Airflow and Particle Deposition Simulations in Health and Emphysema: From In Vivo to In Silico Animal Experiments
- 795 Downloads
Image-based in silico modeling tools provide detailed velocity and particle deposition data. However, care must be taken when prescribing boundary conditions to model lung physiology in health or disease, such as in emphysema. In this study, the respiratory resistance and compliance were obtained by solving an inverse problem; a 0D global model based on healthy and emphysematous rat experimental data. Multi-scale CFD simulations were performed by solving the 3D Navier–Stokes equations in an MRI-derived rat geometry coupled to a 0D model. Particles with 0.95 μm diameter were tracked and their distribution in the lung was assessed. Seven 3D–0D simulations were performed: healthy, homogeneous, and five heterogeneous emphysema cases. Compliance (C) was significantly higher (p = 0.04) in the emphysematous rats (C = 0.37 ± 0.14 cm3/cmH2O) compared to the healthy rats (C = 0.25 ± 0.04 cm3/cmH2O), while the resistance remained unchanged (p = 0.83). There were increases in airflow, particle deposition in the 3D model, and particle delivery to the diseased regions for the heterogeneous cases compared to the homogeneous cases. The results highlight the importance of multi-scale numerical simulations to study airflow and particle distribution in healthy and diseased lungs. The effect of particle size and gravity were studied. Once available, these in silico predictions may be compared to experimental deposition data.
KeywordsCFD Aerosol Multi-scale modeling Emphysema Experimental data Pulmonary mechanics Resistance Compliance Airways Rat
The authors would like to thank Mahdi Esmaily Moghadam for his help with the multi-scale simulation framework. This work was supported by grant 1R21HL087805-02 from the NHLBI (NIH), National Science Foundation Graduate Fellowship (J. M. Oakes), Burroughs Wellcome Fund Travel Grant (J. M. Oakes), the Burroughs Wellcome Fund (A. L. Marsden), the ANR-08-JCJC-0013 grant (C. Grandmont), and associated team INRIA Grant.
Conflict of interest
The authors have no conflict of interest related to the work presented in this manuscript.
- 6.Borzone, G., L. Liberona, P. Olmos, C. Sáez, M. Meneses, T. Reyes, R. Moreno, and C. Lisboa. Rat and hamster species differences in susceptibility to elastase-induced pulmonary emphysema relate to differences in elastase inhibitory capacity. Am. J. Phys. Reg. Int. Comput. Phys. 293(3):R1342–R1349, 2007.Google Scholar
- 10.Carr, I. A., N. Nemoto, R. S. Schwarz, and S. C. Shadden. Size dependent predilections of cardiogenic embolic transport. Am. J. Physiol. Heart Circ. Physiol. 305(5):H732–H739, 2013.Google Scholar
- 11.Choi, J., M. H. Tawhai, E. A. Hoffman, and C.-L. Lin. On intra- and intersubject variabilities of airflow in the human lungs. Phys. Fluids 21(10):101901:1–101901:17, 2009.Google Scholar
- 13.Comerford, A., G. Bauer, W. A. Wall. Nanoparticle transport in a realistic model of the tracheobronchial region. Int. J. Numer. Method Biomed. Eng. 26:904–914, 2010.Google Scholar
- 14.Comerford, A., C. Förster, and W. A. Wall. Structured tree impedance outflow boundary conditions for 3D lung simulations. J. Biomech. Eng. 132(8):81002:1–81002:10, 2010.Google Scholar
- 15.Cotes, J. E., D. J. Chinn, and M. R. Miller. Lung Function: Physiology, Measurement and Application to Medicine, 6th Edition. Oxford: Blackwell Publishing, 2006Google Scholar
- 18.de Rochefort, L., L. Vial, R. Fodil, X. Maître, B. Louis, D. Isabey, G. Caillibotte, M. Thiriet, J. Bittoun, E. Durand, and G. Sbirlea-Apiou. In vitro validation of computational fluid dynamic simulation in human proximal airways with hyperpolarized 3He magnetic resonance phase-contrast velocimetry. J. Appl. Physiol. 102(5):2012–2023, 2007.Google Scholar
- 20.Diamond, L. and M. O’Donnell. Pulmonary mechanics in normal rats. J. Appl. Physiol. 43(6):942–948, 1977.Google Scholar
- 21.Emami, K., E. Chia, S. Kadlecek, J. P. Macduffie-Woodburn, J. Zhu, S. Pickup, A. Blum, M. Ishii, and R. R. Rizi. Regional correlation of emphysematous changes in lung function and structure: a comparison between pulmonary function testing and hyperpolarized MRI metrics. J. Appl. Physiol. 110(1):225–235, 2011.CrossRefGoogle Scholar
- 22.Esmaily Moghadam, M., Y. Bazilevs, and A. L. Marsden. A new preconditioning technique for implicitly coupled multidomain simulations with applications to hemodynamics. Comput. Mech. 52(5):1141–1152, 2013.Google Scholar
- 24.Esmaily Moghadam, M., I. E. Vignon-Clementel, R. Figliola, and A. L. Marsden. A modular numerical method for implicit 0D/3D coupling in cardiovascular finite element simulations. J. Comput. Phys. 244:63–79, 2013.Google Scholar
- 25.Fetita, C., S. Mancini, D. Perchet, F. Prêteux, M. Thiriet, and L. Vial. An image-based computational model of oscillatory flow in the proximal part of tracheobronchial trees. Comput. Methods Biomech. Biomed. Eng. 8(4):279–293, 2005.Google Scholar
- 36.Oakes, J. M., S. Day, S. J. Weinstein, and R. J. Robinson. Flow field analysis in expanding healthy and emphysematous alveolar models using particle image velocimetry. J. Biomech. Eng. 132(2):21008:1–21008:9, 2010.Google Scholar
- 39.Oakes, J. M., E. Breen, M. Scadeng, G. S. Tchantchou, and C. Darquenne. MRI-based measurements of aerosol deposition in the lung of healthy and elastase-treated rats. (submitted).Google Scholar
- 41.Raabe, O. G., H. C. Yeh, G. J. Newton, R. F. Phalen, and D. J. Velasquez. Deposition of inhaled monodisperse aerosols in small rodents. Inhaled Part. 4(1):3–21, 1975.Google Scholar
- 43.Schmidt, J. P., S. L. Delp, M. A. Sherman, C. A. Taylor, V. S. Pande, and R. B. Altman. NIH Public Access. In: Proceedings of the IEEE, Special issue on Computational System Biology. Vol. 96(8), pp. 1266–1280, 2008.Google Scholar
- 45.Shadden, S. C. FlowVC (V. 1) [Computer Software]. https://github.com/FlowPhysics/flowVC. Accessed September 2011.
- 51.Walters, D. K., and W. H. Luke. Computational fluid dynamics simulations of particle deposition in large-scale, multigenerational lung models. J. Biomech. Eng. 133(1):011003:1–011003:8, 2011.Google Scholar