The interaction between mechanical obstruction and outcome in pulmonary embolism (PE) is not well quantified. Therefore a simple prognostic tool that can be used quickly in the clinical setting remains elusive. Several scoring systems have been proposed to address this problem. However, they are unable to adequately capture the functional outcomes in PE so have not been adopted widely clinically. Here we present an image-based computational model that correlates very well with measures of RV dysfunction. The model extracts the geometric features of the lung, airways, blood vessels and emboli from CTPA (computed tomography pulmonary angiogram) imaging and simulates function (perfusion, ventilation and gas exchange) within these geometries. This results in subject-specific predictions of function in 9 patients with acute PE. There is a high correlation between model results and indicators of right heart dysfunction (p=0.001 in the case of the ratio between right and left ventricular volumes and p<0.03 in the case of systolic pulmonary artery pressure estimated from echocardiography). An existing scoring system that accounts only for the mechanical obstruction of capillary bed performs less well than the model (p=0.04 in the case of the ratio between right and left ventricular volumes and p=0.23 in the case of systolic pulmonary artery pressure estimated from echocardiography). This suggests that the functional impact of occlusion must be accounted to construct useful PE scoring systems.
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.
A computational model of blood flow through the human pulmonary arterial tree has been developed to investigate the relative influence of branching structure and gravity on blood flow distribution in the human lung. A geometric model of the largest arterial vessels and definitions of the lobar boundaries were first derived using multi-detector row x-ray computed tomography (MDCT) scans from the Lung Atlas. Further accompanying arterial vessels were generated from the MDCT vessel end points into the lobar volumes using a volume filling branching algorithm. A reduced form of the Navier-Stokes equations were solved within the geometric model to simulate pressure, velocity and vessel radius throughout the network. Blood flow results in the anatomically-based model, with and without gravity, and in a symmetric arterial model were compared in order to investigate their relative contributions to blood flow heterogeneity. Results showed a persistent blood flow gradient and flow heterogeneity in the absence of gravitational forces in the anatomically-based model. Results revealed that the asymmetric branching structure of the model was largely responsible for producing this heterogeneity. Analysis of average results in different slice thicknesses illustrated a clear flow gradient due to gravity in 'lower-resolution’ data (thicker slices), but on examination of higher resolution data a trend was less obvious. Results suggest that while gravity does influence flow distribution, the influence of the tree branching structure is also a dominant factor. These results are consistent with high-resolution experimental studies that have demonstrated gravity to be only a minor determinant of blood flow distribution.