FINITE ELEMENT MODELING OF IN VIVO HUMAN KNEE BONES USING HR-PQCT: EFFECTS OF BOUNDARY CONDITIONS AND MODEL CONFIGURATION ON PREDICTED STRAIN ENERGY DENSITY
Bibliographic record
Abstract
INTRODUCTION Bone strength assessment is essential in musculoskeletal research for understanding bone mechanics under loading. High-resolution peripheral quantitative computed tomography (HR-pQCT) and micro-finite element (μFE) analysis provide insights into bone strength. While widely used for the distal radius and tibia, knee joint modeling is more complex due to interactions of bone, cartilage, and soft tissue, and the significantly larger size of the joint. This study aims to develop a knee bone μFE model using HR-pQCT data, focusing on boundary conditions and material properties affecting strain energy density (SED) in the femur and tibia. OBJECTIVE 1) Investigate the influence of boundary conditions on stress distribution in knee joint finite element models. 2) Evaluate how the elastic modulus of load transfer material influences bone mechanics. METHODS HR-pQCT scans of a 35-year-old female with a recent ACL injury were performed on the knee joint in full extension. A boundary material was applied to simulate a transitional layer between the bone and surrounding tissues. The material was generated using a voxel-based approach that mapped to the bone shape by extruding filled slices along the Z-axis (Figure 1). Finite element models with uniaxial compression boundary conditions were generated with two configurations of boundary materials: bone-shaped boundary material, which adapts to the shape of the largest epiphysis of the bone, or rectangular boundary materials, which create a square-shaped material around the minimum/maximum bounds of the epiphysis bone regions. Both types of models were solved with a range of boundary material elastic moduli (2000, 2500, 3000, 3500 MPa) and lengths extending from the bone surface of 1, 3, 5, and 7 mm. The primary output was model SED in subchondral regions of interest (ROI) to test the boundary material’s impact on mechanical predictions. RESULTS Tibial models contained 500 million degrees of freedom, and femur models included 900 million. As load transfer material length increased beyond 1 mm, the mean SED within ROIs initially decreased, then increased beyond 3 mm—suggesting an optimal load transfer material length between 3 mm and 7 mm. SED skewness and kurtosis increased with material length, indicating more heterogeneous stress distributions. Longer segments (e.g., 5-7 mm) substantially increased computational cost, highlighting a trade-off between the extent of material used for load transfer and simulation efficiency. The bone-shaped boundary material method was more computationally efficient and produced less variability as material length increased. As the elastic modulus of the load transfer material increased, average SED values also increased, particularly with longer PMMA segments. CONCLUSION We found that load transfer material length and elastic modulus significantly influence tibial stress distribution, with an optimal material length between 3 mm and 5 mm balancing mechanical performance and computational efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".