Factors affecting the numerical response and fracture location of the GHBMC M50 rib in dynamic anterior-posterior loading

Population trends in human rib cross-sectional shapes

Rib fractures remain the most frequent thoracic injury in motor vehicle crashes. Computational human body models (HBMs) can be used to simulate these injuries and design mitigation strategies, but they require adequately detailed geometry to replicate such fractures. Due to a lack of rib cross-sectional shape data availability, most commercial HBMs use highly simplified rib sections extracted from a single individual during original HBM development. This study provides human rib shape data collected from chest CT scans of 240 females and males across the full adult age range. A cortical bone mapping algorithm extracted cross-sectional geometry from scans in terms of local periosteal position with respect to the central rib axis and local cortex thickness. Principal component analysis was used to reduce the dimensionality of these cross-sectional shape data. Linear regression found significant associations between principal component scores and subject demographics (sex, age, height, and weight) at all rib levels, and predicted scores were used to explore the expected rib cross-sectional shapes across a wide range of subject demographics. The resulting detailed rib cross-sectional shapes were quantified in terms of their total cross-sectional area and their cortical bone cross-sectional area. Average-sized female ribs were smaller in total cross-sectional area than average-sized male ribs by between 20% and 36% across the rib cage, with the greatest differences seen in the central portions of rib 6. This trend persisted although to smaller differences of 14%-29% when comparing females and males of equal intermediate weight and stature. Cortical bone cross-sectional areas were up to 18% smaller in females than males of equivalent height and weight but also reached parity in certain regions of the rib cage. Increased age from 25 to 80 years was associated with reductions in cortical bone cross-sectional area (up to 37% in females and 26% in males at mid-rib levels). Total cross-sectional area was also seen to reduce with age in females but to a lesser degree (of up to 17% in mid-rib regions). Similar regions saw marginal increases in total cross-sectional area for male ribs, indicating age affects rib cortex thickness moreso than overall rib cross-sectional size. Increased subject height was associated with increased rib total and cortical bone cross-sectional areas by approximately 25% and 15% increases, respectively, in mid-rib sections for a given 30 cm increase in height, although the magnitudes of these associations varied by sex and rib location. Increased weight was associated with approximately equal changes in both cortical bone and total cross-sectional areas in males. These effects were most prominent (around 25% increases for an addition of 50 kg) toward lower ribs in the rib cage and had only modest effects (less than 12% change) in ribs 2-4. Females saw greater increases with weight in total rib area compared to cortical bone area, of up to 21% at the eighth rib level. Results from this study show the expected shapes of rib cross-sections across the adult rib cage and across a broad range of demographics. This detailed geometry can be used to produce accurate rib models representing widely varying populations.

A deep learning-based pipeline for developing multi-rib shape generative model with populational percentiles or anthropometrics as predictors

Rib cross-sectional shapes (characterized by the outer contour and cortical bone thickness) affect the rib mechanical response under impact loading, thereby influence the rib injury pattern and risk. A statistical description of the rib shapes or their correlations to anthropometrics is a prerequisite to the development of numerical human body models representing target demographics. Variational autoencoders (VAE) as anatomical shape generators remain to be explored in terms of utilizing the latent vectors to control or interpret the representativeness of the generated results. In this paper, we propose a pipeline for developing a multi-rib cross-sectional shape generative model from CT images, which consists of the achievement of rib cross-sectional shape data from CT images using an anatomical indexing system and regular grids, and a unified framework to fit shape distributions and associate shapes to anthropometrics for different rib categories. Specifically, we collected CT images including 3193 ribs, surface regular grid is generated for each rib based on anatomical coordinates, the rib cross-sectional shapes are characterized by nodal coordinates and cortical bone thickness. The tensor structure of shape data based on regular grids enable the implementation of CNNs in the conditional variational autoencoder (CVAE). The CVAE is trained against an auxiliary classifier to decouple the low-dimensional representations of the inter- and intra- variations and fit each intra-variation by a Gaussian distribution simultaneously. Random tree regressors are further leveraged to associate each continuous intra-class space with the corresponding anthropometrics of the subjects, i.e., age, height and weight. As a result, with the rib class labels and the latent vectors sampled from Gaussian distributions or predicted from anthropometrics as the inputs, the decoder can generate valid rib cross-sectional shapes of given class labels (male/female, 2nd to 11th ribs) for arbitrary populational percentiles or specific age, height and weight, which paves the road for future biomedical and biomechanical studies considering the diversity of rib shapes across the population.

Explaining and predicting the increased thorax injury in aged females: age and subject-specific thorax geometry coupled with improved bone constitutive models and age-specific material properties evaluated in side impact conditions

Predicting and understanding thorax injury is fundamental for the assessment and development of safety systems to mitigate injury risk to the increasing and vulnerable aged population. While computational human models have contributed to the understanding of injury biomechanics, contemporary human body models have struggled to predict rib fractures and explain the increased incidence of injury in the aged population. The present study enhanced young and aged human body models (HBMs) by integrating a biofidelic cortical bone constitutive model and population-based bone material properties. The HBMs were evaluated using side impact sled tests assessed using chest compression and number of rib fractures. The increase in thoracic kyphosis and the associated change in rib angle with increasing age, led to increased rib torsional moment increasing the rib shear stress. Coupled with and improved cortical bone constitutive model and aged material properties, the higher resulting shear stress led to an increased number of rib fractures in the aged model. The importance of shear stress resulting from torsional load was further investigated using an isolated rib model. In contrast, HBM chest compression, a common thorax injury-associated metric, was insensitive to the aging factors studied. This study proposes an explanation for the increased incidence of thorax injury with increasing age reported in epidemiological data, and provides an enhanced understanding of human rib mechanics that will benefit assessment and design of future safety systems.

Cross-sectional properties of rib geometry from an adult population

Introduction: Human body models (HBMs) play a key role in improving modern vehicle safety systems to protect broad populations. However, their geometry is commonly derived from single individuals chosen to meet global anthropometric targets, thus their internal anatomy may not fully represent the HBM's target demographic. Past studies show sixth rib cross-sectional geometry differences between HBM ribs and population-derived ribs, and corrections to HBM ribs based on these data have improved HBM's abilities to predict rib fracture locations. Methods: We measure and report average and standard deviations (SDs) in rib cross-sectional geometric properties derived from live subject CT scans of 240 adults aged 18-90. Male and female results are given as functions of rib number and rib lengthwise position for ribs 2 through 11. Population means/SDs are reported for measures of rib total area, rib cortical bone area, and rib endosteal area, as well as inertial moment properties of these rib sections. These population corridors are compared between males and females, and against the baseline rib geometries defined in six current HBMs. Results: Total cross-sectional area results found average males ribs to be larger than those of females by between approximately 1-2 SDs depending on rib number and position, and larger in cortical bone cross-sectional area by between 0-1 SDs. Inertial moment ratios showed female ribs being between approximately 0-1 SDs more elongated than male ribs, dependent again on rib number and position. Rib cross-sectional areas from 5 of the 6 HBMs were found to be overly large along substantial portions of most ribs when compared to average population corridors. Similarly, rib aspect ratios in HBMs deviated from average population data by up to 3 SDs in regions towards sternal rib ends. Discussion: Overall, while most HBMs capture overall trends such as reductions in cross-section along shaft lengths, many also exhibit local variation that deviates from population trends. This study's results provide the first reference values for assessing the cross-sectional geometry of human ribs across a wide range of rib levels. Results also further provide clear guidelines to improve rib geometry definitions present in current HBMs in order to better represent their target demographic.

Influences of human thorax variability on population rib fracture risk prediction using human body models

Rib fractures remain a common injury for vehicle occupants in crashes. The risk of a human sustaining rib fractures from thorax loading is highly variable, potentially due to a variability in individual factors such as material properties and geometry of the ribs and ribcage. Human body models (HBMs) with a detailed ribcage can be used as occupant substitutes to aid in the prediction of rib injury risk at the tissue level in crash analysis. To improve this capability, model parametrization can be used to represent human variability in simulation studies. The aim of this study was to identify the variations in the physical properties of the human thorax that have the most influence on rib fracture risk for the population of vehicle occupants. A total of 15 different geometrical and material factors, sourced from published literature, were varied in a parametrized SAFER HBM. Parametric sensitivity analyses were conducted for two crash configurations, frontal and near-side impacts. The results show that variability in rib cortical bone thickness, rib cortical bone material properties, and rib cross-sectional width had the greatest influence on the risk for an occupant to sustain two or more fractured ribs in both impacts. Therefore, it is recommended that these three parameters be included in rib fracture risk analysis with HBMs for the population of vehicle occupants.