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To account for proximal femoral deformities in children with cerebral palsy (CP), subject-specific musculoskeletal models are needed. Non-rigid deformation (NRD) deforms generic onto personalized bone geometry and thereby transforms the muscle points. The goal of this study was to determine to what extent the models and simulation outcomes in CP patients differ when including subject-specific detail using NRD or Magnetic Resonance Imaging (MRI)-based models. The NRD models slightly overestimated hip contact forces compared to MRI models and differences in muscle point positions and moment arm lengths (MALs) remained, although differences were smaller than for the generic model.

Computed tomography is used more routinely to design patient-specific instrumentation for knee replacement surgery. Its moderate imaging cost and simplified segmentation reduce design costs compared with magnetic resonance (MR) imaging, but it cannot provide the necessary cartilage information. Our method based on statistical shape modelling proved to be successful in predicting tibiofemoral cartilage in leave-one-out experiments. The obtained accuracy of 0.54 mm for femur and 0.49 mm for tibia outperforms the average cartilage thickness distribution and reported inter-observer MR segmentation variability. These results suggest that shape modelling is able to predict tibiofemoral cartilage with sufficient accuracy to design patient-specific instrumentation.

Finite element (FE) simulations are increasingly valuable in assessing and improving the performance of biomedical devices and procedures. Due to high computational demands such simulations may become difficult or even infeasible, especially when considering nearly incompressible and anisotropic material models prevalent in analyses of soft tissues. Implementations of GPGPU-based explicit FEs predominantly cover isotropic materials, e.g. the neo-Hookean model. To elucidate the computational expense of anisotropic materials, we implement the Gasser-Ogden-Holzapfel dispersed, fiber-reinforced model and compare solution times against the neo-Hookean model. Implementations of GPGPU-based explicit FEs conventionally rely on single-point (under) integration. To elucidate the expense of full and selective-reduced integration (more reliable) we implement both and compare corresponding solution times against those generated using underintegration. To better understand the advancement of hardware, we compare results generated using representative Nvidia GPGPUs from three recent generations: Fermi (C2075), Kepler (K20c), and Maxwell (GTX980). We explore scaling by solving the same boundary value problem (an extension-inflation test on a segment of human aorta) with progressively larger FE meshes. Our results demonstrate substantial improvements in simulation speeds relative to two benchmark FE codes (up to 300[Formula: see text] while maintaining accuracy), and thus open many avenues to novel applications in biomechanics and medicine.

Plantar pressure simulation driven by integrated 3D motion capture data, using both a finite element and a discrete element model, is compared for ten healthy and ten diabetic neuropathic subjects. The simulated peak pressure deviated on average between 16.7 and 34.2% from the measured peak pressure. The error in the position of the peak pressure was on average smaller than 4.2 cm. No method was more accurate than the other although statistical differences were found between them. Both techniques are thus complementary and useful tools to better understand the alteration of diabetic foot biomechanics during gait.

Custom implants are used to treat patients with large acetabular bone defects. To quantify the bone defect and to initialize the implant design, a virtual anatomical reconstruction of the bone needs to be performed. Our SSM-based reconstruction approach was used to overcome the limitations of the mirrored contralateral method and improves upon other SSM reconstruction techniques. The reconstruction errors for the acetabular direction, the hip joint center and the acetabular radius were, respectively: [Formula: see text], 2.6 mm and 0.7 mm. We believe that our method can be an essential tool in the planning and the design of custom implants.

Acute subdural hematoma (ASDH) is a type of intracranial haemorrhage following head impact, with high mortality rates. Bridging vein (BV) rupture is a major cause of ASDH, which is why a biofidelic representation of BVs in finite element (FE) head models is essential for the successful prediction of ASDH. We investigated the mechanical behavior of BVs in the KTH FE head model. First, a sensitivity study quantified the effect of loading conditions and mechanical properties on BV strain. It was found that the peak rotational velocity and acceleration and pulse duration have a pronounced effect on the BV strains. Both Young's modulus and diameter are also negatively correlated with the BV strains. A normalized multiple linear regression model using Young's modulus, outer diameter and peak rotational velocity to predict the BV strain yields an adjusted [Formula: see text]-value of 0.81. Secondly, cadaver head impact experiments were simulated with varying sets of mechanical properties, upon which the amount of successful BV rupture predictions was evaluated. The success rate fluctuated between 67 and 75%. To further increase the predictive capability of FE head models w.r.t. BV rupture, future work should be directed towards improvement of the BV representation, both geometrically and mechanically.

Multibody simulations of human motion require representative models of the anatomical structures. A model that captures the complexity of the foot is still lacking. In the present work, two detailed 3D multibody foot-ankle models generated based on CT scans using a semi-automatic tool are described. The proposed models consists of five rigid segments (talus, calcaneus, midfoot, forefoot and toes), connected by five joints (ankle, subtalar, midtarsal, tarsometatarsal and metatarsophalangeal), one with 15DOF and the other with 8DOF. The calculated kinematics of both models were evaluated using gait trials and compared against literature, both presenting realistic results. An inverse dynamic analysis was performed for the 8DOF model, again presenting feasible dynamic results.

Hip joint moments are an important parameter in the biomechanical evaluation of orthopaedic surgery. Joint moments are generally calculated using scaled generic musculoskeletal models. However, due to anatomical variability or pathology, such models may differ from the patient's anatomy, calling into question the accuracy of the resulting joint moments. This study aimed to quantify the potential joint moment errors caused by geometrical inaccuracies in scaled models, during gait, for eight test subjects. For comparison, a semi-automatic computed tomography (CT)-based workflow was introduced to create models with subject-specific joint locations and inertial parameters. 3D surface models of the femora and hemipelves were created by segmentation and the hip joint centres and knee axes were located in these models. The scaled models systematically located the hip joint centre (HJC) up to 33.6 mm too inferiorly. As a consequence, significant and substantial peak hip extension and abduction moment differences were recorded, with, respectively, up to 23.1% and 15.8% higher values in the image-based models. These findings reaffirm the importance of accurate HJC estimation, which may be achieved using CT- or radiography-based subject-specific modelling. However, obesity-related gait analysis marker placement errors may have influenced these results and more research is needed to overcome these artefacts.

Although the use of patient-specific surgical guides has gained popularity over the past decade, little research has been done to examine in an objective and qualitative way the fit of such instruments. In this study, we have developed a model to predict the stability of a guide designed to fit on a supporting bone surface, thereby providing feedback on the translational and rotational stability of the device. The method was validated by comparing different guide designs with respect to their stability on the contact surface and comparing these results to those measured with a set of experiments. This validation experiment indicates that our stability model can be used to predict the stability of the fit of a surgical guide during the preoperative design process.

To understand the mechanopathogenesis of brain lesions, finite element (FE) head models are used. There is a broad range of material properties, contact interfaces and integration schemes used for the different parts in current FE head models. The effect of material behaviour and contact definitions on a head impact analysis is reported in the literature, whereas the effect of FE integration schemes is a rather unexplored domain. This paper starts with the development of a simplified head model to which small adaptations are made in the integration scheme to obtain multiple analyses that are compared using an accident reconstruction. The performed study highlighted potential hazards of different integration schemes and the significant effect they have on the simulated mechanical responses of the head. Based on a comparison between FE softwares using an impact test and patch test, it was seen that also the software could have an effect on the FE analysis results.

In morphological analysis of the femur, the hip joint centre (HJC) is generally determined using a 3D model of the femoral head based on medical images. However, the portion of the image selected to represent the femoral head may influence the HJC. We determined if this influence invalidates the results of three HJC calculation methods, one of which we introduce here. To isolate femoral heads in cadaver CT images, thresholds were applied to the distance between femur and acetabulum models. The sensitivity of the HJC to these thresholds and the differences between methods were quantified. For thresholds between 6 and 9 mm and healthy hips, differences between methods were below 1 mm and all methods were insensitive to threshold changes. For higher thresholds, the fovea capitis femoris disturbed the HJC. In two deformed hips, the new method performed superiorly. We conclude that for normal hips all methods produce valid results.

This study combines concepts of bed design and sleep registrations to investigate how quality of spine support affects the manifestation of sleep in healthy subjects. Altogether, 17 normal sleepers (nine males, eight females; age 24.3±7.1 years) participated in an anthropometric screening, prior to the actual sleep experiments, during which personalised sleep system settings were determined according to individual body measures. Sleep systems (i.e. mattress and supporting structure) with an adjustable stiffness distribution were used. Subjects spent three nights of 8 h in bed in the sleep laboratory in a counterbalanced order (adaptation, personalised support and sagging support). During these nights, polysomnography was performed. Subjective sleep data were gathered by means of questionnaires. Results show that individual posture preferences are a determinant factor in the extent that subjects experience a negative effect while sleeping on a sagging sleep system. STATEMENT OF RELEVANCE: This study investigated how spine support affects sleep in healthy subjects, finding that the relationship between bedding and sleep quality is affected by individual anthropometry and sleep posture. In particular, results indicate that a sagging sleep system negatively affects sleep quality for people sleeping in a prone or lateral posture.

The objective of this study was to see whether a mathematical model of fracture healing was able to mimic bone formation around an unloaded screw-shaped titanium implant as it is well-believed that both processes exhibit many biological similarities. This model describes the spatio-temporal evolution of cellular activities, ranging from mesenchymal stem cell migration, proliferation, differentiation to bone formation, which are initiated and regulated by the growth factors present at the peri-implant site. For the simulations, a finite volume code was used and adequate initial and boundary conditions were applied. Two sets of analyses have been performed, in which either initial and boundary condition or model parameter values were changed with respect to the fracture healing model parameter values. For a number of combinations, the spatio-temporal evolution of bone density was well-predicted. However reducing cell proliferation rate and increasing osteoblast differentiation and osteogenic growth factor synthesis rates, the simulation results were in agreement with the experimental data.

Soft tissue modelling has gained a great deal of importance, for a large part due to its application in surgical training simulators for minimally invasive surgery (MIS). This article provides a structured overview of different continuum-mechanical models that have been developed over the years. It aims at facilitating model choice for specific soft tissue modelling applications. According to the complexity of the model, different features of soft biological tissue will be incorporated, i.e. nonlinearity, viscoelasticity, anisotropy, heterogeneity and finally, tissue damage during deformation. A brief summary of experimental methods for material characterisation and an introduction to methods for geometric modelling are also provided. The overview is non-exhaustive, focusing on the most important general models and models with specific biological applications. A trade-off in complexity must be made for enabling real-time simulation, but still maintaining realistic representation of the organ deformation. Depending on the organ and tissue types, different models with emphasis on certain features will prove to be more appropriate, meaning the optimal model choice is organ and tissue-dependent.

Good segmentation of the outer bone cortex from medical images is a prerequisite for applications in the field of finite element analysis, surgical planning environments and personalised, case dependent, bone reconstruction. However, current segmentation procedures are often unsatisfactory. This study presents an automated filter procedure to generate a set of adapted contours from which a surface mesh can be deduced directly. The degree of interaction is user determined. The bone contours are extracted from the patients CT data by quick grey value segmentation. An extended filter procedure then only retains contour information representing the outer cortex as more specific internal loops and shape irregularities are removed, tailoring the image for the above-mentioned applications. The developed medical image based design methodology to convert contour sets of multiple bone types, from tibia tumour to neurocranium, is reported and discussed.

Nowadays, there is a growing consensus on the impact of mechanical loading on bone biology. A bone chamber provides a mechanically isolated in vivo environment in which the influence of different parameters on the tissue response around loaded implants can be investigated. This also provides data to assess the feasibility of different mechanobiological models that mathematically describe the mechanoregulation of tissue differentiation. Before comparing numerical results to animal experimental results, it is necessary to investigate the influence of the different model parameters on the outcome of the simulations. A 2D finite element model of the tissue inside the bone chamber was created. The differentiation models developed by Prendergast, et al. ["Biophysical stimuli on cells during tissue differentiation at implant interfaces", Journal of Biomechanics, 30(6), (1997), 539-548], Huiskes et al. ["A biomechanical regulatory model for periprosthetic fibrous-tissue differentiation", Journal of Material Science: Materials in Medicine, 8 (1997) 785-788] and by Claes and Heigele ["Magnitudes of local stress and strain along bony surfaces predict the course and type of fracture healing", Journal of Biomechanics, 32(3), (1999) 255-266] were implemented and integrated in the finite element code. The fluid component in the first model has an important effect on the predicted differentiation patterns. It has a direct effect on the predicted degree of maturation of bone and a substantial indirect effect on the simulated deformations and hence the predicted phenotypes of the tissue in the chamber. Finally, the presence of fluid also causes time-dependent behavior. Both models lead to qualitative and quantitative differences in predicted differentiation patterns. Because of the different nature of the tissue phenotypes used to describe the differentiation processes, it is however hard to compare both models in terms of their validity.