Annals of Biomedical Engineering

, Volume 43, Issue 10, pp 2538–2551 | Cite as

Patient-Specific Airway Wall Remodeling in Chronic Lung Disease

  • Mona Eskandari
  • Ware G. Kuschner
  • Ellen Kuhl


Chronic lung disease affects more than a quarter of the adult population; yet, the mechanics of the airways are poorly understood. The pathophysiology of chronic lung disease is commonly characterized by mucosal growth and smooth muscle contraction of the airways, which initiate an inward folding of the mucosal layer and progressive airflow obstruction. Since the degree of obstruction is closely correlated with the number of folds, mucosal folding has been extensively studied in idealized circular cross sections. However, airflow obstruction has never been studied in real airway geometries; the behavior of imperfect, non-cylindrical, continuously branching airways remains unknown. Here we model the effects of chronic lung disease using the nonlinear field theories of mechanics supplemented by the theory of finite growth. We perform finite element analysis of patient-specific Y-branch segments created from magnetic resonance images. We demonstrate that the mucosal folding pattern is insensitive to the specific airway geometry, but that it critically depends on the mucosal and submucosal stiffness, thickness, and loading mechanism. Our results suggests that patient-specific airway models with inherent geometric imperfections are more sensitive to obstruction than idealized circular models. Our models help to explain the pathophysiology of airway obstruction in chronic lung disease and hold promise to improve the diagnostics and treatment of asthma, bronchitis, chronic obstructive pulmonary disease, and respiratory failure.


Chronic lung disease Airway remodeling Bronchoconstriction Asthma Bronchitis Finite element analysis Patient-specific modeling 



We thank Alexander Zöllner for support with creating the initial finite element discretization. Additionally, we would like to thank Dr. Ann Leung and Marc Sofilos at Stanford University’s Department of Radiology for providing the patient-specific images and guidance, and Ken Mix and David Rhey at Altair HyperMesh for their helpful technical support. This study was supported by the National Science Foundation Graduate Research Fellowship and by the Stanford Graduate Fellowship to Mona Eskandari and by the National Science Foundation CAREER award CMMI 0952021 and by the National Institutes of Health grant U54 GM072970 to Ellen Kuhl.


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© Biomedical Engineering Society 2015

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringStanford UniversityStanfordUSA
  2. 2.Medical Service, Veterans Affairs Palo Alto Health Care System, Division of Pulmonary and Critical Care MedicineStanford UniversityStanfordUSA
  3. 3.Departments of Mechanical Engineering, Bioengineering, and Cardiothoracic SurgeryStanford UniversityStanfordUSA

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