An imaging-based computational framework for simulation of airflow in subject specific breathing human lungs is established. The three-dimensional (3D) airways of up to 9 generations and lobes are segmented and reconstructed from computed tomography (CT) images. Beyond the CT-resolved 3D airways, a volume filling method is applied to generate the one-dimensional (1D) conducting airway tree that bridges the central airway with the lung parenchyma. Through 3D-1D airway coupling, a novel image-registration-based boundary condition (BC) is proposed to derive physiologically-consistent regional ventilation for the whole lung and provide flow-rate fractions needed for the 3D airway model via the 1D-tree connectivity and the mass conservation. The in-house parallel finite-element large-eddy simulation (LES) code enables to capture genuinely complex airflow characteristics in a computationally-efficient manner. The 3D-1D coupling framework is multiscale because it can not only predict detailed flows in the 3D central airways at a local level, but also yields subject-specific physiologically-consistent regional ventilation at the whole lung level. The framework has been applied to investigate pulmonary airflow and lung physiology. For example, the study of intra- and inter-subject variability provides insight into the effect of airway geometry on airflow structure. The relations between airflow structure, energy dissipation, and airway resistance under normal breathing condition have also been studied, showing similarity behaviors for inspiratory and expiratory flows. In the study of high-frequency oscillatory ventilation, we have compared counter-flow structures near flow reversal (namely phase change between inspiration and expiration) and quantified associated convective mixing in both idealized and CT-based airway models. Furthermore, the image-registration-derived displacement field is used to deform 3D-1D airway models for breathing lung simulation and estimate diameter changes of 1D airway segments during deformation. In conjunction with an arbitrary Lagrangian Eulerian method, airflow in a breathing lung has been simulated and compared with that of a rigid airway model. The results show that the proposed computational framework is promising in better understanding the human lung physiology and improving the treatment of diseased lung.

Computational fluid dynamics (CFD) has become an attractive tool in understanding the characteristic of air flow in the human lungs. Inter-subject variations make subject-specific simulations essential for understanding structure-function relationship, assessing lung function and improving drug delivery. However, currently the subject-specific CFD analysis remains challenging due, in large part to, two issues: construction of realistic deforming airway geometry and imposition of physiological boundary conditions. To address these two issues, we develop subject-specific, dynamic lung models by utilizing two or multiple volume multi-detector row computed tomography (MDCT) data sets and image registrations in this thesis. A mass-preserving nonrigid image registration algorithm is first proposed to match a pair of three-dimensional (3D) MDCT data sets with large deformations. A novel similarity criterion, the sum of squared tissue volume difference (SSTVD), is introduced to account for changes in intensity with lung inflation. We then demonstrate the ability to develop dynamic lung models by using a pair of lung volumes to account for deformations of airway geometries and subject-specific boundary conditions. The deformation of the airway geometry is derived by the registration-derived deformation field and subject-specific boundary condition is estimated from regional ventilation in a 3D and one-dimensional (1D) coupled multi-scale framework. Improved dynamic lung models are then proposed from three lung volumes by utilizing nonlinear interpolations. The improved lung models account for nonlinear geometry motions and time-varying boundary conditions during breathing. The capability of the proposed dynamic lung model is expected to move the CFD-based interrogation of lung function to the next plateau.