Abstract
Abstract
Objectives
This study aids the control of remodelling and strain response in bone; providing a quantified map of apparent modulus and strength in the proximal tibia in 3 anatomically relevant directions in terms of apparent density and factor groups.
Methods
7 fresh-frozen cadaveric specimens were quantified computed tomography (qCT) scanned, segmented and packed with 3 layers of 9mm side length cubic cores aligned to anatomical mechanical axes. Cores were removed with printed custom cutting and their densities found from qCT. Cores (n = 195) were quasi-statically compression tested. Modulus was estimated from a load cycle hysteresis loop, between 40% and 20% of yield stress. Sequential testing order in 3 orthogonal directions was randomised. Group differences were identified via an analysis of variance for the factors density, age, gender, testing order, subchondral depth, condyle and sub-meniscal location. Regression models were fit for significant factor sub-groups, predicting properties from density.
Results
Axial modulus was 1.5 times greater than the two transverse directions (p<0.001), between which no difference was found. For all test directions, differences were quantified for density and modulus across all subchondral depths (p<0.001). 60% of transverse modulus variation was explained by density within subgroups for each subchondral depth. Medial axial modulus was 1.3 times greater than the lateral side (p = 0.011). Lateral axial modulus halved over a 25mm depth whilst remaining constant for the medial side. Density explained 75% of variation when grouped by subchondral depth and condyle. Yield strength was well predicted across all test directions, with density explaining 81% of axial strength variation and no differences over subchondral depth.
Conclusions
The quantification of bone multiaxial modulus based on condyle and subchondral depth has been shown for the first time in a clinically viable protocol using conventional CT. Accounting for spatial variation improves upon literature property prediction models.
Declaration of Interest
(b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project.
