The optimal element size, material property distributions and modeling methods for finite element modeling of lumbar vertebra
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    Abstract:

    Objective To investigate the effects of element size and type, material property distributions of vertebral cancellous bone and simulation methods of cortical bone structure on the finite element (FE) results during the finite element modeling of lumbar vertebral body. Methods Based on QCT images of lumbar spine, 22 FE models of L2 without posterior structure were built by 6 element sizes (0.5, 1.0, 1.5, 2.0, 2.5, 3.0 mm), 2 heterogeneous material distribution methods of cancellous bone (300, 150) and 2 cortical bone modeling methods. The maximum displacement, strain energy, average stress and axial stiffness of these models were obtained to analyze and verify the results. Results When the element size was 0.5 mm, the axial stiffness of models with 10, 150 and 300 kinds of heterogeneous materials showed obvious differences; for the vertebral cancellous bone with 150 kinds of materials, the variation of average stress was not distinct under different element sizes; the average stress of the model using the outermost hexahedral elements to simulate the cortical bone structure was larger than that appending the skin to the outmost of the model. Conclusions It is more reasonable and effective to build the FE model of lumbar vertebral body with the method by 0.5 mm element size, 8-noded hexahedral elements, 150 kinds of heterogeneous materials, and using the outermost hexahedral elements to simulate the cortical bone structure. The research findings will lay a foundation for building subject-specific FE models of lumbar vertebral body on a large scale in future.

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CAI Kang-jian, WANG Li-zhen, YAO Jie, GONG He. The optimal element size, material property distributions and modeling methods for finite element modeling of lumbar vertebra[J]. Journal of medical biomechanics,2016,31(2):135-141

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History
  • Received:November 11,2015
  • Revised:December 07,2015
  • Adopted:
  • Online: April 28,2016
  • Published: