Finite element modeling of the human kidney for probabilistic occupant models: Statistical shape analysis and mesh morphing

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.

The impact of shape uncertainty on aortic-valve pressure-drop computations

Patient-specific image-based computational fluid dynamics (CFD) is widely adopted in the cardiovascular research community to study hemodynamics, and will become increasingly important for personalized medicine. However, segmentation of the flow domain is not exact and geometric uncertainty can be expected which propagates through the computational model, leading to uncertainty in model output. Seventy-four aortic-valves were segmented from computed tomography images at peak systole. Statistical shape modeling was used to obtain an approximate parameterization of the original segmentations. This parameterization was used to train a meta-model that related the first five shape mode coefficients and flowrate to the CFD-computed transvalvular pressure-drop. Consequently, shape uncertainty in the order of 0.5 and 1.0 mm was emulated by introducing uncertainty in the shape mode coefficients. A global variance-based sensitivity analysis was performed to quantify output uncertainty and to determine relative importance of the shape modes. The first shape mode captured the opening/closing behavior of the valve and uncertainty in this mode coefficient accounted for more than 90% of the output variance. However, sensitivity to shape uncertainty is patient-specific, and the relative importance of the fourth shape mode coefficient tended to increase with increases in valvular area. These results show that geometric uncertainty in the order of image voxel size may lead to substantial uncertainty in CFD-computed transvalvular pressure-drops. Moreover, this illustrates that it is essential to assess the impact of geometric uncertainty on model output, and that this should be thoroughly quantified for applications that wish to use image-based CFD models.

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.

Combining statistical shape modeling, CFD, and meta-modeling to approximate the patient-specific pressure-drop across the aortic valve in real-time

BACKGROUND

Advances in medical imaging, segmentation techniques, and high performance computing have stimulated the use of complex, patient-specific, three-dimensional Computational Fluid Dynamics (CFD) simulations. Patient-specific, CFD-compatible geometries of the aortic valve are readily obtained. CFD can then be used to obtain the patient-specific pressure-flow relationship of the aortic valve. However, such CFD simulations are computationally expensive, and real-time alternatives are desired.

AIM

The aim of this work is to evaluate the performance of a meta-model with respect to high-fidelity, three-dimensional CFD simulations of the aortic valve.

METHODS

Principal component analysis was used to build a statistical shape model (SSM) from a population of 74 iso-topological meshes of the aortic valve. Synthetic meshes were created with the SSM, and steady-state CFD simulations at flow-rates between 50 and 650 mL/s were performed to build a meta-model. The meta-model related the statistical shape variance, and flow-rate to the pressure-drop.

RESULTS

Even though the first three shape modes account for only 46% of shape variance, the features relevant for the pressure-drop seem to be captured. The three-mode shape-model approximates the pressure-drop with an average error of 8.8% to 10.6% for aortic valves with a geometric orifice area below 150 mm² . The proposed methodology was least accurate for aortic valve areas above 150 mm² . Further reduction to a meta-model introduces an additional 3% error.

CONCLUSIONS

Statistical shape modeling can be used to capture shape variation of the aortic valve. Meta-models trained by SSM-based CFD simulations can provide an estimate of the pressure-flow relationship in real-time.

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.