Equations Speckle contrast : K(T) = σ s / Decay rate of autocorrelation: g(T) = / Critical decay time: τ 0 = g(e -1 sec) Relative velocity:v = x/ τ 0 Algorithms - Use readerobj function to read in our.avi video file, and use the read and get functions to obtain the number of frames - Loop through each frame of the video and convert each frame from an RGB image to a grayscale uint8 image - Put each frame into a 3D array with x, y, and time as the three coordinates to avoid numerous and slow for-loops - Find mean of our 3D image array - Find standard deviation of array. Split up the array into four quadrants because of memory limitations - Only used pixels with a value over a certain number to avoid noise and interference in our calculations - Used a set of nested for-loops to create vectors of each pixel over time. With this data, we ran the autocorr function to get the autocorrelation value for each pixel. We needed the value 1/e so we averaged the values of the autocorr function run at.3 and.4. Speckle Imaging to Find Relative Danio rerio Blood Flow Eli Ben-Joseph, Analiese DiConti, Marta Lynne Milan, Peter So, Steve Wasserman, Carlos Pardo, Scott Manalis Background Laser Speckle Contrast Imaging (LSCI) is a powerful imaging tool because it includes both spatial and temporal resolution with cameras and lasers. Laser light is scattered from the surface of the object being imaged and a camera images the speckle pattern that results. The intensity speckle pattern is produced by a mutual interference of a set of wave fronts. From that data, one can use the pattern's spatial statistics to find the speckle contrast, which is used to find relative blood flow. LSCI techniques have previously measured blood flow in retina, skin, and other tissues. Laser Dopplar Flowmetry has also been used but is problematic because only temporal or spatial measures can be taken at a single moment. Thus, LSCI is preferable. (Yuan) Problem Summary Using LSCI to image fish blood flow has never been done before. While Yuan et al. measured cerebral blood flow in rodents, we, instead, are attempting to measure relative embryonic Danio rerio (zebrafish) blood flow velocities. Project Summary -Build Fluorescent Microscope (20x objective) -Simulate blood flow with 7.2um beads in a well slide and microfluidic channel slide with bright field and LSCI. -Use MATLAB to find Speckle Contrast (K) to ensure accuracy -Create brightfield and speckle videos of an anesthetized, 6 day old zebrafish in a well. -Use videos to calculate speckle contrast and autocorrelation function, thus finding relative velocity of fish blood flow. References Bolcome et al. Anthrax in Zebrafish vasculature. PNAS, February Yuan et al. Determination of optimal exposure time for image of blood flow changes with laser speckle contrast imaging. Applied Optics, April Conclusions Acknowledgements We would like to thank Peter So, Carlos Pardo, Steve Wasserman, TAs, and the MIT Biological Engineering Department for all of the assistance and advice provided this semester. Bead Images Limitations and Future Improvements -Interference and laser alignment -Thickness of fish created some difficulty in focusing images -Noise in the edges of the images gave values that could not be differentiated from high velocity blood flow in relative velocity calculations. Fish Images Summary Based on the bright field image of the Danio rerio blood vessels and the analysis of the speckle contrast, it is apparent that the relative blood flow is much higher in the veins than in the capillaries. This was expected as it could be observed that fewer blood cells enter the capillary than flow through the veins per unit time. System Apparatus Fluorescence microscope (diagram and photo) Bright field image of beads in water. Any motion detected was due to free movement within the fluid. Speckle image of beads in water. The fluorescence microscope detected the diffraction from the moving beads. Speckle Contrast (k) of beads moving in a microfluidic channel filled with 20% glucose, chosen to simulate the viscosity of blood. Bright spots on the image indicate a large k value which indicates a large amount of movement in this location. Bright field image of Zebrafish blood vessels that was analyzed using speckle imaging. Speckle image of the Zebrafish blood vessels. Speckle contrast of fish blood flow. Brightness is directly related to movement within the image. Thus, the greater the flow within a pixel, the brighter the pixel. Image calculated by dividing standard deviation by the average. Standard Deviation of the intensity of the pixels over time of the speckle video of the fish. Average intensity of the pixels over time of the speckle video of the fish. Relative velocity of blood flow within the zebrafish. Velocity is inversely proportional to tau, so in this case, a darker pixel correlates to a faster flow, relative to another point in the image. Count Tau Histogram of tau values of the zebrafish bloodflow. Tau was calculated by finding the value of the autocorrelation function at 1/e seconds.