Abstract:
Advancing technology has enabled rapid improvements in imaging and image processing techniques providing increasing amounts of structural and functional information. While these imaging modalities now offer a wealth of information about function within the body in health and disease certain limitations remain. We believe these can largely be addressed through a combined medical imaging - computational modeling approach. For example, imaging may only be performed in the prone or supine postures but humans function naturally in the upright position. We have developed an image-based computational model of coupled tissue mechanics and pulmonary blood flow to enable predictions of pulmonary perfusion in various postures and lung volumes. Lung and vascular geometries are derived using a combination of imaging reconstruction and computational algorithms. Solution of finite deformation equations provides predictions of tissue deformation and internal pressure distributions within the lung parenchyma. By embedding vascular models within the lung volume we obtain a coupled model of blood vessel deformation as a result of changes in lung volume. A 1D form of the Navier-Stokes flow equations are solved within the vascular model to predict perfusion. Tissue pressures calculated from the mechanics model are incorporated into the vascular constitutive pressure-radius relationship. Results demonstrated a relatively consistent flow distribution in all postures indicating the large influence of branching structure on flow distribution. It is hoped that this modeling approach may provide insights to enable interpolation of imaging measurements in alternate postures and lung volumes and enable an increased understanding of the mechanisms influencing pulmonary perfusion distribution.
