Geometric variation of the human tibia-fibula: a public dataset of tibia-fibula surface meshes and statistical shape model

Effect of morphology and cortical thickness variations on tibial strains in different movements

Morphology and cortical thickness of tibia bone influence the strength and strain distribution of bone and also influence fatigue fracture risk. However, current studies have not extensively explored the effect of morphology and cortical thickness on tibial strain distribution during different activities. This study aims to assess the effect of tibial morphology and cortical thickness on tibial strain during six different sports movements. The tibial surfaces were reconstructed from 40 males' CT data, with cortical thickness assessed at the outer surface. A statistical shape model captured main variations in tibial morphology and cortical thickness. Finite Element models were created by scaling the mean shape along the first four principal components. Muscle and joint forces from different activities were calculated using static optimization and joint reaction analysis and applied to the models, assessing strained volume and peak strain at middle and distal tibia. The first four principal components accounted for 87 % of the total cumulative variance. Perturbations in the second principal components resulted in the greatest relative differences in peak mid-tibia tensile (128 %) and distal-tibia compressive (160 %) strain during sidestep cutting, but perturbations in the first principal components resulted in the greatest relative differences during other activities (70 %∼118 %, 107 %∼129 %). Perturbations in the first four principal components resulted in the small relative differences in strained volume during walking (-9%∼5%). For runners, tibial size and cortical thickness are more related to tibial fatigue fracture risk, whereas for athletes with frequent directional changes, like basketball players, the tibial shaft size is more relevant.

Predicting Tibia-Fibula Geometry and Density From Anatomical Landmarks Via Statistical Appearance Model: Influence of Errors on Finite Element-Calculated Bone Strain

State-of-the-art participant-specific finite element models require advanced medical imaging to quantify bone geometry and density distribution; access to and cost of imaging is prohibitive to the use of this approach. Statistical appearance models may enable estimation of participants' geometry and density in the absence of medical imaging. The purpose of this study was to: (1) quantify errors associated with predicting tibia-fibula geometry and density distribution from skin-mounted landmarks using a statistical appearance model and (2) quantify how those errors propagate to finite element-calculated bone strain. Participant-informed models of the tibia and fibula were generated for thirty participants from height and sex and from twelve skin-mounted landmarks using a statistical appearance model. Participant-specific running loads, calculated using gait data and a musculoskeletal model, were applied to participant-informed and CT-based models to predict bone strain using the finite element method. Participant-informed meshes illustrated median geometry and density distribution errors of 4.39-5.17 mm and 0.116-0.142 g/cm3, respectively, resulting in large errors in strain distribution (median RMSE = 476-492 με), peak strain (limits of agreement =±27-34%), and strained volume (limits of agreement =±104-202%). These findings indicate that neither skin-mounted landmark nor height and sex-based predictions could adequately approximate CT-derived participant-specific geometry, density distribution, or finite element-predicted bone strain and therefore should not be used for analyses comparing between groups or individuals.

Variability in tibia-fibular geometry is associated with increased tibial strain from running loads

Variation in tibial geometry may alter strain magnitude and distribution during locomotion. We investigated the effect of tibia-fibula geometric variations on tibial strain with running loads applied at various speeds. Participant-specific three-dimensional models of the tibia-fibula were created using lower limb computed tomography scans from 30 cadavers. Finite-element models were developed in FEBio, and running loads from 3, 4 and 5 m s-1 were applied to extract effective strain from the tibial shaft. Linear regression models evaluated the relationship between geometric characteristics and effective strain along the tibial shaft. We found a statistically significant positive relationship between: (i) increased thickness of the midshaft to upper tibia with increased condyle prominence and effective strain at points along the distal anterolateral and proximal posterior regions of the tibial shaft; and (ii) increased midshaft cortical thickness and effective strain at points along the medial aspect of the distal tibial shaft. It is possible that increased thickness in the more proximal region of the tibia causes strain to redistribute to areas that are more susceptible to the applied loads. A thickness imbalance between the upper and distal portions of the tibial shaft could have a negative impact on tibial stress injury risk.