Statistical shape analysis of the human spleen geometry for probabilistic occupant models

Kidney shape statistical analysis: associations with disease and anthropometric factors

BACKGROUND

Organ measurements derived from magnetic resonance imaging (MRI) have the potential to enhance our understanding of the precise phenotypic variations underlying many clinical conditions.

METHODS

We applied morphometric methods to study the kidneys by constructing surface meshes from kidney segmentations from abdominal MRI data in 38,868 participants in the UK Biobank. Using mesh-based analysis techniques based on statistical parametric maps (SPMs), we were able to detect variations in specific regions of the kidney and associate those with anthropometric traits as well as disease states including chronic kidney disease (CKD), type-2 diabetes (T2D), and hypertension. Statistical shape analysis (SSA) based on principal component analysis was also used within the disease population and the principal component scores were used to assess the risk of disease events.

RESULTS

We show that CKD, T2D and hypertension were associated with kidney shape. Age was associated with kidney shape consistently across disease groups. Body mass index (BMI) and waist-to-hip ratio (WHR) were also associated with kidney shape for the participants with T2D. Using SSA, we were able to capture kidney shape variations, relative to size, angle, straightness, width, length, and thickness of the kidneys, within disease populations. We identified significant associations between both left and right kidney length and width and incidence of CKD (hazard ratio (HR): 0.74, 95% CI: 0.61-0.90, p < 0.05, in the left kidney; HR: 0.76, 95% CI: 0.63-0.92, p < 0.05, in the right kidney) and hypertension (HR: 1.16, 95% CI: 1.03-1.29, p < 0.05, in the left kidney; HR: 0.87, 95% CI: 0.79-0.96, p < 0.05, in the right kidney).

CONCLUSIONS

The results suggest that shape-based analysis of the kidneys can augment studies aiming at the better categorisation of pathologies associated with chronic kidney conditions.

Porcine spleen as a model organ for blunt injury impact tests: An experimental and histological study

The spleen is a large and highly vascularized secondary lymphatic organ. Spleen injuries are among the most frequent trauma-related injuries in the abdominal region. The aims of the study were to assess the volume fractions of the main splenic tissue components (red pulp, white pulp, trabeculae and reticular fibres) and to determine the severity of splenic injury due to the experimental impact test. Porcine spleens (n = 17) were compressed by 6.22 kg wooden plate using a drop tower technique from three impact heights (50, 100 and 150 mm corresponding to velocities 0.79, 1.24 and 1.58 m/s). The pressure was measured via catheters placed in the splenic vein. The impact velocity was measured using lasers. The severity of induced injuries was analysed on the macroscopic level. The volume fractions of splenic components were assessed microscopically using stereology. The volume fraction of the red pulp was 76.4%, white pulp 21.3% and trabeculae 2.7% respectively. All impact tests, even with the low impact velocities, led to injuries that occurred mostly in the dorsal extremity of the spleen, and were accompanied by bleeding, capsule rupture and parenchyma crushing. Higher impact height (impact velocity and impact energy) caused more severe injury. Porcine spleen had the same volume fraction of tissue components as human spleen, therefore we concluded that the porcine spleen was a suitable organ model for mechanical experiments. Based on our observations, regions around hilum and the diaphragmatic surface of the dorsal extremity, that contained fissures and notches, were the most prone to injury and required considerable attention during splenic examination after injury. The primary mechanical data are now available for the researchers focused on the splenic trauma modelling.

Finite Element Methods for Modeling the Pressure Distribution in Human Body–Seat Interactions: A Systematic Review

The objective of this systematic review is to investigate the various approaches that have been undertaken in finite element analysis (FEA) of human–seat interactions and synthesize the existing knowledge. With advances in numerical simulation and digital human modeling, FEA has emerged as a powerful tool to study seating comfort and discomfort. FEA employs various biomechanical factors to predict the contact stress and pressure distribution in a particular seat design. Given the complexity of human–seat interaction, several modeling and processing steps are required to conduct realistic FEA. The steps of how to perform an FEA simulation on human–seat interactions, the different models used, the model mesh compositions, and the material properties are discussed and reviewed in this paper. This can be used as a guideline for future studies in the context of FEA of human–seat interactions.

A parametric head geometry model accounting for variation among adolescent and young adult populations

BACKGROUND AND OBJECTIVE

Modeling the size and shape of human skull and scalp is essential for head injury assessment, design of helmets and head-borne equipment, and many other safety applications. Finite element (FE) head models are important tools to assess injury risks and design personal protective equipment. However, current FE head models are mainly developed based on the midsize male, failing to account for the significant morphological variation that exists in the skull and brain. The objective of this study was to develop a statistical head geometry model that accounts for size and shape variations among the adolescent and young adult population.

METHODS

To represent subject-specific geometry using a homologous mesh, threshold-based segmentation of head CT scans of 101 subjects between 14 and 25 years of age was performed, followed by landmarking, mesh morphing, and projection. Skull and scalp statistical geometry models were then developed as functions of age, sex, stature, BMI, head length, head breadth, and tragion-to-top of head using generalized Procrustes analysis (GPA), principal component analysis (PCA) and multivariate regression analysis.

RESULTS

The statistical geometry models account for a high percentage of morphological variations in scalp geometry (R²=0.63), outer skull geometry (R²=0.66), inner skull geometry (R²=0.55), and skull thickness (error < 1 mm) CONCLUSIONS: Skull and scalp statistical geometry models accounts for size and shape variations among the adolescent and young adult population were developed as functions of subject covariates. These models may serve as the geometric basis to develop individualized head FE models for injury assessment and design of head-borne equipment.

Using Statistical Shape and Appearance Modelling to characterise the 3D shape and material properties of human lumbar vertebrae: A proof of concept study

Patient variation affects the outcomes of a range of spinal interventions, from disc replacement to vertebral fixation and vertebroplasty. Statistical Shape and Appearance Modelling (SSAM) can be used to describe anatomical variation and pathological differences within the population. To better understand how bone density and shape variation affect load transfer with respect to surgical treatments, Finite Element (FE) models can be generated from a SSAM. The aim for this study is to understand whether geometric and density variation as well as multiple vertebral levels can be incorporated into a single SSAM and whether this can be used to investigate the relationships between, and effects of, the various modes of variation. FE models of 14 human lumbar vertebrae that had been μCT imaged and validated through experimental testing were used as input specimens for a SSAM. The validity of the SSAM was evaluated by using principal component analysis to identify the primary modes of geometric and bone density variation and comparing to those in the input set. FE models were generated from the SSAM to examine the response to loading. The mean error between the input set and generated models for volume, mean density and FE compressive stiffness were 10%, 3% and 10% respectively. Principal Component (PC) 1 captured the majority of the bone density variation. The remaining PCs described specific geometric variation. The FE models generated from the SSAM showed the variations in vertebral stiffness as a result of complex relationships between bone density and shape. The SSAM created has limited data for its input set, however, it acts as a proof of concept for the novel combination of material and shape variation into a single shape model. This approach and the tools developed can be applied to wider patient groups and treatment scenarios to improve patient stratification and to optimise treatments.

A statistical lumbar spine geometry model accounting for variations by Age, Sex, Stature, and body mass index

The objective of this study was to develop a statistical lumbar spine geometry model accounting for morphological variations among the adult population. Five lumber vertebrae and lumber spine curvature were collected from CT scans of 82 adult subjects through CT segmentation, landmark identification, and template mesh mapping. Generalized Procrustes Analysis (GPA), Principal Component Analysis (PCA), and multivariate regression analysis were conducted to develop the statistical lumbar spine model. Two statistical models were established to predict the vertebrae geometry and lumbar curvature respectively. Using the statistical models, a lumbar spine finite element (FE) model could be rapidly generated with a given set of age, sex, stature, and body mass index (BMI). The results showed that the lumbar spine vertebral size was significantly affected by stature, sex and age, and the lumbar spine curvature was significantly affected by stature and age. This statistical lumbar spine model could serve as the geometric basis for quantifying effects of human characteristics on lumbar spine injury risks in impact conditions.

Statistical shape analysis of gravid uteri throughout pregnancy by a ray description technique

In order to study the anatomical variability of the uterus induced by pregnancy, a parametrization of gravid uterine geometry based on principal component analysis (PCA) is proposed. Corresponding meshes used for PCA are created by a ray description technique applied to a reference mesh. A smoothed voxel-based methodology is applied to determine the reference mesh from a database of 11 real shapes produced by the FEMONUM project. The ray-based correspondence technique is compared to two existing methods (He, Giessen) as well as a proposed mixed method. Principal component analysis results are based on a database of 11 existing shapes. Results of the parametrization show that 90% of the total variance of the database can be represented with four new shape parameters and that a wide spectrum of shapes can be generated. Graphical Abstract Proposed correspondence technique compared to existing methods.

Predicting pelvis geometry using a morphometric model with overall anthropometric variables

Pelvic fractures have been identified as the second most common AIS2+ injury in motor vehicle crashes, with the highest early mortality rate compared to other orthopaedic injuries. Further, the risk is associated with occupant sex, age, stature and body mass index (BMI). In this study, clinical pelvic CT scans from 132 adults (75 females, 57 males) were extracted from a patient database. The population shape variance in pelvis bone geometry was studied by Sparse Principal Component Analysis (SPCA) and a morphometric model was developed by multivariate linear regression using overall anthropometric variables (sex, age, stature, BMI). In the analysis, SPCA identified 15 principal components (PCs) describing 83.6% of the shape variations. Eight of these were significantly captured (α < 0.05) by the morphometric model, which predicted 29% of the total variance in pelvis geometry. The overall anthropometric variables were significantly related to geometrical features primarily in the inferior-anterior regions while being unable to significantly capture local sacrum features, shape and position of ASIS and lateral tilt of the iliac wings. In conclusion, a new detailed morphometric model of the pelvis bone demonstrated that overall anthropometric variables account for only 29% of the variance in pelvis geometry. Furthermore, variations in the superior-anterior region of the pelvis, with which the lap belt is intended to interact, were not captured. Depending on the scenario, shape variations not captured by overall anthropometry could have important implications for injury prediction in traffic safety analysis.

Subject-specific rib finite element models with material data derived from coupon tests under bending loading

Rib fractures are common thoracic injuries in motor vehicle crashes. Several human finite element (FE) human models have been created to numerically assess thoracic injury risks. However, the accurate prediction of rib biomechanical response has shown to be challenging due to human variation and modeling approaches. The main objective of this study was to better understand the role of modeling approaches on the biomechanical response of human ribs in anterior-posterior bending. Since the development of subject specific rib models is a time-consuming process, the second objective of this study was to develop an accurate morphing approach to quickly generate high quality subject specific rib meshes. The exterior geometries and cortical-trabecular boundaries of five human 6th-level ribs were extracted from CT-images. One rib mesh was developed in a parametric fashion and the other four ribs were developed with an in-house morphing algorithm. The morphing algorithm automatically defined landmarks on both the periosteal and endosteal boundaries of the cortical layer, which were used to morph the template nodes to target geometries. Three different cortical bone material models were defined based on the stress-strain data obtained from subject-specific tensile coupon tests for each rib. Full rib anterior-posterior bending tests were simulated based on data recorded in testing. The results showed similar trends to test data with some sensitivity relative to the material modeling approach. Additionally, the FE models were substantially more resistant to failure, highlighting the need for better techniques to model rib fracture. Overall, the results of this work can be used to improve the biofidelity of human rib finite element models.

Pelvis and femur shape prediction using principal component analysis for body model on seat comfort assessment. Impact on the prediction of the used palpable anatomical landmarks as predictors

A personalized pelvis and femur shape is required to build a finite element buttock thigh model when experimentally investigating seating discomfort. The present study estimates the shape of pelvis and femur using a principal component analysis (PCA) based method with a limited number of palpable anatomical landmarks (ALs) as predictors. A leave-one-out experiment was designed using 38 pelvises and femurs from a same sample of adult specimens. As expected, prediction errors decrease with the number of ALs. Using the maximum number of easily palpable ALs (13 for pelvis and 4 for femur), average errors were 5.4 and 4.8 mm respectively for pelvis and femur. Better prediction was obtained when the shapes of pelvis and femur were predicted separately without merging the data of both bones. Results also show that the PCA based method is a good alternative to predict hip and lumbosacral joint centers with an average error of 5.0 and 9.2 mm respectively.

Probabilistic liver atlas construction

BACKGROUND

Anatomical atlases are 3D volumes or shapes representing an organ or structure of the human body. They contain either the prototypical shape of the object of interest together with other shapes representing its statistical variations (statistical atlas) or a probability map of belonging to the object (probabilistic atlas). Probabilistic atlases are mostly built with simple estimations only involving the data at each spatial location.

RESULTS

A new method for probabilistic atlas construction that uses a generalized linear model is proposed. This method aims to improve the estimation of the probability to be covered by the liver. Furthermore, all methods to build an atlas involve previous coregistration of the sample of shapes available. The influence of the geometrical transformation adopted for registration in the quality of the final atlas has not been sufficiently investigated. The ability of an atlas to adapt to a new case is one of the most important quality criteria that should be taken into account. The presented experiments show that some methods for atlas construction are severely affected by the previous coregistration step.

CONCLUSION

We show the good performance of the new approach. Furthermore, results suggest that extremely flexible registration methods are not always beneficial, since they can reduce the variability of the atlas and hence its ability to give sensible values of probability when used as an aid in segmentation of new cases.

A finite element model of a six-year-old child for simulating pedestrian accidents

Child pedestrian protection deserves more attention in vehicle safety design since they are the most vulnerable road users who face the highest mortality rate. Pediatric Finite Element (FE) models could be used to simulate and understand the pedestrian injury mechanisms during crashes in order to mitigate them. Thus, the objective of the study was to develop a computationally efficient (simplified) six-year-old (6YO-PS) pedestrian FE model and validate it based on the latest published pediatric data. The 6YO-PS FE model was developed by morphing the existing GHBMC adult pedestrian model. Retrospective scan data were used to locally adjust the geometry as needed for accuracy. Component test simulations focused only the lower extremities and pelvis, which are the first body regions impacted during pedestrian accidents. Three-point bending test simulations were performed on the femur and tibia with adult material properties and then updated using child material properties. Pelvis impact and knee bending tests were also simulated. Finally, a series of pediatric Car-to-Pedestrian Collision (CPC) were simulated with pre-impact velocities ranging from 20km/h up to 60km/h. The bone models assigned pediatric material properties showed lower stiffness and a good match in terms of fracture force to the test data (less than 6% error). The pelvis impact force predicted by the child model showed a similar trend with test data. The whole pedestrian model was stable during CPC simulations and predicted common pedestrian injuries. Overall, the 6YO-PS FE model developed in this study showed good biofidelity at component level (lower extremity and pelvis) and stability in CPC simulations. While more validations would improve it, the current model could be used to investigate the lower limb injury mechanisms and in the prediction of the impact parameters as specified in regulatory testing protocols.