Details
| Original language | English |
|---|---|
| Journal | Biomechanics and Modeling in Mechanobiology |
| Early online date | 13 Nov 2024 |
| Publication status | E-pub ahead of print - 13 Nov 2024 |
Abstract
Sophisticated high-fidelity simulations can predict bone mass density (BMD) changes around a hip implant after implantation. However, these models currently have high computational demands, rendering them impractical for clinical settings. Model order reduction techniques offer a remedy by enabling fast evaluations. In this work, a non-intrusive reduced-order model, combining proper orthogonal decomposition with radial basis function interpolation (POD-RBF), is established to predict BMD distributions for varying implant positions. A parameterised finite element mesh is morphed using Laplace’s equation, which eliminates tedious remeshing and projection of the BMD results on a common mesh in the offline stage. In the online stage, the surrogate model can predict BMD distributions for new implant positions and the results are visualised on the parameterised reference mesh. The computational time for evaluating the final BMD distribution around a new implant position is reduced from minutes to milliseconds by the surrogate model compared to the high-fidelity model. The snapshot data, the surrogate model parameters and the accuracy of the surrogate model are analysed. The presented non-intrusive surrogate model paves the way for on-the-fly evaluations in clinical practice, offering a promising tool for planning and monitoring of total hip replacements.
Keywords
- Bone remodelling, Parametric surrogate model, Patient-specific simulation, Proper orthogonal decomposition, Radial basis function interpolation
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Biotechnology
- Mathematics(all)
- Modelling and Simulation
- Engineering(all)
- Mechanical Engineering
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In: Biomechanics and Modeling in Mechanobiology, 13.11.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A non-intrusive reduced-order model for finite element analysis of implant positioning in total hip replacements
AU - Reiber, Marlis
AU - Bensel, Fynn
AU - Zheng, Zhibao
AU - Nackenhorst, Udo
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024/11/13
Y1 - 2024/11/13
N2 - Sophisticated high-fidelity simulations can predict bone mass density (BMD) changes around a hip implant after implantation. However, these models currently have high computational demands, rendering them impractical for clinical settings. Model order reduction techniques offer a remedy by enabling fast evaluations. In this work, a non-intrusive reduced-order model, combining proper orthogonal decomposition with radial basis function interpolation (POD-RBF), is established to predict BMD distributions for varying implant positions. A parameterised finite element mesh is morphed using Laplace’s equation, which eliminates tedious remeshing and projection of the BMD results on a common mesh in the offline stage. In the online stage, the surrogate model can predict BMD distributions for new implant positions and the results are visualised on the parameterised reference mesh. The computational time for evaluating the final BMD distribution around a new implant position is reduced from minutes to milliseconds by the surrogate model compared to the high-fidelity model. The snapshot data, the surrogate model parameters and the accuracy of the surrogate model are analysed. The presented non-intrusive surrogate model paves the way for on-the-fly evaluations in clinical practice, offering a promising tool for planning and monitoring of total hip replacements.
AB - Sophisticated high-fidelity simulations can predict bone mass density (BMD) changes around a hip implant after implantation. However, these models currently have high computational demands, rendering them impractical for clinical settings. Model order reduction techniques offer a remedy by enabling fast evaluations. In this work, a non-intrusive reduced-order model, combining proper orthogonal decomposition with radial basis function interpolation (POD-RBF), is established to predict BMD distributions for varying implant positions. A parameterised finite element mesh is morphed using Laplace’s equation, which eliminates tedious remeshing and projection of the BMD results on a common mesh in the offline stage. In the online stage, the surrogate model can predict BMD distributions for new implant positions and the results are visualised on the parameterised reference mesh. The computational time for evaluating the final BMD distribution around a new implant position is reduced from minutes to milliseconds by the surrogate model compared to the high-fidelity model. The snapshot data, the surrogate model parameters and the accuracy of the surrogate model are analysed. The presented non-intrusive surrogate model paves the way for on-the-fly evaluations in clinical practice, offering a promising tool for planning and monitoring of total hip replacements.
KW - Bone remodelling
KW - Parametric surrogate model
KW - Patient-specific simulation
KW - Proper orthogonal decomposition
KW - Radial basis function interpolation
UR - http://www.scopus.com/inward/record.url?scp=85208919363&partnerID=8YFLogxK
U2 - 10.1007/s10237-024-01903-w
DO - 10.1007/s10237-024-01903-w
M3 - Article
AN - SCOPUS:85208919363
JO - Biomechanics and Modeling in Mechanobiology
JF - Biomechanics and Modeling in Mechanobiology
SN - 1617-7959
ER -