A computational study of pulmonary hemodynamics in healthy and hypoxic mice

This study uses a 1D fluid dynamics arterial network model to predict pressure and flow dynamics in healthy and hypoxic mice. Data for this study include blood flow and pressure measurements from the main pulmonary artery of 7 healthy and 5 hypoxic mice. Representative arterial network dimensions for the 21 largest pulmonary arterial vessels are extracted from micro-CT images from healthy and hypoxic lungs. Each vessel is represented by its length and radius, and is assumed to taper longitudinally. Fluid dynamic computations are done assuming that the flow is Newtonian, viscous, laminar, has no swirl, and that the arterial walls are thin. The system of equations is closed by a constitutive equation relating pressure and area, using either a linear model derived from stress-strain deformation in the circumferential direction or a nonlinear model of empirical nature. For each dataset, an inflow waveform is extracted from data, and nominal parameters related the outflow boundary conditions were specified using mean values and timescales computed from the measured flow and pressure waveforms. The model was calibrated for each mouse by estimating parameters using optimization that minimized the least squares error between measured and computed pressure and flow waveforms from the main pulmonary artery. Results show that for healthy animals, the nonlinear wall model is needed to predict flow and pressure characteristics, while for the hypoxic animals both models predict the experimental data well, possibly because vessels are significantly stiffer due to wall remodeling.