TY - JOUR
T1 - Reduction of material groups for vertebral bone finite element simulation
T2 - cross comparison of grouping methods
AU - Strack, Daniel
AU - Rayudu, Nithin Manohar
AU - Kirschke, Jan S.
AU - Baum, Thomas
AU - Subburaj, Karupppasamy
N1 - Publisher Copyright:
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2024
Y1 - 2024
N2 - In patient-specific biomechanical modeling, the process of image-to-mesh-material mapping is important, and various strategies have been explored for assigning the number of groups of unique material properties to the mesh. This study aims to cross-compare different grouping strategies to identify the minimum number of unique groups necessary for accurately calculating the fracture load of vertebral bones. We analyzed 12 vertebral specimens by experimentally determining the biomechanical fracture load and acquiring corresponding CT scans. After geometry extraction and meshing, we applied commonly used fixed-value strategies for reducing the number of unique groups, such as Modulus Gaping and Percentual Thresholding. Additionally, we introduced a patient-specific adaptive grouping method based on K-means clustering, which allowed us to maintain a consistent number of groups of unique material properties across the study. A total of 204 simulations were performed, achieving a potential 98% reduction in the number of individual material parameters while maintaining a strong correlation with experimental results when utilizing Percentual Thresholding or Adaptive Clustering, compared to Modulus Gaping. The findings demonstrate the feasibility of significantly reducing simulation complexity while maintaining the accuracy of patient-specific models that strongly correlate with experimental results. This reduction enables efficient processing of patient-specific biomechanical models derived from image data, offering potential benefits for clinicians, particularly in resource-constrained settings.
AB - In patient-specific biomechanical modeling, the process of image-to-mesh-material mapping is important, and various strategies have been explored for assigning the number of groups of unique material properties to the mesh. This study aims to cross-compare different grouping strategies to identify the minimum number of unique groups necessary for accurately calculating the fracture load of vertebral bones. We analyzed 12 vertebral specimens by experimentally determining the biomechanical fracture load and acquiring corresponding CT scans. After geometry extraction and meshing, we applied commonly used fixed-value strategies for reducing the number of unique groups, such as Modulus Gaping and Percentual Thresholding. Additionally, we introduced a patient-specific adaptive grouping method based on K-means clustering, which allowed us to maintain a consistent number of groups of unique material properties across the study. A total of 204 simulations were performed, achieving a potential 98% reduction in the number of individual material parameters while maintaining a strong correlation with experimental results when utilizing Percentual Thresholding or Adaptive Clustering, compared to Modulus Gaping. The findings demonstrate the feasibility of significantly reducing simulation complexity while maintaining the accuracy of patient-specific models that strongly correlate with experimental results. This reduction enables efficient processing of patient-specific biomechanical models derived from image data, offering potential benefits for clinicians, particularly in resource-constrained settings.
KW - Biomechanical modeling
KW - bone fracture
KW - finite element analysis
KW - material properties grouping
KW - spine
UR - http://www.scopus.com/inward/record.url?scp=85210085422&partnerID=8YFLogxK
U2 - 10.1080/10255842.2024.2422901
DO - 10.1080/10255842.2024.2422901
M3 - Article
C2 - 39512144
AN - SCOPUS:85210085422
SN - 1025-5842
JO - Computer Methods in Biomechanics and Biomedical Engineering
JF - Computer Methods in Biomechanics and Biomedical Engineering
ER -