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To minimize noise and increase vessel intensity, an iterative Gaussian smoothing with different mask sizes was used.
To assess microvascular wall levels of formazan (the reduction product of TNBT and therefore an index of oxidant activity), the measured pixel intensities were used to calculate microvascular wall light absorption (A): A = ln(l t/ l o), where l t is the vessel intensity and l o is the intensity for the adjacent extravascular region.
Blood vessels were identified in panels a-c, a 10-pixel expansion was mapped from each blood vessel to create a mask and intensity was measured as shown in panel d, and blood vessel intensity was then subtracted as in panel e to generate blood brain barrier permeability quantification.
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In small vessels, intensity was stronger in SScPAH vs. IPAH.
There are considerable interests in imaging blood vessels using intensity or amplitude information instead of phase information.
When vessel-TEM1-intensity was divided into any two groups, however, no significant correlations between vessel-TEM1-intensity and RFS, cancer-OS, or all-OS were observed when Kaplan Meier curves were drawn (P = 0.576, 0.123, and 0.515, respectively) (Fig. S1).
Relationship between vessel-TEM1-intensity and clinicopathological factors in 945 patients with gastric cancer.
However, vessel-TEM1-intensity was not significantly associated with a poor prognosis as shown in Table S2.
Recurrence-free survival (RFS) curves, cancer-related overall survival (COS) curves, and overall survival(OS) curves according to the grading of vessel-TEM1-intensity.
In DSA, a mask frame and number of Phase frames are digitized and subtracted using Laboratory virtual instrument engineering workbench (Lab VIEW) software to reduce the background tissues in the image and thereby enhancing the visualization of blood vessel and the intensity distribution of the image is also analyzed.
To calculate the vessel area, the intensity differences between vessels and background were increased.
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