RESUMEN
BACKGROUND AND OBJECTIVE: In the last few decades, several studies have been performed to investigate traumatic brain injuries (TBIs) and to understand the biomechanical response of brain tissues, by using experimental and computational approaches. As part of computational approaches, human head finite element (FE) models show to be important tools in the analysis of TBIs, making it possible to estimate local mechanical effects on brain tissue for different accident scenarios. The present study aims to contribute to the computational approach by means of the development of three advanced FE head models for accurately describing the head tissue dynamics, the first step to predict TBIs. METHODS: We have developed three detailed FE models of human heads from magnetic resonance images of three volunteers: an adult female (32 yrs), an adult male (35 yrs), and a young male (16 yrs). These models have been validated against experimental data of post mortem human subjects (PMHS) tests available in the literature. Brain tissue displacements relative to the skull, hydrostatic intracranial pressure, and head acceleration have been used as the parameters to compare the model response with the experimental response for validation. The software CORAplus (CORrelation and Analysis) has been adopted to evaluate the bio-fidelity level of FE models. RESULTS: Numerical results from the three models agree with experimental data. FE models presented in this study show a good bio-fidelity for hydrostatic pressure (CORA score of 0.776) and a fair bio-fidelity brain tissue displacements relative to the skull (CORA score of 0.443 and 0.535). The comparison among numerical simulations carried out with the three models shows negligible differences in the mechanical state of brain tissue due to the different morphometry of the heads, when the same acceleration history is considered. CONCLUSIONS: The three FE models, thanks to their accurate description of anatomical morphology and to their bio-fidelity, can be useful tools to investigate brain mechanics due to different impact scenarios. Therefore, they can be used for different purposes, such as the investigation of the correlation between head acceleration and tissue damage, or the effectiveness of helmet designs. This work does not address the issue to define injury thresholds for the proposed models.
Asunto(s)
Lesiones Traumáticas del Encéfalo , Cabeza , Adulto , Masculino , Femenino , Humanos , Análisis de Elementos Finitos , Encéfalo/fisiología , Dispositivos de Protección de la Cabeza , Cráneo , Lesiones Traumáticas del Encéfalo/diagnóstico por imagen , Fenómenos Biomecánicos , Modelos BiológicosRESUMEN
Robotic patients show great potential for helping to improve medical palpation training, as they can provide feedback that cannot be obtained in a real patient. They provide information about internal organ deformation that can significantly enhance palpation training by giving medical trainees visual insight based on the pressure they apply for palpation. This can be achieved by using computational models of abdomen mechanics. However, such models are computationally expensive, and thus unable to provide real-time predictions. In this work, we proposed an innovative surrogate model of abdomen mechanics by using machine learning (ML) and finite element (FE) modelling to virtually render internal tissue deformation in real time. We first developed a new high-fidelity FE model of the abdomen mechanics from computerized tomography (CT) images. We performed palpation simulations to produce a large database of stress distribution on the liver edge, an area of interest in most examinations. We then used artificial neural networks (ANNs) to develop the surrogate model and demonstrated its application in an experimental palpation platform. Our FE simulations took 1.5 h to predict stress distribution for each palpation while this only took a fraction of a second for the surrogate model. Our results show that our artificial neural network (ANN) surrogate has an accuracy of 92.6%. We also showed that the surrogate model is able to use the experimental input of palpation location and force to provide real-time projections onto the robotics platform. This enhanced robotics platform has the potential to be used as a training simulator for trainees to hone their palpation skills.
RESUMEN
This paper evaluates the effects of topology and relative density of helmet lattice liners on mitigating Traumatic Brain Injury (TBI). Finite Element (FE) models of new lattice liners with prismatic and tetrahedral topologies were developed. A typical frontal head impact in motorcycle accidents was simulated, and linear and rotational accelerations of the head were recorded. A high-fidelity FE model of TBI was loaded with the accelerations to predict the brain response during the accident. The results show that prismatic lattices have better performance in preventing TBI than tetrahedral lattices and EPS that is typically used in helmets. Moreover, varying the cell size through the thickness of the liner improves its performance, but this effect was marginal. The relative density also has a significant effect, with lattices with lower relative densities providing better protection. Across different lattices studied here, the prismatic lattice with a relative density of 6% had the best performance and reduced the peak linear and rotational accelerations, Head Injury Criterion (HIC), brain strain and strain rate by 48%, 37%, 49%, 32% and 65% respectively, compared to the EPS liner. These results can be used to guide the design of lattice helmet liners for better mitigation of TBI.