title: Dynamic flow characteristics in normal and asthmatic lungs.
abstract: Complex flow patterns exist within the asymmetric branching airway network in the lungs. These flow patterns are known to become increasingly heterogeneous during disease as a result of various mechanisms such as bronchoconstriction or alterations in lung tissue compliance. Here, we present a coupled model of tissue deformation and network airflow enabling predictions of dynamic flow properties, including temporal flow rate, pressure distribution, and the occurrence of reverse flows. We created two patient-specific airway geometries, one for a healthy subject and one for a severe asthmatic subject, derived using a combination of high-resolution CT data and a volume-filling branching algorithm. In addition, we created virtually constricted airway geometry by reducing the airway radii of the healthy subject model. The flow model was applied to these three different geometries to solve the pressure and flow distribution over a breathing cycle. The differences in wave phase of the flows in parallel airways induced by asymmetric airway geometry and bidirectional interaction between intra-acinar and airway network pressures were small in central airways but were more evident in peripheral airways. The asthmatic model showed elevated ventilation heterogeneity and significant flow disturbance. The reverse flows in the asthmatic model not only altered the local flow characteristics but also affected total lung resistance. The clinical significance of temporal flow disturbance on lung ventilation in normal airway model is obscure. However, increased flow disturbance and ventilation heterogeneity observed in the asthmatic model suggests that reverse flow may be an important factor for asthmatic lung function.
authors.affiliation: Department of Computer Science, University of Oxford, Oxford, UK.
authors.forename: Minsuok
authors.initials: M
authors.lastname: Kim
authors.affiliation: Department of Computer Science, University of Oxford, Oxford, UK.
authors.forename: Rafel
authors.initials: R
authors.lastname: Bordas
authors.affiliation: FluidDA, Groeningenlei 132, Kontich, 2550, Belgium.
authors.forename: Wim
authors.initials: W
authors.lastname: Vos
authors.affiliation: Institute for Lung Health, Department of Infection, Immunity and Inflammation, University Hospitals of Leicester, Leicester, UK.
authors.forename: Ruth A
authors.initials: RA
authors.lastname: Hartley
authors.affiliation: Institute for Lung Health, Department of Infection, Immunity and Inflammation, University Hospitals of Leicester, Leicester, UK.
authors.forename: Chris E
authors.initials: CE
authors.lastname: Brightling
authors.affiliation: Department of Computer Science, University of Oxford, Oxford, UK.
authors.forename: David
authors.initials: D
authors.lastname: Kay
authors.affiliation: Institute of Biomedical Engineering, Department of Engineering Science and Oxford e-Research Centre, University of Oxford, Oxford, UK.
authors.forename: Vicente
authors.initials: V
authors.lastname: Grau
authors.affiliation: Department of Computer Science, University of Oxford, Oxford, UK.
authors.forename: Kelly S
authors.initials: KS
authors.lastname: Burrowes
mesh_terms: D001249:Asthma / Q000503:physiopathology*; D016084:Bronchoconstriction; D005260:Female; D006801:Humans; D001239:Inhalation; D008168:Lung / Q000503:physiopathology*; D008875:Middle Aged; D008954:Models, Biological; D012123:Pulmonary Ventilation; D012212:Rheology
keywords: airway constriction; airway network; asthma; lung ventilation model; reverse flow