QCT-FE modeling of the proximal tibia: Effect of mapping strategy on convergence time and model accuracy

• Published in: Medical engineering & physics Vol. 88; pp. 41 - 46
• Main Authors: Ashjaee, Nima, Kalajahi, S. Mehrdad Hosseini, Johnston, James D.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.02.2021
• Subjects: Bone mechanics; Finite element modeling; Gauss points; Material mapping; Finite element modeling; Bone mechanics; Material mapping; Gauss points
• ISSN: 1350-4533; 1873-4030; 1873-4030
• DOI: 10.1016/j.medengphy.2020.12.006

•Various material mapping methods are used with subject-specific finite element models.•Key methods include: constant-E, element-based, and node-based.•Methods were compared in terms of accuracy, convergence-time, and run-time.•Element-based method converged with a larger element compared to other approaches.•Element-based method resulted in similar accuracy and shorter run-time. Quantitative computed tomography (QCT) based finite element (FE) modeling, referred to as QCT-FE, has seen rapid growth and application for modeling bone mechanics. With this approach, varying bone material properties are set via experimentally-derived density-modulus equations. One challenge though associated with QCT-FE is to identify the appropriate mapping strategy for assigning elastic moduli to elements. The goal of this study was to evaluate different QCT-FE mapping strategies to identify the optimum approach with fastest convergence rate and highest accuracy. Four proximal tibial medial compartments were imaged using QCT and experimentally tested to characterize proximal tibial subchondral bone stiffness at four surface points, resulting in a total of 16 indentation measures. Three material mapping methods were analyzed: (1) constant-E where an average elastic modulus was assigned to each element; (2) node-based where the material properties were first mapped on nodes then interpolated to Gaussian integration points; and (3) element-based in which the material properties were directly assigned to Gaussian integration points. Different element sizes were assessed with edge-lengths ranging from 0.9 to 3 mm. Results indicated that all converged models showed similar coefficient-of-determination (R2) and normalized root-mean-square errors (RMSE%). Though, the constant-E and node-based methods converged with the element edge-length of 1.5 mm (prediction error of 4.8% and 2.5%, respectively) whereas the element-based method converged with a larger element having an edge-length 2.5 mm (error = 4.9%). In conclusion, the element-based method, with a larger element size, resulted in similar predictive accuracy, faster convergence and shorter run-times relative to the constant-E and node-based approaches. As such, we recommend the element-based method for future subject-specific QCT-FE modeling.