Biomechanical performance design of joint prosthesis for medical rehabilitation via generative structure optimization

Abstract

This paper proposes a biomechanical performance design method of joint prosthesis for medical rehabilitation via Generative Structure Optimization (GSO). Firstly, the 3D reconstruction of manifold structure involving hard bone and cartilage is sequentially and progressively implemented from heterogeneous medical images such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) via iteration. On the basis of reconstructed mesh structure, the finite element method (FEM) is hereby employed to verify the structure by evaluating the mechanical force distribution. The biomechanical performance design model for 3 D printing (3DP) is then built using multi-objective optimization (MOO) by considering adaptive layer thickness, infill patterns and infill trajectories, etc. The GSO outlets a generative data-driven system which covers various stages such as personalized CT, subsequent 3 D reconstruction, further finite element analysis (FEA) and even structural parameter optimization. The physical experiment of Additive manufacturing (AM) proves that, the relative density, surface topography and wear-resisting performance of joint prosthesis can be improved by GSO which helps to improve biomechanical performance, including kinematics and dynamics. The proposed method may arouse the huge attention in the prosthesis applications to promote patients’ high-end customization well-being.

Keywords:

• Biomechanical performance design
• joint prosthesis
• 3D reconstruction
• finite element analysis (FEA)
• generative structure optimization (GSO)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work presented in this article is funded by the National Natural Science Foundation of China (Nos. 51775494, 51935009 and 51821093), National Key Research and Development Project of China (No. 2018YFB1700701), Zhejiang Key Research and Development Project (Nos. 2019C01141, LGG18E050007 and LGG20E050006).