Purpose

The meniscus is a critical component of a healthy knee joint and plays a pivotal role in preserving the knee homeostasis and biomechanics. Consequently, meniscal damage affects the knee equilibrium, progressively contributing to cartilage disruption up to the development of osteoarthritis (OA). Considering the meniscal poor-self healing potential, its repair still represents a challenge to orthopedic surgeons. To counteract the increasing demand for meniscus implants and allografts, innovative, and effective repair strategies are required and among vanguard approaches, 3D printing technologies seem to be intriguing and promising.

Material and Methods

To pave the way towards the development of 3D printed patient-specific menisci, we conducted an experimental study to, (i) create a virtual 3D reconstruction of the meniscus microstructure based on μCT scans; and (ii) build a STL model of the meniscus for 3D printing based on the scanned models.

The menisci used in this study were removed from TKA human knees. All tissue samples were harvested immediately after surgery, and a staining and freeze-drying protocol with Lugol’s solution was applied. μCT scans of the menisci were performed using a SCANCO μCT 50 (SCANCO Medical AG, Brütistellen, Switzerland) specimen scanner. A 3D voxel model was reconstructed from the data and converted to STL format using MIMICS (Materialise, Leuven, Belgium).

Results

A Lugol stained and freeze-dryed meniscus can be scanned via μCT. The μCT images clearly displayed microfibers in the meniscus with an isotropic resolution of 20.7 μm. The surface layer, lamellar layer, circumferential fibers, and radial fibers could be identified. Furthermore, a 3D STL-model of the meniscus was digitally built based on the μCT images, and the microscale fibers could be discriminated in the virtually reconstructed meniscus.

Conclusion

The microstructure of the meniscus can be visualized via μCT and can be reconstructed into STL format. The 3D STL model will be the data basis for generating a printable model of patient-specific menisci via 3D bioprinting.

Introduction

The knee joint is one of the most complex human joints and injuries like ligament rupture, cartilage or meniscus defects are common. Instabilities caused by these defects or suboptimal treatment lead to secondary complications. Patient-individual models that take into account the patient-specific knee joint motion are supposed to largely improve surgery planning and outcome. State-of-the-art finite element (FE) knee joint models are highly complex, computationally demanding and time-intensive.

Methods

We propose a simplified knee joint model based on position-based dynamics (PBD) for real-time prediction of patient-individual knee joint motion [1]. The model’s outcome is compared to the predicted patient-individual knee joint motion of a state-of-the-art FE model [2]. Both models are automatically generated from magnetic resonance (MR) images of a patient’s knee. Comparison is done using MR images of eleven healthy volunteers. The study was approved by the ethics committee of the Albert-Ludwigs University Freiburg (Nr. 91/19 – 210696, 19 August 2021) and all volunteers gave written informed consent prior to participation. We compared two motion sequences going from 0° knee flexion to 20° flexion and adding 1) internal or 2) external rotation torque. Both methods have previously been validated against reference MR measurements.

Results and Discussion

Important quality measures are the accuracy of the models as well as their computational performance, such that the clinical workflow is not interrupted and fast, physics-informed surgical planning can be performed.
A direct comparison of the FE and PBD model results shows similar motion predicted by both methods. Calculating one of the described motion sequences for one patient takes about 2 h using the FE approach. Applying the PBD model, one motion sequence can be calculated in 48 s on average.
Cartilage contact areas in the knee joint during motion are estimated and compared using both approaches. However, questions concerning the stresses and strains in ligaments, cartilages or the menisci can only be answered using the more complex FE model.

Conclusions

From the above observations, we see that the high modelling accuracy of FE knee joint models is dispensable in our use case of predicting knee joint motion. The PBD model predicts similar motion while being approximately 150-times faster. Thus, the PBD model is even capable of real-time modifications, e.g., repositioning of a planned anterior cruciate ligament transplant. Summarizing, the PBD model yields a good trade-off between accuracy and time constraints.

Acknowledgements

This work has been supported by the German Ministry of Education and Research (BMBF) in the call “Individualisierte Medizintechnik” under contract number 13GW0277. We would like to thank Ingmar Ludwig, Elin Wefer, Elham Taghizadeh, Sebastian Bendak, Jonas Buchholtz, and Hagen Schmal for their support of this work.

References

[1] I. Ludwig, E. Taghizadeh, K. Izadpanah, T. Lange, J. Georgii, “Patient-specific Modelling and Simulation of Knee Joint Motion using Position-Based Dynamics”, in Proc. of CARS, 2020.

[2] E. Theilen et al., "Validation of a finite element simulation for predicting individual knee joint kinematics," in IEEE Open Journal of Engineering in Medicine and Biology, doi: 10.1109/OJEMB.2023.3258362.

Children with limb deformities often seek paediatric orthopaedic consultation because of uneven growth of the physis or growth plate of the bone structure. This process can be influenced by mechanical stimulation. Surgical implantation of devices such as staples can restrict growth in specific areas of the affected cartilage to correct the asymmetry. Although these techniques temporarily block the physis, they are less effective in certain deformities and can cause complications. The development of computational growth models will improve these implant-guided growth techniques. Therefore, our aim is to develop a computational tool to predict epiphyseal growth and support clinical strategies to correct abnormal growth of immature bone.

The model developed is based on the mathematical model of Garzón-Alvarado et al.¹, which is a biologically derived model that considers both the mechanical and cellular activity of the growth plate. The growth rate is described by a tensor, which is the sum of the contribution of both proliferating and hypertrophying chondrocytes to the increase in height in the growth direction. Each term has a biological growth component and another component describing the effect of increased hydrostatic and deviatoric stresses associated with growth in the respective zones of the physis. This model has a significant advantage in that it describes the pattern of chondrocytes in each zone using a tensor. We have taken the parameter values from the literature and the stress-related values are approximations. In addition, we have introduced a parameter in the hypertrophic zone that is proportional to the relative growth of the proliferative zone. This parameter allows to block the physis under high stresses, as occurs when a staple is inserted.

We compared the growth generated by our model with the results published by Narváez et al.² for the case of proximal tibia growth in rats over a 23-day period. Similar growth values were obtained in the simulation of free growth, i.e.where there is no increase in stress due to external loads. Our model also showed a marked tendency for the growth rate to decrease with the application of compressive loads as well. We also simulated Narvaez's experiment in which the growth plate was subjected to traction during the 23-day period. Our results differed from the original experiment in that they produced less growth and varied the geometry of the plate. This could be explained by the hypothesis that the growth is mainly due to the ability of the cells to proliferate and then hypertrophy. Therefore, if distraction does not have a positive effect on proliferative activity as some experiments have shown and may even decrease it^3,4, we are inclined to think that our results are physiologically relevant.

References

1. Garzón-Alvarado et al., J. Mech. Med. Biol 11(5): 1213-1240 (2011)
2. Narváez-Tovar et al., Theor Biol Med Model 9, 41 (2012)
3. Alberty et al., Acta Orthop Scand 64(4):449-55 (1993)
4. Apte et al., J Bone Joint Surg Br 76(5):837-43 (1994)

Acknowledgements

Spanish Government and Health Institute Carlos III (PI20/00293); Children Hospital Sant Joan de Déu; Department of Communication and Information Technologies, Universitat Pompeu Fabra.

The human shoulder joint combines mobility and stability in a unique musculoskeletal system. The anatomical structure of the glenohumeral joint allows for an extensive range of motion, while passive and active soft tissues ensure the joint’s integrity through static and dynamic mechanisms. In this context, muscles, especially the rotator cuff and the deltoid, play an essential role. On the one hand, muscles actively stabilize the otherwise extremely unstable glenohumeral joint through the mechanisms of concavity compression and scapulohumeral balance. On the other hand, muscles act as torque generators and enable complex movement patterns through their sophisticated interplay. Maintaining this delicate balance between mobility and stability is essential for proper shoulder function, yet it is easily disrupted by injury or pathological conditions. Despite the high incidence of shoulder disorders in clinical practice, understanding of the underlying biomechanics remains limited, posing major challenges for medical assessment and treatment.
Computational musculoskeletal models offer great potential for biomechanical studies of the shoulder’s physiology, investigations of pathological conditions, objective predictions and evaluations of (patient-specific) treatments, and the development of rehabilitation equipment for physical therapy. While numerous reduced-dimensional multi-body models exist, research on comprehensive continuum-based finite element models remains limited. However, three-dimensional interactions between the joint components, such as contact and sliding mechanisms, are central to the shoulder’s physiology. In contrast to multi-body models, continuum-mechanical models can represent such volumetric effects, account for complex muscle fiber and tendon arrangements, and model sophisticated constitutive behavior. For biomechanical studies of the shoulder, they are thus particularly relevant.
Considering their role as active joint stabilizers and force generators, skeletal muscles deserve special attention regarding their material description. Passive skeletal muscle is - according to its histological composition - commonly modeled as a transverse isotropic composite of unidirectionally oriented fibers connected by extracellular tissue. Active contractile effects are incorporated through active-stress or (generalized) active-strain approaches. Since selecting an appropriate constitutive model is crucial for reliable predictions, the question arises of which material is best suitable for characterizing the shoulder’s skeletal muscles.
In this contribution, we contrast three hyperelastic formulations considering mathematical, computational, and physiological aspects: an active-stress, an active-strain, and a generalized active-strain approach. To establish a basis for comparison, we fit the material parameters to a common set of experimentally obtained stress-strain data. As one load case is generally insufficient to determine the material response uniquely, we consider multiple active and passive loading conditions. We discuss the concepts of modeling active material behavior from a mathematical and physiological perspective, address analytical and numerical problems arising from the mathematical formulations, and analyze the included biophysical principles of force generation in terms of physiological correctness and relevance considering the modeling of the human shoulder. Conclusively, we present a constitutive model combining the studied materials’ most promising and relevant properties. By the example of a fusiform muscle geometry, we investigate force generation, deformation, and kinematics during active isometric and free contractions. Eventually, we demonstrate the applicability of the material formulations in simulations of a comprehensive continuum-mechanical model of the human shoulder.

Surgical resection of chest wall tumours or large defects due to thorax trauma with rib fractures may lead to a loss of ribcage stability and require reconstruction to allow for physical thorax functioning. When titanium implants are used especially for larger, lateral defects, they tend to fail due to implant fracture, peri-implant rib fracture or screw loosening. Implant failures are mainly caused by the specific mechanical requirements for chest-wall reconstruction which must mimic the physiological properties of the ribcage and which are not yet met by available implants.

Rib implants must show some important characteristics: On the one hand, there are a variety of quasi-static, dynamic and fatigue loads that they must be able to withstand permanently. Quasi-static loads can be induced by body posture, e.g., lying on the side, whereas dynamic loads are mostly induced by impacts. The most important fatigue load is normal breathing with approximately 8.4 million cycles per year. On the other hand, these implants must have an appropriate stiffness and strength to enable all daily movements and at the same time protect the vital inner organs. To meet these requirements, it is essential to understand the biomechanics of the thorax. For this purpose, a full thorax FEM model was developed, comprising all biomechanical relevant structures, e.g., ribs, sternum, costal cartilage, vertebral column, costo-vertebral joints, vertebral disks, passive intercostal muscles, subcutis and skin.

The simulation model was assembled in a stepwise approach. First a chest CT scan of a fresh, unembalmed cadaver in the supine position was made to reconstruct the anatomical structures of bones and cartilage. Additional CTs in different positions as well as stiffness measurements on several anatomical structures and levels were used to define mechanical properties of the FE model and for calibration and verification. Various activities such as ventilation, breathing, resuscitation, lying on the side or coughing were simulated on this verified FE model and the deformations and stresses were evaluated. Several simulations were carried out with different defects on the rib levels 5 to 9 and with corresponding implants according to the current state of the art. the critical activities that lead to damage of the implants could be identified using the prescribed procedure. Based on these findings, an algorithm for determining the implant dimensions for different alternative metallic and non-metallic materials (PEEK, …) was developed to achieve the required stiffness and strength of 3D-printed rib implants. This paves the way to optimized, patient-specific and intraoperatively 3D-printed rib implants.

Background: Cephalomedullary nailing is frequently used to treat per- and subtrochanteric fractures of the proximal femur. After fracture healing, patients sometimes request nail removal due to persistent pain or irritation. However, removing the nail leaves a large void in the bone, which poses a considerable risk of re-fracture at the femoral neck. Pre-operative prediction of fracture risk would help to make an informed decision about nail removal and to estimate the required post-operative care. This study investigated whether patient-specific finite element (FE) models created from pre-operative CT scans can predict femoral bone strength after nail removal. Experimental data of femora after nail removal were used to evaluate the accuracy of the models.

Methods: Ten femoral bones of human body donors who were treated with a cephalomedullary nail during their lifetime due to a per- or subtrochanteric fracture were obtained from the Medical Bio-/Implantbank Vienna. CT scans (0.4x0.4x0.6 mm³ voxel size) were taken prior to nail removal using a dual energy protocol and an iterative metal artefact reduction algorithm. The bones were cut to 50 % length, embedded, and mounted to a material testing machine to simulate loading in stance. The load was increased monotonically until failure and the maximum force was recorded. The experiments were replicated using patient-specific nonlinear voxel-based FE models. The models were created by virtually removing the implant from the pre-operative CT image, aligning the bones in agreement with the experiment, coarsening the image to 3 mm voxel size and converting each voxel to a linear hexahedral element. Due to remaining metal artefacts from the distal locking screws, the FE models were cut to the proximal region above the distal locking screw. A density-dependent, isotropic, elastic-damage material was assigned to each element and the models were loaded until failure in analogy to the experiments. The maximum force predicted by the models was then compared to the experimentally measured maximum force using linear regression analysis.

Results: Experimental femoral bone strength after nail removal ranged from 611 to 2851 N and FE-predicted strength ranged from 390 to 1873 N. FE model predictions and experimental measurements were well correlated (R²=0.78, p<0.001), but the models underestimated the experimental measurements (experimental mean: 1837±598 N, FE mean: 1127±425 N).

Conclusions: The FE models were able to predict the strength of femoral bones after cephalomedullary nail removal pre-operatively with good correlation to experimental measurements. This shows that voxel-based FE models can predict bone strength despite the presence of a metal implant in the CT scan and the highly irregular structure of the previously fractured and healed bones. Thus, FE models may be a useful tool to support clinical decisions on nail removal in the future.

Introduction: Nonlinear micro-finite element (μFE) models represent a powerful tool to predict elastic and yield properties as well as damage onset of bone across length scales ¹. Apparent mechanical properties and damage predictions have been validated against experimental measurements at the macroscale ^2,3. Recently, validation of local properties and damage onset have been achieved using digital volume correlation (DVC) based on micro-computed tomography (μCT) images in trabecular bone ⁴. However, validation of damage predictions in cortical bone remains missing due to the limited μCT studies capturing the deformation of cortical bone at high spatial resolution. Here, we aim to (i) use experimentally measured 3D displacement fields to validate nonlinear μFE models of cortical bone; (ii) use those models to investigate damage emergence and propagation based on synchrotron-radiation (SR)-μCT imaging.

Materials and methods: We performed time-resolved in situ SR-μCT compression testing in bovine cortical bone specimens in beamline I13-2 at Diamond Light Source. Specimens were loaded while SR-μCT images (6.5 μm voxel size) simultaneously acquired during compression up to apparent failure. We used DVC to obtain full-field displacement fields in the specimens ⁵. We generated μFE models using the unloaded SR-μCT image coarsened to 26 μm voxel size, and applied DVC displacement fields as boundary conditions ⁴. An elastic-viscoplastic damage model ¹ featuring an isotropic Drucker-Prager yield surface ⁶ was used (UMAT, Abaqus v6 R2018), with an isotropic Young’s modulus of 22.8 GPa ⁷ and a Poisson’s ratio of 0.3. Correlations between DVC measurements and μFE predictions in the apparent elastic regime and prior to failure were investigated using concordance correlation coefficient (r_c). We validated damage predictions against cracks location from the SR-μCT images post-failure.

Results: Displacements predictions using nonlinear μFE models outperformed linear μFE models both at the apparent elastic (r_c,linear ≥ 0.71, r_c,nonlinear ≥0.85) and plastic (r_c,linear ≥ 0.74, r_c,nonlinear ≥0.87) regions. Nonlinear μFE models damage predictions seem to initiate next to vascular porosity and correlated to regions that displayed significant cracks post-failure in the SR-μCT images.

Discussion: Our results demonstrate the ability of nonlinear μFE models to accurately predict displacements and to capture damage location in cortical bone tissue. They can, thus, be explored to investigate the mechanisms of bone failure in relation to structural and material changes due to aging or disease, enabling the development of treatment strategies that prevent bone fracture.

References: 1 Schwiedrzik, J. J. et al. Biomech. Model. Mechanobiol. 12, 201–213 (2013)e. 2 Hosseini, H. S. et al. J. Mech. Behav. Biomed. Mater. 15, 93–102 (2012). 3 Dall’Ara, E. et al. Bone 52, 27–38 (2013). 4 Peña Fernández, M. et al. J. Mech. Behav. Biomed. Mater. 132, 105303 (2022). 5 Peña Fernández, M. et al. Acta Biomater. 131, 424–439 (2021). 6 Schwiedrzik, J. J. et al. Biomech. Model. Mechanobiol. 12, 1155–1168 (2013). 7 Schwiedrzik, J. et al. Acta Biomater. 60, 302–314 (2017)

Acknowledgements: Leverhulme Trust RPG-2020-215