| الوصف: |
Inert gases are used as markers or contrast agents in experimental and imaging methods that measure lung function. The premise is that by analysing the expired concentrations of inert gas, or by quantifying their distribution from \textit{in vivo} imaging, information can be gained about the ventilatory function of the lung. The inert gases that are used in the various methods can have very different physical properties, which potentially affects their distribution and hence the interpretation of the tests. Mathematical models have previously been derived to study expired inert gas washout, however they have generally had simple geometric structure and lack any contribution from airway resistance and tissue compliance. These models have provided much of the current understanding of the general features of expired gas washout tests, but they cannot be used to bridge to regional ventilation data acquired using contrast-enhanced \textit{in vivo} imaging. The goal of this thesis is to develop a mathematical model for predicting inert gas transport and mixing in the lung, such that the model includes the important mechanisms that contribute to the dynamics of the multiple breath nitrogen washout test and the distribution of xenon during contrast enhanced computed tomography imaging. A series of computational models are developed to simulate the distribution of inhaled gas, and its mixing in the periphery of the lung. The models are generated from anatomical and physiological data, and function within them is governed by physical laws. Advective flow distribution in the airways is dependent upon the density and viscosity of the gas being transported, the airway geometry, and the regional compliance of the parenchyma. One of the important features of a model is that it should not be more complicated than is necessary (Occams razor). The models that are developed here include complex geometry, however it is the effect of this complex geometry that it is necessary to study in linking the regional heterogeneity of ... |
