Inverse remodelling algorithm identifies habitual manual activities of primates based on metacarpal bone architecture

Previously, a micro-finite element (micro-FE)-based inverse remodelling method was presented in the literature that reconstructs the loading history of a bone based on its architecture alone. Despite promising preliminary results, it remains unclear whether this method is sensitive enough to detect differences of bone loading related to pathologies or habitual activities. The goal of this study was to test the sensitivity of the inverse remodelling method by predicting joint loading histories of metacarpal bones of species with similar anatomy but clearly distinct habitual hand use. Three groups of habitual hand use were defined using the most representative primate species: manipulation (human), suspensory locomotion (orangutan), and knuckle-walking locomotion (bonobo, chimpanzee, gorilla). Nine to ten micro-computed tomography scans of each species ($$n=48$$n=48in total) were used to create micro-FE models of the metacarpal head region. The most probable joint loading history was predicted by optimally scaling six load cases representing joint postures ranging from $$-\,75^{\circ }$$-75∘(extension) to $$+\,75^{\circ }$$+75∘(flexion). Predicted mean joint load directions were significantly different between knuckle-walking and non-knuckle-walking groups ($$p<0.05$$p<0.05) and in line with expected primary hand postures. Mean joint load magnitudes tended to be larger in species using their hands for locomotion compared to species using them for manipulation. In conclusion, this study shows that the micro-FE-based inverse remodelling method is sensitive enough to detect differences of joint loading related to habitual manual activities of primates and might, therefore, be useful for palaeoanthropologists to reconstruct the behaviour of extinct species and for biomedical applications such as detecting pathological joint loading

Computational models for the simulation of the elastic and fracture properties of highly porous 3D-printed hydroxyapatite scaffolds

Bone scaffolding is a promising approach for the treatment of critical-size bone defects. Hydroxyapatite can be used to produce highly porous scaffolds as it mimics the mineralized part of bone tissue, but its intrinsic brittleness limits its usage. Among 3D printing techniques, vat photopolymerization allows for the best printing resolution for ceramic materials. In this study, we implemented a Computed micro-Tomography based Finite Element Model of a hydroxyapatite porous scaffold fabricated by vat photopolymerization. We used the model in order to predict the elastic and fracture properties of the scaffold. From the stress–strain diagram of a simulated compression test, we computed the stiffness and the strength of the scaffolds. We found that three morphometric features substantially affect the crack pattern. In particular, the crack propagation is not only dependent on the trabecular thickness but also depends on the slenderness and orientation of the trabeculae with respect to the load. The results found in this study can be used for the design of ceramic scaffolds with heterogeneous pore distribution in order to tailor and predict the compressive strength

Micro-CT imaging and finite element models reveal how sintering temperature affects the microstructure and strength of bioactive glass-derived scaffolds

: This study focuses on the finite element simulation and micromechanical characterization of bioactive glass-ceramic scaffolds using Computed micro Tomography ([Formula: see text]CT) imaging. The main purpose of this work is to quantify the effect of sintering temperature on the morphometry and mechanical performance of the scaffolds. In particular, the scaffolds were produced using a novel bioactive glass material (47.5B) through foam replication, applying six different sintering temperatures. Through [Formula: see text]CT imaging, detailed three-dimensional images of the scaffold's internal structure are obtained, enabling the extraction of important geometric features and how these features change with sintering temperature. A finite element model is then developed based on the [Formula: see text]CT images to simulate the fracture process under uniaxial compression loading. The model incorporates scaffold heterogeneity and material properties-also depending on sintering temperature-to capture the mechanical response, including crack initiation, propagation, and failure. Scaffolds sintered at temperatures equal to or higher than 700&nbsp;[Formula: see text]C exhibit two-scale porosity, with micro and macro pores. Finite element analyses revealed that the dual porosity significantly affects fracture mechanisms, as micro-pores attract cracks and weaken strength. Interestingly, scaffolds sintered at high temperatures, the overall strength of which is higher due to greater intrinsic strength, showed lower normalized strength compared to low-temperature scaffolds. By using a combined strategy of finite element simulation and [Formula: see text]CT-based characterization, bioactive glass-ceramic scaffolds can be optimized for bone tissue engineering applications by learning more about their micromechanical characteristics and fracture response

Precision of bone mechanoregulation assessment in humans using longitudinal high-resolution peripheral quantitative computed tomography in vivo.

Local mechanical stimuli in the bone microenvironment are essential for the homeostasis and adaptation of the skeleton, with evidence suggesting that disruption of the mechanically-driven bone remodelling process may lead to bone loss. Longitudinal clinical studies have shown the combined use of high-resolution peripheral quantitative computed tomography (HR-pQCT) and micro-finite element analysis can be used to measure load-driven bone remodelling in vivo; however, quantitative markers of bone mechanoregulation and the precision of these analyses methods have not been validated in human subjects. Therefore, this study utilised participants from two cohorts. A same-day cohort (n = 33) was used to develop a filtering strategy to minimise false detections of bone remodelling sites caused by noise and motion artefacts present in HR-pQCT scans. A longitudinal cohort (n = 19) was used to develop bone imaging markers of trabecular bone mechanoregulation and characterise the precision for detecting longitudinal changes in subjects. Specifically, we described local load-driven formation and resorption sites independently using patient-specific odds ratios (OR) and 99 % confidence intervals. Conditional probability curves were computed to link the mechanical environment to the remodelling events detected on the bone surface. To quantify overall mechanoregulation, we calculated a correct classification rate measuring the fraction of remodelling events correctly identified by the mechanical signal. Precision was calculated as root-mean-squared averages of the coefficient of variation (RMS-SD) of repeated measurements using scan-rescan pairs at baseline combined with a one-year follow-up scan. We found no significant mean difference (p < 0.01) between scan-rescan conditional probabilities. RMS-SD was 10.5 % for resorption odds, 6.3 % for formation odds, and 1.3 % for correct classification rates. Bone was most likely to be formed in high-strain and resorbed in low-strain regions for all participants, indicating a consistent, regulated response to mechanical stimuli. For each percent increase in strain, the likelihood of bone resorption decreased by 2.0 ± 0.2 %, and the likelihood of bone formation increased by 1.9 ± 0.2 %, totalling 38.3 ± 1.1 % of strain-driven remodelling events across the entire trabecular compartment. This work provides novel robust bone mechanoregulation markers and their precision for designing future clinical studies

Inverse remodelling algorithm identifies habitual manual activities of primates based on metacarpal bone architecture

Previously, a micro-finite element (micro-FE)-based inverse remodelling method was presented in the literature that reconstructs the loading history of a bone based on its architecture alone. Despite promising preliminary results, it remains unclear whether this method is sensitive enough to detect differences of bone loading related to pathologies or habitual activities. The goal of this study was to test the sensitivity of the inverse remodelling method by predicting joint loading histories of metacarpal bones of species with similar anatomy but clearly distinct habitual hand use. Three groups of habitual hand use were defined using the most representative primate species: manipulation (human), suspensory locomotion (orangutan), and knuckle-walking locomotion (bonobo, chimpanzee, gorilla). Nine to ten micro-computed tomography scans of each species ( n=48 in total) were used to create micro-FE models of the metacarpal head region. The most probable joint loading history was predicted by optimally scaling six load cases representing joint postures ranging from −75∘ (extension) to +75∘ (flexion). Predicted mean joint load directions were significantly different between knuckle-walking and non-knuckle-walking groups ( p<0.05 ) and in line with expected primary hand postures. Mean joint load magnitudes tended to be larger in species using their hands for locomotion compared to species using them for manipulation. In conclusion, this study shows that the micro-FE-based inverse remodelling method is sensitive enough to detect differences of joint loading related to habitual manual activities of primates and might, therefore, be useful for palaeoanthropologists to reconstruct the behaviour of extinct species and for biomedical applications such as detecting pathological joint loading

Development of a density-based topology optimization of homogenized lattice structures for individualized hip endoprostheses and validation using micro-FE

Prosthetic implants, particularly hip endoprostheses, often lead to stress shielding because of a mismatch in compliance between the bone and the implant material, adversely affecting the implant’s longevity and effectiveness. Therefore, this work aimed to demonstrate a computationally efficient method for density-based topology optimization of homogenized lattice structures in a patient-specific hip endoprosthesis. Thus, the root mean square error (RMSE) of the stress deviations between the physiological femur model and the optimized total hip arthroplasty (THA) model compared to an unoptimized-THA model could be reduced by 81 % and 66 % in Gruen zone (GZ) 6 and 7. However, the method relies on homogenized finite element (FE) models that only use a simplified representation of the microstructural geometry of the bone and implant. The topology-optimized hip endoprosthesis with graded lattice structures was synthesized using algorithmic design and analyzed in a virtual implanted state using micro-finite element (micro-FE) analysis to validate the optimization method. Homogenized FE and micro-FE models were compared based on averaged von Mises stresses in multiple regions of interest. A strong correlation (CCC > 0.97) was observed, indicating that optimizing homogenized lattice structures yields reliable outcomes. The graded implant was additively manufactured to ensure the topology-optimized result’s feasibility

Machine Learning approaches for the design of biomechanically compatible bone tissue engineering scaffolds

Triply-Periodic Minimal Surfaces (TPMS) analytical formulation does not provide a direct correlation between the input parameters (analytical) and the mechanical and morphological properties of the structure. In this work, we created a dataset with more than one thousand TPMS scaffolds for the training of Machine Learning (ML) models able to find such correlation. Finite Element Modeling and image analysis have been used to characterize the scaffolds. In particular, we trained three different ML models, exploring both a linear and non-linear approach, to select the features able to predict the input parameters. Furthermore, the features used for the prediction can be selected in three different modes: i) fully automatic, through a greedy algorithm, ii) arbitrarily, by the user and iii) in a combination of the two above methods: i.e. partially automatic and partially through a user-selection. The latter, coupled with the non-linear ML model, exhibits a median error less than 3% and a determination coefficient higher than 0.89 for each of the selected features, and all of them are accessible during the design phase. This approach has been applied to the design of a hydroxyapatite TPMS scaffolds with prescribed properties obtained from a real trabecular-like hydroxyapatite scaffold. The obtained results demonstrate that the ML model can effectively design a TPMS scaffold with prescribed features on the basis of biomechanical, mechanobiology and technological constraints

Finite Element Algorithms and Data Structures on Graphical Processing Units

The finite element method (FEM) is one of the most commonly used techniques for the solution of partial differential equations on unstructured meshes. This paper discusses both the assembly and the solution phases of the FEM with special attention to the balance of computation and data movement. We present a GPU assembly algorithm that scales to arbitrary degree polynomials used as basis functions, at the expense of redundant computations. We show how the storage of the stiffness matrix affects the performance of both the assembly and the solution. We investigate two approaches: global assembly into the CSR and ELLPACK matrix formats and matrix-free algorithms, and show the trade-off between the amount of indexing data and stiffness data. We discuss the performance of different approaches in light of the implicit caches on Fermi GPUs and show a speedup over a two-socket 12-core CPU of up to 10 times in the assembly and up to 6 times in the solution phase. We present our sparse matrix-vector multiplication algorithms that are part of a conjugate gradient iteration and show that a matrix-free approach may be up to two times faster than global assembly approaches and up to 4 times faster than NVIDIA’s cuSPARSE library, depending on the preconditioner used

Matrixfreie voxelbasierte Finite-Elemente-Methode für Materialien mit komplizierter Mikrostruktur

Modern image detection techniques such as micro computer tomography (μCT), magnetic resonance imaging (MRI) and scanning electron microscopy (SEM) provide us with high resolution images of the microstructure of materials in a non-invasive and convenient way. They form the basis for the geometrical models of high-resolution analysis, so called image-based analysis. However especially in 3D, discretizations of these models reach easily the size of 100 Mill. degrees of freedoms and require extensive hardware resources in terms of main memory and computing power to solve the numerical model. Consequently, the focus of this work is to combine and adapt numerical solution methods to reduce the memory demand first and then the computation time and therewith enable an execution of the image-based analysis on modern computer desktops. Hence, the numerical model is a straightforward grid discretization of the voxel-based (pixels with a third dimension) geometry which omits the boundary detection algorithms and allows reduced storage of the finite element data structure and a matrix-free solution algorithm. This in turn reduce the effort of almost all applied grid-based solution techniques and results in memory efficient and numerically stable algorithms for the microstructural models. Two variants of the matrix-free algorithm are presented. The efficient iterative solution method of conjugate gradients is used with matrix-free applicable preconditioners such as the Jacobi and the especially suited multigrid method. The jagged material boundaries of the voxel-based mesh are smoothed through embedded boundary elements which contain different material information at the integration point and are integrated sub-cell wise though without additional boundary detection. The efficiency of the matrix-free methods can be retained.Moderne bildgebende Verfahren wie Mikro-Computertomographie (μCT), Magnetresonanztomographie (MRT) und Rasterelektronenmikroskopie (SEM) liefern nicht-invasiv hochauflösende Bilder der Mikrostruktur von Materialien. Sie bilden die Grundlage der geometrischen Modelle der hochauflösenden bildbasierten Analysis. Allerdings erreichen vor allem in 3D die Diskretisierungen dieser Modelle leicht die Größe von 100 Mill. Freiheitsgraden und erfordern umfangreiche Hardware-Ressourcen in Bezug auf Hauptspeicher und Rechenleistung, um das numerische Modell zu lösen. Der Fokus dieser Arbeit liegt daher darin, numerische Lösungsmethoden zu kombinieren und anzupassen, um den Speicherplatzbedarf und die Rechenzeit zu reduzieren und damit eine Ausführung der bildbasierten Analyse auf modernen Computer-Desktops zu ermöglichen. Daher ist als numerisches Modell eine einfache Gitterdiskretisierung der voxelbasierten (Pixel mit der Tiefe als dritten Dimension) Geometrie gewählt, die die Oberflächenerstellung weglässt und eine reduzierte Speicherung der finiten Elementen und einen matrixfreien Lösungsalgorithmus ermöglicht. Dies wiederum verringert den Aufwand von fast allen angewandten gitterbasierten Lösungsverfahren und führt zu Speichereffizienz und numerisch stabilen Algorithmen für die Mikrostrukturmodelle. Es werden zwei Varianten der Anpassung der matrixfreien Lösung präsentiert, die Element-für-Element Methode und eine Knoten-Kanten-Variante. Die Methode der konjugierten Gradienten in Kombination mit dem Mehrgitterverfahren als sehr effizienten Vorkonditionierer wird für den matrixfreien Lösungsalgorithmus adaptiert. Der stufige Verlauf der Materialgrenzen durch die voxelbasierte Diskretisierung wird durch Elemente geglättet, die am Integrationspunkt unterschiedliche Materialinformationen enthalten und über Teilzellen integriert werden (embedded boundary elements). Die Effizienz der matrixfreien Verfahren bleibt erhalten