Experimentally Validated Finite Element Analysis of Thoracic Spine Compression Fractures in a Porcine Model.

Vertebral compression fractures (VCFs) occur in 1 to 1.5 million patients in the US each year and are associated with pain, disability, altered pulmonary function, secondary vertebral fracture, and increased mortality risk. A better understanding of VCFs and their management requires preclinical models that are both biomechanically analogous and accessible. We conducted a study using twelve spinal vertebrae (T12-T14) from porcine specimens. We created mathematical simulations of vertebral compression fractures (VCFs) using CT scans for reconstructing native anatomy and validated the results by conducting physical axial compression experiments. The simulations accurately predicted the behavior of the physical compressions. The coefficient of determination for stiffness was 0.71, the strength correlation was 0.88, and the failure of the vertebral bodies included vertical splitting on the lateral sides or horizontal separation in the anterior wall. This finite element method has important implications for the preventative, prognostic, and therapeutic management of VCFs. This study also supports the use of porcine specimens in orthopedic biomechanical research.

Can a Musculoskeletal Model Adapted to Knee Implant Geometry Improve Prediction of 3D Contact Forces and Moments?

Tibiofemoral contact loads are crucial parameters in the onset and progression of osteoarthrosis. While contact loads are frequently estimated from musculoskeletal models, their customization is often limited to scaling musculoskeletal geometry or adapting muscle lines. Moreover, studies have usually focused on superior-inferior contact force without investigating three-dimensional contact loads. Using experimental data from six patients with instrumented total knee arthroplasty (TKA), this study customized a lower limb musculoskeletal model to consider the positioning and the geometry of the implant at knee level. Static optimization was performed to estimate tibiofemoral contact forces and contact moments as well as musculotendinous forces. Predictions from both a generic and a customized model were compared to the instrumented implant measurements. Both models accurately predict superior-inferior (SI) force and abduction-adduction (AA) moment. Notably, the customization improves prediction of medial-lateral (ML) force and flexion-extension (FE) moments. However, there is subject-dependent variability in the prediction of anterior-posterior (AP) force. The customized models presented here predict loads on all joint axes and in most cases improve prediction. Unexpectedly, this improvement was more limited for patients with more rotated implants, suggesting a need for further model adaptations such as muscle wrapping or redefinition of hip and ankle joint centers and axes.

Kinetics influence of multibody kinematics optimisation for soft tissue artefact compensation.

Soft tissue artefact (STA) remains a major source of error in human movement analysis. The multibody kinematics optimisation (MKO) approach is widely stated as a solution to reduce the effects of STA. This study aimed at assessing the influence of the MKO STA-compensation on the errors of estimation of the knee intersegment moments. Experimental data were issued from the CAMS-Knee dataset where six participants with instrumented total knee arthroplasty performed five activities of daily living: gait, downhill walking, stair descent, squat, and sit-to-stand. Kinematics was measured both on the basis of skin markers and a mobile mono-plane fluoroscope, used to obtain the STA-free bone movement. For four different lower limb models and one corresponding to a single-body kinematics optimization (SKO), knee intersegmental moments (estimated using model-derived kinematics and ground reaction force) were compared with an estimate based on the fluoroscope. Considering all participants and activities, mean root mean square differences were the largest along the adduction/abduction axis: of 3.22Nm with a SKO approach, 3.49Nm with the three-DoF knee model, and 7.66Nm, 8.52Nm, and 8.54Nm with the one-DoF knee models. Results showed that adding joint kinematics constraints can increase the estimation errors of the intersegmental moment. These errors came directly from the errors in the estimation of the position of the knee joint centre induced by the constraints. When using a MKO approach, we recommend to analyse carefully joint centre position estimates that do not remain close to the one obtained with a SKO approach.

Dynamic estimation of soft tissue stiffness for use in modeling socket, orthosis or exoskeleton interfaces with lower limb segments.

Modeling the interface between the lower limb segments and a socket, orthosis or exoskeleton is crucial to the design, control, and assessment of such devices. The present study aimed to estimate translational and rotational soft tissue stiffness at the thigh and shank during daily living activities performed by six subjects. Smooth orthogonal decomposition (SOD) was used on skin marker trajectories and fluoroscopy-based knee joint kinematics to compute stiffness coefficients during squatting, sitting and rising from a chair, level walking, and stair descending. On average, for all subjects and for all activities, in the anatomical directions observed, the translational and rotational stiffness coefficients for the shank were, respectively, 1.4 ± 1.99kN/m (median and interquartile range) and 41.5 ± 34.3Nm/deg. The results for the thigh segment were 1.79 ± 2.73kN/m and 30.5 ± 50.4Nm/deg. As previously reported in the literature dealing with the soft tissue artifact - considered as soft tissue deformation in this study - the computed stiffness coefficients were dependent on tasks, subjects, segments, and anatomical directions. The main advantage of SOD over previous methods lies in enabling estimation of a task-dependent 6 × 6 stiffness matrix of the interface between segments and external devices, useful in their modeling and assessment.