The overall goal of this procedure is to quantify the spatiotemporal variations in the in vivo cell-free layer width. This method can help to answer key questions in the field of micro hemodynamics, to better understand the role of cell-philia in micro circulation. The main advantage of this method is that in vivo cell-philia width can be quantified more consistently and conveniently than previous manual measurement techniques, which were very time consuming.
Before beginning the cell-free layer width measurement, run the cell-free layer pre-MatLab script file. Next click open file to select the video file for analysis and adjust the rotation slide to vertically align the walls of the single vessel using the zoom slide to adjust the zoom level as necessary. When the vessel is in the appropriate position, click confirm editing, then click set ROI to crop to define the region of interest.
The aligned image will be displayed in a pop-up window. Adjust the rectangular objective on the image as necessary and double-click the objective to confirm the region of interest. Then click Extract Images to extract all of the edited video frames into consecutive bitmap images, which will be found in the folder with the same name as the selected video file.
To measure the cell-free layer width, click Select Folder and click on the folder of images. The first image frame will appear in the grayscale image panel along with the corresponding gray intensity histogram in the image histogram panel. Select the desired image frame from the list box to perform the analysis and click Find Vessel Walls to identify the inner vessel wall in the image determined as the location where the light intensity profile peak transits from dark to light over two pixels.
Check median filter to apply a median filter to the image to reduce the salt and pepper noise. Check auto-contrast for digital adjustment of the image intensities to enhance the image contrast. Then select a thresholding algorithm in the list box to automatically determine the thresholding value that divides the gray levels into two classes, white pixels with gray levels above the thresholding value, and black pixels with gray levels below the thresholding value.
To measure the spatial variation of the cell-free layer widths, enter the pixel resolution in the pixel resolution box. Then click Calculate to obtain the spatial variation of the cell-free layer widths and click Export. csv to export the cell-free layer width data in a tabulated format.
To measure the temporal variation of the cell-free layer widths at a specific analysis line along the vessel, click Temporal Variation and enter the frame-rate information. Enter the first and last frames of the images for the analysis in the start frame and last frame boxes respectively. Adjust the analysis line slide-bar to select the position of the analysis line along the vessel and confirm the position of the analysis line as illustrated on both the grayscale and binary images.
Then click Calculate to obtain the temporal variation of the cell-free layer widths and click Export. csv to export the cell-free layer width data in a tabulated format. Here, a typical red blood cell flow through an unbranched arterial in the rat cremaster muscle where the cell-free layer can be observed between the RBC core and the inner vessel wall is shown.
A good contrast between these components is critical for ensuring the accuracy of the cell-free layer width measurements. The initial phase of the image analysis involved the detection of the inner vessel wall. By acquiring the light intensity profile along the analysis line perpendicular to the vessel, the location is approximated at the peak that transits from dark to light over two pixels.
Subsequently, the RBC core boundaries are detected using the image thresholding algorithm and the CFL widths can then be calculated. As red blood cells in the cell-free layer possess different light transmittances the difference in gray levels can be subdivided into two classes. However, the identification of an accurate threshold value between the two peaks in image histogram maybe restricted by poor image quality and contrast.
To improve the contrast between the red blood cells and the cell-free layer, a blue filter can be used. This is even more evident in these images where the boundaries of the red blood cell cores were more accurately identified with a blue filter. The thresholding algorithm can also influence the cell-free layer width measurement as apparent in these images in which the different thresholding algorithms resulted in the identification of different red blood cell core boundaries and therefore different cell-free layer widths.
The measurement of the in vivo cell-free layer width is very sensitive to the quality of images. Therefore, be sure to preform the surgery carefully and to use an appropriate optical assistant to obtain a good quality of image. Moreover, it is essential to select the appropriate image thresholding algorithm to ensure an accurate and consistent cell-free layer width measurement.
