An imaging-based computational approach to model ventilation distribution and soft-tissue deformation in the ovine lung.

RATIONALE AND OBJECTIVES A computational modeling framework of soft-tissue mechanics and air flow has been developed toward the aim of linking computed tomography measures of ventilation distribution to subject-specific predictions in imaging-based geometric models of the lung. The aim of this approach is to enable predictions of the effect of perturbations in geometry or functional parameters on imaged function of the lung. MATERIALS AND METHODS Computational techniques that can deal with anatomic detail and spatially distributed nonlinear material properties are used to model parenchymal soft-tissue mechanics in a physically realistic model of the ovine lung. The lung is modeled as a homogeneous, compressible, nonlinearly elastic body. Using equations for large deformation mechanics, change in geometry of the lung is simulated at static inflation pressures from 25 cm H2O to 0 cm H2O. Multidetector row computed tomography imaging defines the model geometry, the movement of the model lung surface during inflation, and displacement of airway bifurcations for comparison with predicted internal displacements of the model. RESULTS A novel modeling framework has been formulated that links equations for large deformation of the lung tissue to equations for airway flow and pressure. This preliminary model predicts airway displacements that are in good agreement with imaged displacements (total root mean square [RMS] error < 4 mm from 25 to 0 cm H2O). CONCLUSIONS State-of-the-art computed tomography imaging is interpreted using a modeling framework to predict ventilation distribution and changes in the geometry of the lung during increments in inflation pressure. Further development will provide a predictive link between subject-specific anatomy and function.