SUMMARYIn this paper, we propose efficient and robust unstructured mesh generation methods based on computed tomography (CT) and magnetic resonance imaging (MRI) data, in order to obtain a patient-specific geometry for high-fidelity numerical simulations. Surface extraction from medical images is carried out mainly using open source libraries, including the Insight Segmentation and Registration Toolkit and the Visualization Toolkit, into the form of facet surface representation. To create high-quality surface meshes, we propose two approaches. One is a direct advancing front method, and the other is a modified decimation method. The former emphasizes the controllability of local mesh density, and the latter enables semi-automated mesh generation from low-quality discrete surfaces. An advancingfront-based volume meshing method is employed. Our approaches are demonstrated with high-fidelity tetrahedral meshes around medical geometries extracted from CT/MRI data.
The objective of this study was to examine the role of body mass and subcutaneous fat in injury severity and pattern sustained by overweight drivers. Finite element models were created to represent the geometry and properties of subcutaneous adipose tissue in the torso with data obtained from reconstructed magnetic resonance imaging datasets. The torso adipose tissue models were then integrated into the standard multibody dummy models together with increased inertial parameters and sizes of the limbs to represent overweight occupants. Frontal crash simulations were performed considering a variety of occupant restraint systems and regional body injuries were measured. The results revealed that differences in body mass and fat distribution have an impact on injury severity and pattern. Even though the torso adipose tissue of overweight subjects contributed to reduce abdominal injury, the momentum effect of a greater body mass of overweight subjects was more dominant over the cushion effect of the adipose tissue, increasing risk of other regional body injuries except abdomen. Through statistical analysis of the results, strong correlations (p < 0.01) were found between body mass index and regional body injuries except neck injury. The analysis also revealed that a greater momentum of overweight males leads to greater forward torso and pelvic excursions that account for higher risks (p < 0.001) of head, thorax, and lower extremity injury than observed in non-overweight males. The findings have important implications for improving the vehicle and occupant safety systems designed for the increasing global obese population.
Objective
To investigate the risk and injury severity on the regional body (head, neck, and chest) of obese children in frontal motor vehicle crashes.
Design and Methods
No physical surrogates (i.e., crash dummies) for obese children are available and experiments on pediatric cadavers are generally not feasible. Therefore, we developed computational models of obese children using medical imaging processing and state-of-the-art modeling techniques. A hybrid modeling technique was used to integrate finite element model for torso fat layer into the standard multibody model to represent various levels of obese children for 3- and 6-year-old age group. The models were used to investigate injury severity under various crash scenarios through model simulations.
Results
The head injury criterion and chest acceleration were observed to increase as body mass index (BMI) increased. Meanwhile, no such correlations were found between BMI and neck injury and chest deformation. Forward head and torso excursions were observed to increase as obesity increased, owing to the momentum effect of greater body mass.
Conclusions
Obese children appear to have greater risks of the head and chest injuries than do their non-obese counterparts in frontal motor vehicle crashes, owing to higher head and chest accelerations induced by greater body excursion.
In 2010, the National Institute for Occupational Safety and Health (NIOSH) published new digital head form models based on their recently updated fit-test panel. The new panel, based on the 2000 census to better represent the modern work force, created two additional sizes: Short/Wide and Long/Narrow. While collecting the anthropometric data that comprised the panel, additional three-dimensional data were collected on a subset of the subjects. Within each sizing category, five individuals' three-dimensional data were used to create the new head form models. While NIOSH has recommended a switch to a five-size system for designing respirators, little has been done in assessing the potential benefits of this change. With commercially available elastomeric facepieces available in only three or four size systems, it was necessary to develop the facepieces to enable testing. This study aims to develop a method for designing and fabricating elastomeric facepieces tailored to the new head form designs for use in fit-testing studies. This novel method used computed tomography of a solid silicone facepiece and a number of computer-aided design programs (VolView, ParaView, MEGG3D, and RapidForm XOR) to develop a facepiece model to accommodate the Short/Wide head form. The generated model was given a physical form by means of three-dimensional printing using stereolithography (SLA). The printed model was then used to create a silicone mold from which elastomeric prototypes can be cast. The prototype facepieces were cast in two types of silicone for use in future fit-testing.
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