RESUMEN
Traumatic brain injury such as that developed as a consequence of blast is a complex injury with a broad range of symptoms and disabilities. Computational models of brain biomechanics hold promise for illuminating the mechanics of traumatic brain injury (TBI) and for developing preventive devices. However, reliable material parameters are needed for models to be predictive. Unfortunately, the properties of human brain tissue are difficult to measure, and the bulk modulus of brain tissue in particular is not well-characterized. Thus, a wide range of bulk modulus values are used in computational models of brain biomechanics, spanning up to three orders of magnitude in the differences between values. However, the sensitivity of these variations on computational predictions is not known. In this work, we study the sensitivity of a 3D computational human head model to various bulk modulus values. A subject-specific human head model was constructed from T1-weighted MRI images at 2 mm3 voxel resolution. Diffusion tensor imaging provided data on spatial distribution and orientation of axonal fiber-bundles for modeling white-matter anisotropy. Non-injurious, full-field brain deformations in a human volunteer were used to assess the simulated predictions. The comparison suggests that a bulk modulus value on the order of GPa gives the best agreement with experimentally measured in vivo deformation in the human brain. Further, simulations of injurious loading suggest that bulk modulus values on the order of GPa provide the closest match with the clinical findings in terms of predicated injured regions and extent of injury.
RESUMEN
In this paper, we study the transient thermal response of skin layers to determine to which extent the surface temperature distribution reflects the properties of subsurface structures, such as benign or malignant lesions. Specifically, we conduct a detailed sensitivity analysis to interpret the changes in the surface temperature distribution as a function of variations in thermophysical properties, blood perfusion rate, metabolic heat generation and thicknesses of skin layers, using a multilayer computational model. These properties can vary from individual to individual or depend on location, external and internal influences, and in certain situations accurate property data are not available in the literature. Therefore, the uncertainties in these data could potentially affect the accuracy of the interpretation/diagnosis of a lesion in a clinical setting. In this study, relevant parameters were varied within characteristic physiological ranges, and differences in the surface temperature response were quantified. It was observed that variations in these parameters have a small influence on the surface temperature distribution. Analysis using this multilayer model was further conducted to determine the sensitivity of transient thermal response to different lesion sizes. This work validates the idea of examining the transient thermal response obtained using a thermal imaging system with the objective of lesion identification. The modeling effort and the sensitivity analysis reported in this paper comprise a portion of a comprehensive research effort involving experimentation on a skin phantom model as well as measurements on patients in a clinical setting, that are currently underway. One of the preliminary results from the ongoing clinical trial is also included to demonstrate the feasibility of the proposed approach.
