Dynamic breathing lung models for healthy and asthmatic subjects based on assessment of regional lung function

We develop dynamic breathing lung models for healthy and asthmatic subjects by utilizing two or multiple volumetric multi-detector-row computed tomographic (MDCT) of lung images acquired from both static CT and four-dimensional CT (4D-CT) scans. A mass preserving image registration is utilized to derive local variables including Jacobian (ratio of volume change) and maximum shear strain or anisotropic deformation index (ADI) for assessment of lung deformation, and local air volume and flow rate for assessment of regional ventilation. First, lung image data of six normal human subjects acquired at three static inflation levels, 20% of vital capacity (VC), 60% VC and 80% VC, are used to evaluate the non-linear characteristics of the human lung during deep breathing. We quantify the non-linearity by comparing the variables which are interpolated linearly between 20% and 80% VC images with those of direct registration of 20%, 60% and 80%VC images to observe how the results are deviated from linear curves. Then, we assess regional ventilation, nonlinearity, and hysteresis of the lung motion during dynamic breathing using 4D-CT data sets. Six healthy adult humans are studied during controlled tidal breathing as well as during total lung capacity (TLC) and functional residual capacity (FRC) breath holds. Results from static analysis are utilized to contrast static vs. dynamic (deep vs. tidal) breathing. A rolling-seal piston system is employed to maintain consistent tidal breathing during 4D-CT spiral image acquisition, providing required between-breath consistency for physiologically meaningful reconstructed respiratory motion. Lobar distributions of air volume change during tidal breathing are correlated with those of deep breathing to differentiate regional ventilation between deep and tidal breathing. With ADI, we are able to quantify nonlinearity and hysteresis of lung deformation that can only be captured in dynamic images. In addition, 4D-CT data sets for six mild/moderate asthmatic subjects are added during tidal breathing following acquisition of two static scans at TLC and FRC. We analyze those data to assess ventilation heterogeneity, non-linear deformation and hysteresis of lung motion to distinguish regional and global features of asthmatic lungs from those of healthy lungs during breathing. Eventually, 4D-CT data for healthy and asthmatic lungs are utilized to derive physiologically consistent boundary conditions for computational fluid dynamic (CFD) simulation of airflow in the human lungs during tidal breathing. We investigate the effect of dynamic breathing on air flow distribution and pressure drop along the central airways.