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speckle 2.5.2. analysis Laser contrast



  • speckle 2.5.2. analysis Laser contrast
  • Imaging depth and multiple scattering in laser speckle contrast imaging
  • 1. Introduction
  • Laser speckle contrast analysis (LASCA), also known as laser speckle contrast imaging (LSCI), is a method that instantly visualizes microcirculatory tissue blood . Using registered laser speckle contrast analysis and temporal clustering . Measurement of blood speed. The principle of LSI has been. Laser speckle contrast imaging (LSCI) is a powerful and simple method for full . Extravascular absorption and scattering coefficients were based on the in vitro .. Effect of optical properties on scattering distributions.

    speckle 2.5.2. analysis Laser contrast

    Boas, and Maria Angela Franceschini Optica 3 9 Express 8 12 You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only.

    You may subscribe either as an OSA member, or as an authorized user of your institution. Cited by links are available to subscribers only. Figure files are available to subscribers only. Article level metrics are available to subscribers only.

    Login or Create Account. April 12, Revised Manuscript: July 14, Manuscript Accepted: July 19, Published: Not Accessible Your account may give you access. Abstract We present a lateral laser speckle contrast analysis method combined with line beam scanning illumination to improve the sampling depth of blood flow imaging.

    References You do not have subscription access to this journal. Cited By You do not have subscription access to this journal. A numerical photon migration technique that can separate the intravascular scattering events from the extravascular scattering events is required. Furthermore, such a model must include a realistic description of the complex microvascular structure in the tissue. Modeling photon migration numerically has recently become possible due to increased computational power and three-dimensional 3-D Monte Carlo simulations using tissue-mimicking geometries.

    The sampling depth and the degree of multiple scattering can then be derived from the probability distributions of photon travel in the intravascular space. A 3-D Monte Carlo method was used to simulate photon propagation in an arbitrary geometry. Due to the complexity of the geometry, many photons must be used to achieve statistical convergence of the photon path distributions. However, since each launched photon is statistically independent, many simulations can be simultaneously initialized as long as the random number generators produce statistically independent values.

    Parallelization of the code was performed using the message passing interface MPI. The scalable parallel pseudorandom number generator was used to ensure statistical independence among the parallel simulation instances. This is in contrast to several recently reported graphics processing unit GPU adaptations of the Monte Carlo algorithm, which require much more extensive code modification to implement but in return allow a high amount of parallelization on a desktop computer.

    The model geometry was derived from in vivo images of mouse cortical vasculature. Region of interest ROI indicates location of two photon imaging for vascular geometry. The image stacks were stitched together using the method developed by Preibisch et al. The geometry extension was done because the tissue absorption coefficients in the LSCI wavelength range are low, so more room was needed to allow the photons to scatter down into the geometry and then return to the surface.

    Each voxel was assigned optical properties based on whether it represented extravascular or intravascular space. The intravascular absorption coefficients were generated based on the extinction coefficients of hemoglobin. The concentration of hemoglobin in the vasculature was assumed to be 2.

    The in vitro measurements were taken in the absence of blood. The photon scattering angle was determined using the Henyey—Greenstein phase function. In our simulations, every photon scattering event was recorded for photons exiting the top of the sample i.

    This was done in order to record the spatial distribution of intravascular scattering events. An ROI was selected on the surface to determine the depth-dependent intravascular scattering distribution, as well as the number of times the photon scattered inside vessels corresponding to a given detector size and location.

    As previously mentioned, the LSCI signal is determined by the dynamic interaction between the photons and the moving erythrocyte scatterers. As such, the depth dependence considers only the scattering events that occur inside vessels. The depth-dependent signal distribution, f z , which is the probability of an intravascular scattering events occurring at depth z in the geometry, was calculated by integrating the absorption-weighted scattering events in x and y , and then normalizing.

    The photon weight w i was calculated using the following relation:. The expression for F z is as follows:. The number of intravascular scattering events was determined by counting the number of times each photon scattered inside vessels during the Monte Carlo simulation. A histogram was created, and the absorption weighted value of each detected photon was added to the bin corresponding to the number of times that photon had scattered in vessels.

    The histogram was then normalized to produce a probability distribution of the amount of intravascular scattering. Our first consideration was to determine whether different regions of a speckle contrast image exhibit difference depth dependence and multiple scattering characteristics in a typical LSCI imaging setup. In this context, parenchyma is defined as high-contrast regions of the LSCI image that do not appear to be vessels.

    Two ROIs were chosen to examine the effects of optical properties on photon sampling. The probability values in Figs. Comparison of depth-dependent and depth-integrated scattering characteristics at different ROIs. The ROI in the top left corresponds to parenchyma in the speckle contrast image from Fig. Though photons scattered and changed directions outside of vessels, only the intravascular events are relevant to LSCI imaging, and therefore the extravascular events are not displayed.

    Photon scattering detected at the parenchyma ROI are evenly spread over a larger area of tissue, and a larger number of vessels, than the photon scattering detected at the surface arteriole. The scattering in the surface vessel ROI in Fig. Once the arteriole branches into capillaries, the scattering distribution begins to spread out into the nearby vessels.

    This simple comparison demonstrates the vast difference in spatial sampling that is achieved when considering different regions of the image. This is not primarily due to reduced scattering outside of the ROI in the case of the vessel ROI, but instead is due to increased scattering events occurring near the ROI region. The effect of changing the intravascular scattering coefficient on the number of dynamic scattering events and depth-dependent intravascular scattering distributions can be seen in Figs.

    In-depth examination of surface vessel ROI and parenchyma ROI location, showing the effect of changing intravascular scattering coefficient. The parenchyma ROI in Figs. As expected, the number of dynamic scattering events show an approximately linear dependence on the intravascular scattering coefficient due to the high intravascular anisotropy. The results show that changing intravascular scattering coefficient causes a negligible change in the relative depth-dependent scattering distributions in both the parenchyma and the surface vessels.

    This suggests that our results should hold over a wide range of wavelengths, blood oxygenation fractions, and hematocrit Hct values. The effect of extravascular scattering coefficient can be seen in Fig. In the surface vessel ROI in Fig. The parenchyma ROI in Fig. The effect of changing the extravascular scattering coefficient. The results demonstrate that increasing the extravascular scattering coefficient causes a negligible increase in single scattering. In this case, the amount of single scattering decreases slightly as the extravascular scattering coefficient is increased.

    The probability of scattering 4 to 8 times, however, appears to increase slightly as the extravascular scattering coefficient is increased. Although slight changes in scattering were observed, overall the results suggest that changing the extravascular scattering coefficient has little effect on the number of dynamic scattering events. We performed several convergence tests to determine the validity of the presented scattering distributions.

    For the visualization of the 3-D scattering distributions in Figs. The capability to run these simulations was the result of MPI parallelization of the Monte Carlo code. This was a simple affair, as there is no need for between-thread communication due to the independence of stochastic photon simulations.

    At the end of each run, the results from each thread were combined to attain the statistical power required for convergence of the scattering distributions. As only a single vascular anatomy was used to generate the results shown, it is difficult to claim that any given imaging scenario will result in a specific imaging depth for a given ROI. However, the results do show trends in the spatial distribution of intravascular scattering that seem to be invariant of the ROI.

    Not only does the detected signal represent vascular scattering far beneath the surface, it also represents scattering laterally in space. The depth-dependence plot shown in Fig.

    However, there are no surface vessels in that region below the ROI, so all dynamic scattering at those depths occurred spatially offset from the selected ROI. This can be seen in the rendered representation of that ROI in Fig.

    This has little effect on the overall signal when considering a surface vessel, as the amount of scattering inside the vessel strongly outweighs any contribution from lateral regions. A change in speckle contrast value in the parenchyma will therefore represent a large integrated volume both in depth and laterally on the surface.

    The results in Fig. This has a couple of implications for speckle contrast imaging in the cortex. First, it means that the contrast values corresponding to resolved vessels do not solely represent the resolved vessel in the image. At these depths, the volume fraction of vasculature determines the amount of scattering. This is because the intravascular scattering distribution spreads out spatially as with increasing depth into the tissue. The vascular anatomy also plays a significant role in the distribution of scattering events.

    For example, in Fig. Combining this observation with the possibility of increased surface vessel sensitivity would allow for a signal that is strongly weighted toward the intravascular scattering events occurring in the single-surface arteriole. The primary conclusion from the intravascular scattering distributions shown in Figs.

    Imaging depth and multiple scattering in laser speckle contrast imaging

    ABSTRACT. A new noninvasive technique for monitoring capillary blood flow has been developed. Based on the phe- nomenon of time-varying laser speckle. flow with laser speckle contrast imaging: analysis of static scatterers effect through modelling and Alteration of microvascular structure. Analysis of cartilage surfaces using laser speckle imaging Laser, speckles, cartilage, surface, roughness, contrast, arthroscopy, .

    1. Introduction



    ABSTRACT. A new noninvasive technique for monitoring capillary blood flow has been developed. Based on the phe- nomenon of time-varying laser speckle.


    flow with laser speckle contrast imaging: analysis of static scatterers effect through modelling and Alteration of microvascular structure.

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