Biomechanical Effects of the Porous Structure of Gyroid and Voronoi Hip Implants: A Finite Element Analysis Using an Experimentally Validated Model

Abstract

Total hip arthroplasty (THA) is most likely one of the most successful surgical procedures in medicine. It is estimated that three in four patients live beyond the first post-operative year, so appropriate surgery is needed to alleviate an otherwise long-standing suboptimal functional level. However, research has shown that during a complete THA procedure, a solid hip implant inserted in the femur can damage the main arterial supply of the cortex and damage the medullary space, leading to cortical bone resorption. Therefore, this study aimed to design a porous hip implant with a focus on providing more space for better osteointegration, improving the medullary revascularisation and blood circulation of patients. Based on a review of the literature, a lightweight implant design was developed by applying topology optimisation and changing the materials of the implant. Gyroid and Voronoi lattice structures and a solid hip implant (as a control) were designed. In total, three designs of hip implants were constructed by using SolidWorks and nTopology software version 2.31. Point loads were applied at the x, y and z-axis to imitate the stance phase condition. The forces represented were x = 320 N, y = −170 N, and z = −2850 N. The materials that were used in this study were titanium alloys. All of the designs were then simulated by using Marc Mentat software version 2020 (MSC Software Corporation, Munich, Germany) via a finite element method. Analysis of the study on topology optimisation demonstrated that the Voronoi lattice structure yielded the lowest von Mises stress and displacement values, at 313.96 MPa and 1.50 mm, respectively, with titanium alloys as the materials. The results also indicate that porous hip implants have the potential to be implemented for hip implant replacement, whereby the mechanical integrity is still preserved. This result will not only help orthopaedic surgeons to justify the design choices, but could also provide new insights for future studies in biomechanics.

Machine Learning Integrating 99mTc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors

“…The authors pointed out that texture features have been important in most of the selected studies, and they identified the need for well-designed prospective studies to assess the value of radiomics in classifying renal neoplasia. CT is adequate for the analysis of the fine structure of hard and soft materials [38][39][40], being an excellent method for the extraction of fine radiomics information. These features can be used with in silico approaches that are important in simulating conditions and data that are not readily available in research and clinical practice, reducing experimental time and associated costs [41][42][43].…”

Nerve MR in the Differential Diagnosis of Neuropathies: A Case Series from a Single Center

“…Imaging is in continuous technological progress; new possibilities, such as artificial intelligence, are yet to be explored in nerve imaging, and further studies are needed [28][29][30].…”

Study on Flow Characteristics of Hydraulic Suction of Seabed Ore Particles

“…This work used Star CCM+ software to In order not to be impacted by varying mesh sizes, a convergence analysis [41] was conducted to guarantee the quality of the particle collection model. Since the nodule discussed in this work is a coarse particle, the trimmed mesh and the advancing layer mesh are used to generate the meshes which have been shown to be more suitable for the fluid-structure interaction of coarse particles [42]. This work used Star CCM+ software to generate four mesh densities ranging from 3 mm to 2.4 mm at the suction velocity of 1.8 m/s, and compared the calculation of the vertical forces in four models.…”