Observations on the accuracy of photometric techniques used to measure some in vivo microvascular blood flow parameters

Abstract

Objective: The accuracy of optical methods used to measure in vivo microvascular blood flow parameters is investigated using measurements made in all vessels of microvascular networks of the rat mesentery.

Methods: The principle of mass conservation was applied to in vivo blood flow rate and discharge hematocrit data, which were determined by photometric methods. One of the several implied assumptions of most interpretations of in vivo optical data is that the vessels are circular in cross-section: to see the impact of vessel lumen shape on one of these measurements, the average velocity of blood flowing through a D-shaped glass capillary tube was measured by the dual-slit method.

Results: For in vivo data, significant imbalance exists in a large number of bifurcations, and the correlation between the blood flow imbalance and the red cell flux imbalance is very small (r2 = 0.39), indicating multiple sources of error. Furthermore, the measured discharge hematocrits were consistent with the observed flow directions at bifurcations in only 39% to 46% of the bifurcations in a network. The imbalance at these bifurcations is not simply caused by the inaccuracy of measurements in only a few microvessels that join such bifurcations, i.e., the inaccuracies are evenly distributed among the vessels. The results of the in vitro study of blood velocity measurement in D-shaped tubes indicates that the ratio of dual-slit velocity to the actual average blood velocity is sensitive to the shape of the vessel lumen, and is a function of blood flow rate, hematocrit, vessel lumen shape, and orientation.

Conclusions: Significant inaccuracies exist in the flow and hematocrit data obtained by current methods of interpretation of in vivo photometric measurements. These inaccuracies must be considered when making vessel to-vessel comparisons, or vessel-by-vessel comparisons between in vivo observations and model predictions, even though the inaccuracies are greatly reduced when comparing averaged data.