7 May 2021
Computational fluid dynamics (CFD) modeling is an emerging tool for understanding the prognosis and development of cardiovascular disease. Advances in image analysis and data acquisition has led to patient-specific CFD, whereby imaging data is directly used as the vascular domain for computational hemodynamic simulations. These techniques are underutilized in understanding pulmonary vascular disease, e.g., pulmonary hypertension (PH), which affect hundreds to thousands of pulmonary blood vessels. One-dimensional (1D) CFD models can predict wave propagation throughout a network of blood vessels, yet it is unclear how model predictions and parameter inference are affected by the size of the vascular network used. This talk investigates the effect of pulmonary arterial network size on model sensitivity, parameter inference, and model predictions in both normotensive and PH models, utilizing data from mice.