激光散斑信号处理方法研究
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摘要
了解脑组织不同成分在正常生理状态下或者病理状态下的动态响应,对研究神经血管耦合机制、脑部疾病的诊断预防、药物评估以及术中监测等都有重大意义。激光散斑衬比成像技术以其简单的成像系统、高时空分辨率、无需扫描的且非接触的二维流速全场成像等特点,在脑皮层功能研究、视网膜疾病和皮肤疾病的诊断和治疗、药物评价等领域的血流动态变化检测中取得了重大应用,是生命科学基础研究和临床诊断的重要工具。但是该技术本身不具备动静脉分割能力,不能独立的实现脑皮层不同成分的血流变化分析。基于光学影像的动静脉分割技术是提取组织不同成分的一个有效技术手段,对于基础研究、临床诊断、术中监测等有很重大的意义。不过,目前的动静脉分割技术和激光散斑衬比成像技术结合,会出现很多问题,比如降低系统时间分辨率、使成像系统结构变得复杂。因此,本文对激光散斑数据所携带的信息进行进一步分析：通过分析激光散斑强度的分布,提出了基于单波长激光散斑相对最小反射率(Relative temporal minimum reflectance, RTMR)分析的动静脉分割法,该方法具有简单、自动以及准确率高等优点。该技术结合激光散斑衬比成像,在不改变激光散斑成像系统地前提下,实现了脑皮层不同组织成分独立的血流变化分析。主要内容如下：
(1)提出一种自动有效的基于单波长激光散斑相对最小反射率分析的动静脉分割法。衬比值比较小,并且CCD记录的散斑图像互相统计独立时,Rayleigh分布函数是实际的积分散斑强度概率密度函数的一个很好的近似。通过Rayleigh分布函数得出激光散斑最小光强的表达式,发现它是平均光强和血流速度的整合参数。在激光散斑最小光强图内,根据激光散斑最小光强值,对血管进行分类。不过由于平均光强分布和入射光强有关,因此激光散斑最小光强存在背景不均匀现象,为此引入激光散斑相对最小反射率这个参量。空间任意一点的激光散斑相对最小反射率定义为：该点的激光散斑最小光强除以该点在激光散斑时域内均图内的邻域的非血管脑皮层组织的平均光强。在RTMR图中：动脉血管区域值最大；静脉血管区域值相对最小,部分可能和非血管皮层区域重叠；非血管脑皮层区域的RTMR值在了两类血管之间。为了避免误判静脉区域,所以先通过RTMR值采用多阈值法逐个像素的提取动脉网络(即对于血管内的一个像素,如果它的RTMR值比它的邻域内非血管脑组织像素的RTMR值平均值大,则该像素属于动脉),然后从血管网络图中减去动脉结构,得出静脉网络。实验证明基于单波长激光散斑相对最小反射率分析的动静脉分割法的准确率高,动脉的PTR (True Positive rate)是98.5%,静脉的PTR是95%,动脉的误判率是1.5%,静脉的误判率是5%。同时分析了激光散斑相对最小反射率分析的有效波长,成像深度等参数,其中最佳波长可能是600nm,成像深度大概是几百微米。
(2)基于单波长激光散斑相对最小反射率分析的动静脉分割法和激光散斑衬比成像技术结合,实现大鼠脑皮层不同成分组织在CSD模型中的独自的血流变化分析。准确地显示了微小区域内,微动脉、微静脉以及非血管脑皮层组织各自在CSD过程中的血流变化情况。
Understanding the responses of different cerebral tissue compartments under normal or abnormal physiological conditions is considerably important to basic researches and clinical diagnostics, such as studying the mechanism of neurovascular coupling, diagnosis and prevention of cerebral disorders, evaluating drug efficiency and intraoperative imaging. Laser speckle contrast imaging (LSCI) as a blood flow imaging method, has been widely used to study the functional activities of brain, diagnose and treat of retinal and skin disorders, and evaluate drug efficiency with the advantages of simpler system structure, high temporal and spatial resolution and non-invasive full-field imaging without scanning. Unfortunately, this method does not have the function of separating artery and vein, simultaneously detecting the blood flow changes within different tissue compartments in a small region. The artery-vein separation method can effectively separate cerebral tissue compartments. But, there have several problems when integrating the current artery-vein separation method with LSCI to analyze the changes of cerebral blood flow within different cerebral tissue compartments, such as temporal resolution reduction and complicating system structure. Therefore, this thesis further analyzes the laser speckle phenomenon:through the research of the probability density function (PDF) of laser speckle intensity, a simple but effective automatic artery-vein separation method which utilizes single-wavelength coherent illumination is presented. This method is based on the relative temporal minimum reflectance (RTMR) analysis of laser speckle images. Combining this method with laser speckle contrast analysis, the artery-vein separation and blood flow imaging can be simultaneously obtained using the same raw laser speckle images data to enable more accurate analysis of changes of cerebral blood flow within different tissue compartments during functional activation, disease dynamic, and neurosurgery. The main contents of this thesis include:
(1) We present a simple but effective automatic artery-vein separation method which utilizes single-wavelength coherent illumination. This method is based on the RTMR analysis of laser speckle images. Theoretic analysis and experimental results demonstrate that the Rayleigh distribution is an effective approximation function of the PDF of integrated laser speckle data, when the speckle contrast value is very small and the time sequential speckle images are statistically independent. According to the Rayleigh function, the expression of laser speckle minimum intensity (Imin) is derived, which shows that the laser speckle minimum intensity is a function of laser speckle averaged intensity and velocity (speckle contrast). In the laser speckle minimum intensity image, vessels can be classified into two groups, one group with higher Imin than other cortical compartments and another group with lower (approximate)Imin than its cortical parenchyma neighborhood. But, there is inhomogeneous background due to the uneven illumination, which decreases the accuracy of classification of two groups. To avoid the influence from inhomogeneous background due to the uneven illumination, the relative temporal minimum reflectance is utilized. RTMR is defined as the ratio of the temporal minimum intensity to the spatially averaged intensity of the cortical parenchyma neighborhood (for a specific pixel (x, y), the cortical parenchyma neighborhood is a square neighborhood without vessels in the temporal mean speckle image). The RTMR values in arteries are higher than other parts, the RTMR values in relatively larger veins are lower than other parts and the RTMR values in relatively smaller veins are similar to those in cortical parenchyma. Arterial regions are segmented from other parts in the cerebral cortex based on the fact that the RTMR values in arterial regions are higher than those of their cortical parenchyma neighborhoods. To avoid misclassification of relatively smaller veins, the venous regions are obtained by removing the arterial regions from the vascular structures which are segmented from laser speckle temporal contrast image. The TPR (True Positive Rate) of this separation method reaches98.5%for the arteries, and95%for the veins. The misclassification of arteries as veins is1.5%, and the misclassification of veins as arteries is5%. The parameters of RTMR analysis are estimated, such as the effective wavelengths are between600nm and640nm, the penetration depth is a few hundred microns in brain tissue under effective wavelengths.
(2) An application of combining artery-vein separation method by RTMR with LSCI in investigating the blood flow changes in arterioles, venules and parenchyma during cortical spreading depression (CSD) is presented. This combined method improves the compartment-resolved imaging of cerebral blood flow during functional activation, disease dynamic, and neurosurgery.
(1)提出一种自动有效的基于单波长激光散斑相对最小反射率分析的动静脉分割法。衬比值比较小,并且CCD记录的散斑图像互相统计独立时,Rayleigh分布函数是实际的积分散斑强度概率密度函数的一个很好的近似。通过Rayleigh分布函数得出激光散斑最小光强的表达式,发现它是平均光强和血流速度的整合参数。在激光散斑最小光强图内,根据激光散斑最小光强值,对血管进行分类。不过由于平均光强分布和入射光强有关,因此激光散斑最小光强存在背景不均匀现象,为此引入激光散斑相对最小反射率这个参量。空间任意一点的激光散斑相对最小反射率定义为：该点的激光散斑最小光强除以该点在激光散斑时域内均图内的邻域的非血管脑皮层组织的平均光强。在RTMR图中：动脉血管区域值最大；静脉血管区域值相对最小,部分可能和非血管皮层区域重叠；非血管脑皮层区域的RTMR值在了两类血管之间。为了避免误判静脉区域,所以先通过RTMR值采用多阈值法逐个像素的提取动脉网络(即对于血管内的一个像素,如果它的RTMR值比它的邻域内非血管脑组织像素的RTMR值平均值大,则该像素属于动脉),然后从血管网络图中减去动脉结构,得出静脉网络。实验证明基于单波长激光散斑相对最小反射率分析的动静脉分割法的准确率高,动脉的PTR (True Positive rate)是98.5%,静脉的PTR是95%,动脉的误判率是1.5%,静脉的误判率是5%。同时分析了激光散斑相对最小反射率分析的有效波长,成像深度等参数,其中最佳波长可能是600nm,成像深度大概是几百微米。
(2)基于单波长激光散斑相对最小反射率分析的动静脉分割法和激光散斑衬比成像技术结合,实现大鼠脑皮层不同成分组织在CSD模型中的独自的血流变化分析。准确地显示了微小区域内,微动脉、微静脉以及非血管脑皮层组织各自在CSD过程中的血流变化情况。
Understanding the responses of different cerebral tissue compartments under normal or abnormal physiological conditions is considerably important to basic researches and clinical diagnostics, such as studying the mechanism of neurovascular coupling, diagnosis and prevention of cerebral disorders, evaluating drug efficiency and intraoperative imaging. Laser speckle contrast imaging (LSCI) as a blood flow imaging method, has been widely used to study the functional activities of brain, diagnose and treat of retinal and skin disorders, and evaluate drug efficiency with the advantages of simpler system structure, high temporal and spatial resolution and non-invasive full-field imaging without scanning. Unfortunately, this method does not have the function of separating artery and vein, simultaneously detecting the blood flow changes within different tissue compartments in a small region. The artery-vein separation method can effectively separate cerebral tissue compartments. But, there have several problems when integrating the current artery-vein separation method with LSCI to analyze the changes of cerebral blood flow within different cerebral tissue compartments, such as temporal resolution reduction and complicating system structure. Therefore, this thesis further analyzes the laser speckle phenomenon:through the research of the probability density function (PDF) of laser speckle intensity, a simple but effective automatic artery-vein separation method which utilizes single-wavelength coherent illumination is presented. This method is based on the relative temporal minimum reflectance (RTMR) analysis of laser speckle images. Combining this method with laser speckle contrast analysis, the artery-vein separation and blood flow imaging can be simultaneously obtained using the same raw laser speckle images data to enable more accurate analysis of changes of cerebral blood flow within different tissue compartments during functional activation, disease dynamic, and neurosurgery. The main contents of this thesis include:
(1) We present a simple but effective automatic artery-vein separation method which utilizes single-wavelength coherent illumination. This method is based on the RTMR analysis of laser speckle images. Theoretic analysis and experimental results demonstrate that the Rayleigh distribution is an effective approximation function of the PDF of integrated laser speckle data, when the speckle contrast value is very small and the time sequential speckle images are statistically independent. According to the Rayleigh function, the expression of laser speckle minimum intensity (Imin) is derived, which shows that the laser speckle minimum intensity is a function of laser speckle averaged intensity and velocity (speckle contrast). In the laser speckle minimum intensity image, vessels can be classified into two groups, one group with higher Imin than other cortical compartments and another group with lower (approximate)Imin than its cortical parenchyma neighborhood. But, there is inhomogeneous background due to the uneven illumination, which decreases the accuracy of classification of two groups. To avoid the influence from inhomogeneous background due to the uneven illumination, the relative temporal minimum reflectance is utilized. RTMR is defined as the ratio of the temporal minimum intensity to the spatially averaged intensity of the cortical parenchyma neighborhood (for a specific pixel (x, y), the cortical parenchyma neighborhood is a square neighborhood without vessels in the temporal mean speckle image). The RTMR values in arteries are higher than other parts, the RTMR values in relatively larger veins are lower than other parts and the RTMR values in relatively smaller veins are similar to those in cortical parenchyma. Arterial regions are segmented from other parts in the cerebral cortex based on the fact that the RTMR values in arterial regions are higher than those of their cortical parenchyma neighborhoods. To avoid misclassification of relatively smaller veins, the venous regions are obtained by removing the arterial regions from the vascular structures which are segmented from laser speckle temporal contrast image. The TPR (True Positive Rate) of this separation method reaches98.5%for the arteries, and95%for the veins. The misclassification of arteries as veins is1.5%, and the misclassification of veins as arteries is5%. The parameters of RTMR analysis are estimated, such as the effective wavelengths are between600nm and640nm, the penetration depth is a few hundred microns in brain tissue under effective wavelengths.
(2) An application of combining artery-vein separation method by RTMR with LSCI in investigating the blood flow changes in arterioles, venules and parenchyma during cortical spreading depression (CSD) is presented. This combined method improves the compartment-resolved imaging of cerebral blood flow during functional activation, disease dynamic, and neurosurgery.
引文
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