Young patients receiving metallic bone implants after surgical resection of bone cancer require implants that last into adulthood, and ideally life-long. Porous implants with similar stiffness to bone can promote bone ingrowth and thus beneficial clinical outcomes. A mechanical remodelling stimulus, strain energy density (SED), is thought to be the primary control variable of the process of bone growth into porous implants. The sequential process of bone growth needs to be taken into account to develop an accurate and validated bone remodelling algorithm, which can be employed to improve porous implant design and achieve better clinical outcomes. A bone remodelling algorithm was developed, incorporating the concept of bone connectivity (sequential growth of bone from existing bone) to make the algorithm more physiologically relevant. The algorithm includes adaptive elastic modulus based on apparent bone density, using a node-based model to simulate local remodelling variations while alleviating numerical checkerboard problems. Strain energy density (SED) incorporating stress and strain effects in all directions was used as the primary stimulus for bone remodelling. The simulations were developed to run in MATLAB interfacing with the commercial FEA software ABAQUS and Python. The algorithm was applied to predict bone ingrowth into a porous implant for comparison against data from a sheep model.Abstract
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Methods
Long-term survival of massive prostheses used to treat bone cancers is associated with extra-cortical bone growth and osteointegration into a grooved hydroxyapatite coated collar positioned adjacent to the transection site on the implant shaft [1]. The survivorship at 10 years reduces from 98% to 75% where osteointegration of the shaft does not occur. Although current finite element (FE) methods successfully model bone adaption, optimisation of adventitious new bone growth and osteointegration is difficult to predict. There is thus a need to improve existing FE models by including biological processes of osteoconduction and osteoinduction. The principal bone adaptation criteria is based on the standard strain-energy remodeling algorithm, where the rate of remodeling is controlled by the difference in the stimulus against the reference value [3]. The additional concept of bone connectivity was introduced, to limit bone growth to neighbouring elements (cells) adjoining existing bone elements. The algorithm was developed on a cylindrical model before it was used on an ovine model. The geometry and material properties from two ovine tibiae were obtained from computed tomography (CT) scans and used to develop FE models of the tibiae implanted with a grooved collar. The bones were assigned inhomogeneous material properties based on the CT grey values and typical ovine walking load conditions were applied. The FE results show a region of bone tissue growth below the implanted collar and a small amount of osteointegration with the implant, which is in good agreement to clinical results. Some histological results suggest that further bone growth is possible and potential improvements to the model will be discussed. In summary, by including an algorithm that describes osteoconduction, adventitious bone growth can be predicted.
