Estimating blood flow in skeletal muscle arteriolar trees reconstructed from in vivo data using the Fry approach.

Objective: To develop a computational method to accurately predict blood flow in skeletal muscle arteriolar trees in the absence of complete boundary data.

Methods: We used arteriolar trees in the rat GM muscle that were reconstructed from montages obtained via IVVM, and incorporated a recently published method for approximating unknown b.c.'s into our existing two-phase, steady-state blood flow model. For varying numbers of unknown b.c.'s, we used the new flow model and GM geometry to approximately match RBC flows corresponding to experimental measurements.

Results: We showed this method gives errors that decrease as the number of unknown b.c.'s decreases. We also showed that specifying total blood flow decreases the mean RBC flow error and its variability. By varying required target values of intravascular pressure and wall shear stress, we showed results are less sensitive to target pressure. Finally, we developed and validated a method for determining target values, so that network hemodynamics and resistance can be accurately calculated based only on measured or estimated total blood flow.

Conclusions: We have developed and validated a computational method that can accurately estimate RBC flow distribution in skeletal muscle arteriolar trees in the absence of complete boundary data.