心血管疾病是威胁人类健康的头号杀手，实现对易损斑块的早期诊断是降低该病死亡率的关键。新兴的光声成像技术既可实现高分辨的结构成像，又可借助光声光谱法检测化学成份，是一种非常有应用潜力的血管内成像技术。原有的光声光谱成份分析方法，对复杂样品的定量分析不够准确，甚至会出现无法求解的情况。新近从分析化学领域引入的主成分分析法因其强大的对多成份、未知成份样本的分辨能力，非常适于进行生物组织样本的分析。但是在光声光谱图像的实际处理中，对深层组织的分辨因为光谱的畸变而出现了困难。本项目拟以原有光学分辨血管内成像系统为平台，对斑块仿体进行光声光谱成像实验；分析主成分分析法理论，对算法进行优化，使之适应光声光谱成像数据特点，提高识别和定量分析能力；通过动物模型，评估和验证新的主成分分析法的精确度。预期通过本项目的研究，将会推动易损斑块的光声图像识别的发展，并可能为研究易损斑块中的重要基础科学问题带来突破。

The mortality rate caused by cardiovascular disease is at the top of all disease. And the key to decrease it is to diagnose the vulnerable plaque earlier. Photoacoustic imaging can get high resolution structure information with the components information of the plaque together by the spectroscopic photoacoustic method, which shows the large potential in the clinical application. However, the spectroscopic photoacoustic imaging has not made the quantificational analysis practical. The Principal Component Analysis method(PCA), which has been developed in analytical chemistry for several decades, has powerful ability to analyze the multi-components or unknown-components sample, which is the common situation in biomedical sample. But when it face the photoacoustic image which contains the deep tissue information, the PCA cannot get good results because of spectrum distortion. So, in this proposal, the spectroscopic photoacoustic imaging experiments will be performed based on the optical resolution intravascular system firstly. And the PCA algorithm will be optimized after the research of the theory of PCA and characteristic of photoacoustic signal, which will develop the ability of identification and quantitative analysis. Finally, the optimized PCA algorithm will be inspected by the spectroscopic photoacoustic imaging of animal model, and the order of accuracy will be evaluated.This proposal is aimed to improve the early diagnose for the vulnerable plaque based on intravascular imaging, and to push the research on the key scientific problem of the vulnerable plaque.