MDCT-based dynamic, subject-specific lung models via image registration for CFD-based interrogation of regional lung function

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.