Cortical bone mapping improves finite element strain prediction accuracy at the proximal femur

• Published in: Bone (New York, N.Y.) Vol. 136; p. 115348
• Main Authors: Schileo, Enrico, Pitocchi, Jonathan, Falcinelli, Cristina, Taddei, Fulvia
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-07-2020
• Subjects: Computed tomography; Cortical bone; Cortical bone mapping; Density-elasticity relationship; Finite elements; Optimisation; Proximal femur; Subject-specific Modelling; Validation; Validation; Finite elements; Computed tomography; Proximal femur; Optimisation; Density-elasticity relationship; Subject-specific Modelling; Cortical bone mapping; Cortical bone
• ISSN: 8756-3282; 1873-2763
• DOI: 10.1016/j.bone.2020.115348

Despite evidence of the biomechanical role of cortical bone, current state of the art finite element models of the proximal femur built from clinical CT data lack a subject-specific representation of the bone cortex.
Our main research hypothesis is that the subject-specific modelling of cortical bone layer from CT images, through a deconvolution procedure known as Cortical Bone Mapping (CBM, validated for cortical thickness and density estimates) can improve the accuracy of CT-based FE models of the proximal femur, currently limited by partial volume artefacts. Our secondary hypothesis is that a careful choice of cortical-specific density-elasticity relationship may improve model accuracy.
We therefore:
(i) implemented a procedure to include subject-specific CBM estimates of both cortical thickness and density in CT-based FE models.
(ii) defined alternative models that included CBM estimates and featured a cortical-specific or an independently optimised density-elasticity relationship.
(iii) tested our hypotheses in terms of elastic strain estimates and failure load and location prediction, by comparing with a published cohort of 14 femurs, where strain and strength in stance and fall loading configuration were experimentally measured, and estimated through reference FE models that did not explicitly model the cortical compartment.
Our findings support the main hypothesis: an explicit modelling of the proximal femur cortical bone layer including CBM estimates of cortical bone thickness and density increased the FE strains prediction, mostly by reducing peak errors (average error reduced by 30%, maximum error and 95th percentile of error distribution halved) and especially when focusing on the femoral neck locations (all error metrics at least halved). We instead rejected the secondary hypothesis: changes in cortical density-elasticity relationship could not improve validation performances. From these improved baseline strain estimates, further work is needed to achieve accurate strength predictions, as models incorporating cortical thickness and density produced worse estimates of failure load and equivalent estimates of failure location when compared to reference models.
In summary, we recommend including local estimates of cortical thickness and density in FE models to estimate bone strains in physiological conditions, and especially when designing exercise studies to promote bone strength.
•New femur FE models including Cortical Bone Mapping (CBM), i.e. local estimates of cortical thickness&density from CT data•CBM models improved bone strain estimates in the elastic range•Thus, CBM models can be used to estimate bone strains in exercise•Cortical-specific (experimental or optimised) density-elasticity law could not improve accuracy of CBM models•CBM models do not accurately estimate strength yet, but provide accurate strain data to develop biofidelic failure criteria