摘要由于脉管系统网络中小血管分布结构的改变与多种疾病有关，本文利用散斑变化OCT（Speckled Variance Optical Coherence Tomography，SV-OCT）对小血管分布成像以达到检测病症的目的。SV-OCT依赖于扫频光源OCT（Swept Source Optical Coherence Tomography, SSOCT）提供的皮肤结构图样，因此由于参考面不平整、成像系统的机械振动等原因产生的固定模式噪声会影响结构图像质量，进而导致血流分布不准确。论文在描述了实验室搭建的SSOCT系统、阐述了成像原理的基础上，通过仿真比较去除上述固定模式噪声的方法，得出了偏移后的中位数相减法具有最好的信噪比，但影响成像深度;改进后的分部最小偏差法对图像处理效果最好，但部分结构内部的噪声无法处理；区域模糊法能解决部分结构内部噪声，却影响图像清晰度。综合考虑上述滤噪方法，结合SV计算得到了高分辨率的血流分布图样。79541

毕业论文关键字：SV-OCT  SSOCT  固定模式噪声  高分辨率   

毕业设计说明书外文摘要

Title   The study of swept-source optical coherence tomography in vivo flow based on high-resolution measurement。                                                               

Abstract

The structural and functional changes within small vessels in the vasculature network are associated with various pathological conditions。 In this thesis, the imaging of the vessels distribution based on SV-OCT is studied。 The image quality of SV-OCT is dependent on the structural images of skin which are affected by roughness of the surface of the reference mirror, mechanical vibration of the optical system, resulting in fixed pattern noise and reducing accuracy of the reconstructed images of the microvasculature。 First, a handheld SSOCT system was described and its imaging principle was discussed。 By comparing simulated results of the several noise suppression methods, it was found that the median subtraction compensation method had the higher signal-to-noise ratio but the imaging depth decreased; The optimized minimal variances method had the greatest processing results but less effects on noise in inner structure; The regional blur method could remove part of inner noise, but the image contrast became worse。 After the removal of noise, the distribution of in vivo high-resolution flow was shown according to SVOCT。

Keywords: SVOCT  SSOCT  fixed pattern noise  high-resolution 

目   录

1  引言 1

1。1  OCT原理及发展过程 1

1。2  本文所做工作 3

2  SSOCT 4

2。1  SSOCT系统搭建 4

2。2  成像原理 5

2。3  成像步骤 7

2。4  图像采集与分析 8

3  固定模式噪声的处理 11

3。1  平均光谱（A-line）相减法 11

3。2  中位数值相减法 13

3。3  最小方差平均值相减法 16

3。4  分部方差平均值相减法 18

3。5  区域模糊化处理 19

4  血流沿深度的分布图样