Lung radiotherapy is greatly benefitted when the tumor motion caused by breathing can be modeled. The aim of this paper is to present the importance of using anisotropic and subject-specific tissue elasticity for simulating the airflow inside the lungs. A computational-fluid-dynamics (CFD) based approach is presented to simulate airflow inside a subject-specific deformable lung for modeling lung tumor motion and the motion of the surrounding tissues during radiotherapy. A flow-structure interaction technique is employed that simultaneously models airflow and lung deformation. The lung is modeled as a poroelastic medium with subject-specific anisotropic poroelastic properties on a geometry, which was reconstructed from four-dimensional computed tomography (4DCT) scan datasets of humans with lung cancer. The results include the 3D anisotropic lung deformation for known airflow pattern inside the lungs. The effects of anisotropy are also presented on both the spatiotemporal volumetric lung displacement and the regional lung hysteresis.

A mathematical model is developed to investigate the effect of various processing parameters on pressureassisted combustion synthesis of NiTi intermetallics. Specifically, preheat and ambient temperature, particle size, initial porosity, and pressure differential are studied to determine their influence on propagation behavior and final porosity. The governing equations are solved using a high-order-implicit numerical scheme capable of accommodating the steep spatial and temporal gradients of properties. The predicted results appear plausible and consistent with the trends presented in the available literature.

This paper presents a novel method to simulate flow and deformation of the lung. The lung is assumed to behave as a poro-elastic medium with heterogeneous elastic property. The method uses a flow-structure interaction technique to simultaneously model flow within the airway and deformation of the lung lobes. The 3D lung geometry is reproduced from 4D CT scan dataset obtained on real human subjects at a Cancer Center. The non-linear Young’s modulus is estimated in a parallel study based on similar CT scan dataset. The novelty of the present technique lies in the use of onion-layer grid with distributed spatial permeability. It allows prediction of the spatial lung displacement that could be used for tracking lung tumor during radiotherapy.

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