Abstract
Changes in the shape of the lung during breathing determine the movement of airways and alveoli, and thus impact airflow dynamics. Modeling airflow dynamics in health and disease is a key goal for predictive multiscale models of respiration. Past efforts to model changes in lung shape during breathing have measured shape at multiple breath-holds. However, breath-holds do not capture hysteretic differences between inspiration and expiration resulting from the additional energy required for inspiration. Alternatively, imaging dynamically – without breath-holds – allows measurement of hysteretic differences. In this study, we acquire multiple micro-CT images per breath (4DCT) in live rats, and from these images we develop, for the first time, dynamic volume maps. These maps show changes in local volume across the entire lung throughout the breathing cycle and accurately predict the global pressure-volume (PV) hysteresis. Male Sprague-Dawley rats were given either a full- or partial-lung dose of elastase or saline as a control. After three weeks, 4DCT images of the mechanically ventilated rats under anesthesia were acquired dynamically over the breathing cycle (11 time points, ≤100 ms temporal resolution, 8 cmH2O peak pressure). Non-rigid image registration was applied to determine the deformation gradient – a numerical description of changes to lung shape – at each time point. The registration accuracy was evaluated by landmark identification. Of 67 landmarks, one was determined misregistered by all three observers, and 11 were determined misregistered by two observers. Volume change maps were calculated on a voxel-by-voxel basis at all time points using both the Jacobian of the deformation gradient and the inhaled air fraction. The calculated lung PV hysteresis agrees with pressure-volume curves measured by the ventilator. Volume maps in diseased rats show increased compliance and ventilation heterogeneity. Future predictive multiscale models of rodent respiration may leverage such volume maps as boundary conditions.
Highlights
There is increasing interest in computational fluid dynamics (CFD) modeling of airflow and tissue mechanics in the respiratory tract for purposes of predicting particulate deposition and clearance, uptake of vapors, and disease onset and progression [1,2,3,4,5]
A key component, and a major hurdle, to the development of realistic transient CFD models is a quantitative understanding of lung architecture and tissue mechanics, including strain and compliance, and how the parenchyma and airways move during the hysteretic breathing cycle
This paper describes acquisition of dynamic 4D CT images, the non-rigid registration of the images, and the calculation of tissue dynamics and local ventilation with high spatial and temporal resolution
Summary
There is increasing interest in computational fluid dynamics (CFD) modeling of airflow and tissue mechanics in the respiratory tract for purposes of predicting particulate deposition and clearance, uptake of vapors, and disease onset and progression [1,2,3,4,5]. As transient CFD models are developed, the dynamics and hysteresis of the full breathing cycle must be accounted for with real time-dependent structural data obtained from in vivo imaging during breathing In this way, the different dynamics of inhale and of exhale, which are lost during breath-hold, can be properly incorporated. This paper describes acquisition of dynamic 4D CT images (multi-time-point 3D images acquired without breath-hold), the non-rigid registration of the images, and the calculation of tissue dynamics and local ventilation with high spatial and temporal resolution
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