Abstract: This study compares the effect of the extra-thoracic airways on the flow field through the lower airways by carrying out computational fluid dynamics (CFD) simulations of the airflow through the human respiratory tract. In order to facilitate this comparison, two geometries were utilized. The first was a realistic nine-generation lower airway geometry derived from computed tomography (CT) images, while the second included an additional component, i.e., an idealized extra-thoracic airway (ETA) coupled with the same nine-generation CT model. Another aspect of this study focused on the impact of breathing transience on the flow field. Consequently, simulations were carried out for transient breathing in addition to peak inspiration and expiration. Physiologically-appropriate regional ventilation for two different flow rates was induced at the distal boundaries by imposing appropriate lobar specific flow rates. The scope of these simulations was limited to the modeling of tidal breathing at rest. The typical breathing rates for these cases range from 7.5 to 15 breaths per minute with a tidal volume of 0.5L. For comparison, the flow rates for constant inspiration/expiration were selected to be identical to the peak flow rates during the transient breathing. Significant differences were observed from comparing the peak inspiration and expiration with transient breathing in the entire airway geometry. Differences were also observed for the lower airway geometry. These differences point to the fact that simulations that utilize constant inspiration or expiration may not be an appropriate approach to gain better insight into the flow patterns present in the human respiratory system. Consequently, particle trajectories derived from these flow fields might be misleading in their applicability to the human respiratory system.

Abstract: Modeling of fluid mechanics for the vascular system is of great value as a source of knowledge about development, progression, and treatment of cardiovascular disease. Full three-dimensional simulation of blood flow in the whole human body is a hard computational problem. We discuss parallel decomposition of blood flow simulation as a graph partitioning problem. The detailed model of full human arterial tree and some simpler geometries are discussed. The effectiveness of coarse-graining as well as pure spectral approaches is studied. Published data can be useful for development of parallel hemodynamic applications as well as for estimation of their effectiveness and scalability.

Abstract: We describe an algorithm to generate a normative infant cranial model from the input of 3D meshes that are extracted from CT scans of normal infant skulls. We generate a correspondence map between meshes based on a registration algorithm. Then we apply our averaging algorithm to construct the normative model. The goal of this normal model is to assist an objective evaluating system to analyze the efficacy of plastic surgeries.

Abstract: Targeting deep regions in the brain is a key challenge in noninvasive transcranial electrical neuromodulation. We explore this problem by means of computer simulations within a detailed seven-tissue finite element head model (2 millions tetrahedrons) constructed from high resolution MRI and CT volumes. We solve the forward electrical stimulation and EEG problems governed by the quasi-static Poisson equation numerically using the first order Finite Element Method (FEM) with the Galerking approach. Given a dense array of EEG-electrode layout and location of regions of interest inside the brain, we compute optimal current injection patterns based on the reciprocity principle in EEG and compare results with optimization based on the Least Squares (LS) or Linearly Constrained Minimum Variance (LCMV) algorithms. It is found that the reciprocity algorithms show good performance comparable to the LCMV and LS solutions for deep brain targets which are generally computationally more expensive to obtain.

Abstract: Supermodeling is an interesting and non-standard concept used recently for simulation of complex and chaotic systems such as climate and weather dynamics. It consists in coupling of many imperfect models to create a single supermodel. We discuss here supermodeling strategy in the context of tumor growth. To check its adaptive flexibility we have developed a basic, but still computationally complex, modeling framework of melanoma growth. The supermodel of melanoma consists of a few coupled sub-models, which differ in values of a parameter responsible for tumor cells and extracellular matrix interactions. We demonstrate that due to synchronization of sub-models, the supermodel is able to simulate qualitatively different modes of cancer growth than those observed for a single model. These scenarios correspond to the basic types of melanoma cancer. This property makes the supermodel very flexible to be fit to real data. On the basis of preliminary simulation results, we discuss the prospects of supermodeling strategy as a promising coupling factor between both formal and data-based models of tumor.