that the rupture of “vulnerable plaque” is the main cause of acute coronary events. However, the traditional low-resolution imaging techniques do not have sufficient resolution to observe individual plaque component. So its effect on plaque vulnerability and the pathogenesis of coronary artery disease are unclear. Moreover, during the intervention treatment, there may be stent malapposition and then causing stent thrombosis, due to the invisibility of implanted stent. Intravascular optical coherence tomography (IVOCT), as a new imaging technique with high spatial resolution at the micron scale, is expected to become the standard for plaques composition analysis and implanted stents assessment. This research is to develop a complete automated analysis system for IVOCT images. First, distinguished features extraction and effective classifier construction methods are proposed to analysis the proportion and morphology of plaques composition. This can provide more accurate and effective information for the research of pathogenesis of coronary artery disease. Second, to assess the implanted stents, an accurate stent detection method is proposed by combining low-level features and 3D spatial features. This can provide effective evaluation basis for assessing stents position during the surgical therapy. Our objective is to understand the pathogenesis of coronary artery disease and improve the accuracy of stent implantation, and to explore new methods and technologies.