Table 1 Workflow chart for Dynamic Tissue Perfusion Measurement (DTPM) of transplant kidneys

Step 1

The software opened the video and calibrated the distances automatically

Step 2

The software read out the color bar and all hues were calibrated

Step 3

Maximal velocities encoded by the colors were registered

Step 4

Selection of the region of interest (ROI)

a. The ROI consisted of a parallelogram which included an entire cortical segment in the center of the transplant, fed by one interlobar artery running straight towards the transducer.

b. The four corners of the parallelogram were set as follows:

i. At the center of the outer edge of a medullary pyramid (1)

ii. At the center of the outer edge of the neighboring medullary pyramid (2)

iii. At the renal surface perpendicular to the first two corners (3 and 4)

c. The ROI was sliced into defined horizontal slices

i. Each slice stretched from the left to the right border of the ROI and had a height which encompassed a certain percentage of the ROI’s entire height (10%, 20% , 50% or 100%). So the sub-ROI encompassing the proximal 20% (resp. 50%) was labeled p20 (resp. p50) and those encompassing the distal 20% (resp. 50%) was labeled d20 (resp. d50). (Figures 1 and 2)

ii. These slices were arranged to cover adjacent horizontal strips of the ROI.

Step 5

Dynamic tissue perfusion measurement was initiated. All steps (a-f) were carried out automatically by the PixelFlux-software

a. Each pixel (colored or grey) was evaluated with respect to its size and coloration.

b. The color bar’s hues were assigned a velocity value according to its relative distance from zero to the maximum velocity value.

c. The relative distance value (from 0 to 1) was then multiplied by the maximum velocity indicated to calculate a specific velocity value for each color hue. Colorless pixels were assigned the velocity of zero.

d. The mean velocity value of all colored pixels inside the ROI and the area of all colored pixels were calculated.

e. The measurements were repeated for all consecutive images of the video.

f. The software recognized beginning and end of any heart cycle inside the video automatically and restricted all consecutive calculations to a full heart cycle.

g. Then the mean perfusion velocity of each image was multiplied by the mean perfused area. This product was divided by the area of the entire ROI resulting in the mean flow intensity of the ROI during a complete heart cycle.