A novel numerical model for the prediction of patient-dependent bone density loss in microgravity based on micro-CT images


The deterioration of the musculoskeletal system is a serious health concern for long-term space missions. The accumulated information over the past decades of space flights showed that microgravity impacts significantly the musculoskeletal system with muscle atrophy and bone loss. Until now, it has been difficult to make reasonable predictions of the bone loss for prolonged space missions due to the lack of in-space experimental data and weak understanding of the mechanobiological bone mechanisms. On earth, the healthy musculoskeletal degradation is mainly age related with osteoporosis and delayed fracture healing. A better understanding of the bone mechanobiological functions could help us improve our model predictions of the musculoskeletal health system during long-term space missions. We develop a numerical model able to predict the bone loss at the mesoscopic scale (bone trabecula) in microgravity. The model is able to correlate the calculated bone degradation mechanism with data available in the literature showing the effective bone density loss measured experimentally. An optimization algorithm is used for an average bone microstructure distribution and long-term prediction. Extrapolation is made to link the local bone loss at the structural scale with the corresponding effective bone strength. The first part of the paper details the extraction of the bone microstructure using micro-CT images and numerical model development. Next, the degradation and optimization schemes are detailed. Finally, some results are presented for long-term degradation.

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Bagherian, A., Baghani, M., George, D. et al. A novel numerical model for the prediction of patient-dependent bone density loss in microgravity based on micro-CT images. Continuum Mech. Thermodyn. 32, 927–943 (2020). https://doi.org/10.1007/s00161-019-00798-8

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  • Bone density loss
  • Microgravity
  • Degradation
  • Microstructure
  • Finite element
  • Optimization
  • Micro-CT