高灵敏、高对比度无标记三维光学微血管造影系统与脑科学应用研究

System of label-free three-dimensional optical coherence tomography angiography with high sensitivity and motion contrast and its applications in brain science

结合光学相干层析技术的三维成像能力和动态散射技术的运动识别能力,可以实现无标记的三维光学微血管造影,在不牺牲线扫描速度的前提下,通过帧间分析的方法提高血流造影的灵敏度,实现毛细血管水平的探测.提出小波域分量复合的方法降低静态组织信号和动态血流信号之间的分割误差,实现高对比度的血管造影分别利用组织血流模拟样品和活体大鼠脑组织进行实验验证,结果发现,采用小波域分量复合之后,血管分割误差分别减小了83%和71%,造影图对比度增强,并且具有更好的血管连接性.进而,利用研制的系统对大鼠脑血管局部缺血性中风模型进行了初步的成像研究,清晰地呈现了中风模型形成,血管受损和血管恢复的整个过程,有助于对局部缺血性中风模型机理的研究.

Combining three-dimensional （3D） imaging ability of optical coherence tomography （OCT） with movement recog-nition ability of dynamic scattering technique, label-free 3D OCT angiography can be realized, which has a wide range of applications in basic science research and clinical diagnosis. At no expense of line scanning speed, the scale of cap-illaries can be detected by improving the sensitivity through the interframe analysis. However, there exists a certain residual overlap between dynamic flow signals and static tissue beds due to a series of reasons, thus making it di？cult to completely distinguish dynamic flow signals from static tissue beds. Thus, when it comes to threshold segmentation for the blood flow signal extraction, classification error rate is inevitable, resulting in the decrease of the motion contrast of angiogram. In order to reduce classification error rate between static tissue beds and dynamic flow signals for high motion-contrast angiography, we propose a method of component compounding in wavelet domain. Three main steps are needed for this method. Firstly, on the basis of two-dimensional （2D） discrete static wavelet transform, a frame image can be decomposed into multiple levels. Each level has four components, i.e., approximation component, horizontal detail component, vertical detail component and diagonal detail component. Different decomposition levels and types of wavelet can be selected according to the demand. Secondly, the algorithm of inverse iteration compounding is used, which contains the arithmetic mean and the geometric mean of the components of adjacent decomposition levels. The adopted order for inverse iteration compounding is from the last level to the first one. The weight of the arithmetic mean to the geometric mean is one to one. In this way, four compounding components can be obtained. Thirdly, a new frame image with higher motion contrast can be obtained by using 2D discrete static wavelet inverse transform of the four compounding components. Both flow phantom and live animal experiments are performed. The results show that classification error rate decreases by 83% and 71% respectively after component compounding in wavelet domain. Besides, the angiogram has an improved motion contrast and a better vessel connectivity, which may contribute to bet-ter and wider applications of OCT angiography. Furthermore, based on the developed system, the preliminary imaging studies on the model of local stroke are conducted. In this experiment, we record the 3D data of SD mouse brain before and after the local stroke and on the tenth day. As a consequence, a clear presentation for the whole process of stroke model formation, vessel damage and vessel recovery is achieved, which may be beneficial to studying the mechanism of local stroke model.