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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