Research On The Laser Speckle Signal Processing Method

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.