Finite element analysis of bone remodelling with piezoelectric effects using an open-source framework.
Yogesh Deepak BansodMaeruan KebbachDaniel KluessRainer BaderUrsula Van RienenPublished in: Biomechanics and modeling in mechanobiology (2021)
Bone tissue exhibits piezoelectric properties and thus is capable of transforming mechanical stress into electrical potential. Piezoelectricity has been shown to play a vital role in bone adaptation and remodelling processes. Therefore, to better understand the interplay between mechanical and electrical stimulation during these processes, strain-adaptive bone remodelling models without and with considering the piezoelectric effect were simulated using the Python-based open-source software framework. To discretise numerical attributes, the finite element method (FEM) was used for the spatial variables and an explicit Euler scheme for the temporal derivatives. The predicted bone apparent density distributions were qualitatively and quantitatively evaluated against the radiographic scan of a human proximal femur and the bone apparent density calculated using a bone mineral density (BMD) calibration phantom, respectively. Additionally, the effect of the initial bone density on the resulting predicted density distribution was investigated globally and locally. The simulation results showed that the electrically stimulated bone surface enhanced bone deposition and these are in good agreement with previous findings from the literature. Moreover, mechanical stimuli due to daily physical activities could be supported by therapeutic electrical stimulation to reduce bone loss in case of physical impairment or osteoporosis. The bone remodelling algorithm implemented using an open-source software framework facilitates easy accessibility and reproducibility of finite element analysis made.
Keyphrases
- bone mineral density
- bone loss
- postmenopausal women
- body composition
- soft tissue
- physical activity
- magnetic resonance
- systematic review
- magnetic resonance imaging
- machine learning
- endothelial cells
- spinal cord injury
- deep learning
- finite element analysis
- induced pluripotent stem cells
- high speed
- data analysis
- risk assessment
- stress induced
- atomic force microscopy