期刊
出版社
WILEY
DOI: 10.1002/cnm.3440
关键词
brain biomechanics; cerebrospinal fluid; finite element modelling; smoothed particle hydrodynamics
类别
资金
- Engineering and Physical Sciences Research Council [EP/N509486/1]
- MRC [UKDRI-7006] Funding Source: UKRI
This study explored the stability and accuracy of key modelling parameters of an SPH model of the CSF when predicting relative brain/skull displacements. The Moving Least Squares (MLS) SPH formulation and Ogden rubber material model were found to be the most accurate and stable. Differences in strain and strain rate were observed between the SPH and FE models of CSF, with FE mesh anchoring the gyri and SPH showing higher levels of strains in the sulci. Tensile instability was identified as a key challenge of the SPH method, highlighting the need for further research in addressing this issue.
The Cerebrospinal Fluid (CSF) can undergo shear deformations under head motions. Finite Element (FE) models, which are commonly used to simulate biomechanics of the brain, including traumatic brain injury, employ solid elements to represent the CSF. However, the limited number of elements paired with shear deformations in CSF can decrease the accuracy of their predictions. Large deformation problems can be accurately modelled using the mesh-free Smoothed Particle Hydrodynamics (SPH) method, but there is limited previous work on using this method for modelling the CSF. Here we explored the stability and accuracy of key modelling parameters of an SPH model of the CSF when predicting relative brain/skull displacements in a simulation of an in vivo mild head impact in human. The Moving Least Squares (MLS) SPH formulation and Ogden rubber material model were found to be the most accurate and stable. The strain and strain rate in the brain differed across the SPH and FE models of CSF. The FE mesh anchored the gyri, preventing them from experiencing the level of strains seen in the in vivo brain experiments and predicted by the SPH model. Additionally, SPH showed higher levels of strains in the sulci compared to the FE model. However, tensile instability was found to be a key challenge of the SPH method, which needs to be addressed in future. Our study provides a detailed investigation of the use of SPH and shows its potential for improving the accuracy of computational models of brain biomechanics.
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