BONE REMODELLING PREDICTION FOR IMPROVED POROUS IMPLANT DESIGN

Abstract

AbstractObjectivesYoung 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.MethodsA 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.ResultsThe accuracy of the predicted bone remodelling was verified for standard loading cases (bending, torsion) against analytical calculations. Good convergence was achieved. The algorithm predicted good bone remodelling and growth into the investigated porous implant. Using the standard algorithm without connectivity, bone started to remodel at locations unconnected to any bone, which is physiologically implausible. The implementation of bone connectivity ensures the gradual process of bone growth into the implant pores from the sides. The bone connectivity algorithm predicted that the full remodelling required more time (approximately 50% longer), which should be considered when developing post-surgical rehabilitation strategies for patients. Both algorithms with and without bone connectivity implementation converged to same final stiffness (less than 0.01% difference). Almost all nodes reached the same density value, with only a limited number of nodes (less than 1%) in transition areas with a strong density gradient having noticeable differences.ConclusionsAn improved bone remodelling algorithm based on strain energy density that modelled the sequential process of bone growth has been developed and tested. For a porous metallic bone implant the same final bone density distribution as for the original adaptive elasticity theory was predicted, with a slower and more fidelic process of growth from existing surrounding bone into the porous implant.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.