This paper presents a novel computational method for micromechanical modeling of adipose tissue. The model can be regarded as the first step for developing an inversion based framework that uses adipose stiffness data obtained from elastography to determine its microstructural alterations. Such information can be used as biomarkers for diseases associated with adipose tissue microstructure alteration (e.g. adipose tissue fibrosis and inflammation in obesity). In contrast to previous studies, the presented model follows a multiphase structure which accounts for both solid and fluid components as well as their mechanical interaction. In the model, the lipid droplets and extracellular matrix were considered as the fluid and solid phase, respectively. As such, the fluid-structure interaction (FSI) problem was solved using finite element method. In order to gain insight into how microstructural characteristics influence the macro scale mechanical properties of the adipose tissue, a compression mechanical test was simulated using the FSI model and its results were fitted to corresponding experimental data. The simulation procedure was performed for adipocytes in healthy conditions while the stiffness of extracellular matrix in normal adipose tissue was found by varying it systematically within an optimization process until the simulation response agreed with experimental data. Results obtained in this study are encouraging and show the capability of the proposed model to capture adipose tissue macroscale mechanical behavior based on its microstructure under health and different pathological conditions.
Lung cancer is one of the leading causes of cancer death in men and women. External Beam Radiation Therapy (EBRT) is a commonly used primary treatment for the condition. A major challenge with such treatments is the delivery of sufficient radiation dose to the lung tumor while ensuring that surrounding healthy lung parenchyma receives only minimal dose. This can be achieved by coupling EBRT with respiratory computer models which can predict the tumour location as a function of phase during the breathing cycle1. The diaphragm muscle contraction is mainly responsible for a large portion of the lung tumor motion during normal breathing, especially when tumours are in the lower lobes, therefore the importance of accurately modelling the diaphragm is paramount in lung tumour motion prediction. The goal of this research is to develop a biomechanical model of the diaphragm, including its active and passive response, using detailed geometric, biomechanical and anatomical information that mimics the diaphragmatic behaviour in a patient specific manner. For this purpose, a Finite Element Model (FEM) of the diaphragm was developed in order to predict the in vivo motion of the diaphragm, paving the way for computer assisted lung cancer tumor tracking in EBRT. Preliminary results obtained from the proposed model are promising and they indicate that it can be used as a plausible tool for effective lung cancer EBRT to improve patient care.