Estimating Blood Flow in Skeletal Muscle Arteriolar Trees Reconstructed from In Vivo Data

Background Our group uses both computational modeling and intravital videomicroscopy to study the regulation of blood flow in the microcirculation of skeletal muscle. Although we are able to obtain nearly complete arteriolar network structure from in vivo experiments, obtaining complete hemodynamic information is much more difficult and time‐consuming. It is also difficult to obtain the full boundary data needed to directly calculate microvascular blood flow and hematocrit distributions. Therefore, the objective of the present work was to develop a computational model that could accurately predict blood flow in skeletal muscle arteriolar trees in the absence of complete boundary data. Methods We used arteriolar trees in the rat gluteus maximus muscle (GM) that were reconstructed from montages obtained via intravital videomicroscopy, and incorporated a recently published method for approximating unknown boundary conditions into our existing steady‐state model of two‐phase blood flow. For varying numbers of unknown boundary conditions, we used the new flow model and GM arteriolar geometry to approximately match red blood cell (RBC) flows corresponding to experimental measurements. Results We showed that this method gives errors that decrease as the number of unknown boundary conditions decreases. We also showed that specifying total blood flow into the arteriolar tree decreases the mean RBC flow error and its variance. By varying target values of pressure and wall shear stress required by the model, we showed that results are less sensitive to the target pressure, and we developed a method for estimating the optimal target shear stress. Conclusion We have developed and validated a computational method that can accurately estimate RBC flow distribution in arteriolar trees in the absence of complete boundary data. Support or Funding Information Funding: Natural Sciences and Engineering Research Council (NSERC) grant #R4081A03 awarded to DG, NSERC grant #R4218A03 awarded to DNJ, and NSERC CGS‐D Scholarship awarded to BKA.