A polynomial hyperelastic model for the mixture of fat and glandular tissue in female breast
|Calvo Gallego, José Luis
Martínez Reina, Francisco Javier
Domínguez Abascal, Jaime
|Universidad de Sevilla. Departamento de Ingeniería Mecánica y de Fabricación
|In the breast of adult women, glandular and fat tissues are intermingled and cannot be clearly distinguished. This work studies if this mixture can be treated as a homogenized tissue. A mechanical model is proposed for the ...
In the breast of adult women, glandular and fat tissues are intermingled and cannot be clearly distinguished. This work studies if this mixture can be treated as a homogenized tissue. A mechanical model is proposed for the mixture of tissues as a function of the fat content. Different distributions of individual tissues and geometries have been tried to verify the validity of the mixture model. A multiscale modelling approach was applied in a finite element model of a representative volume element (RVE) of tissue, formed by randomly assigning fat or glandular elements to the mesh. Both types of tissues have been assumed as isotropic, quasi-incompressible hyperelastic materials, modelled with a polynomial strain energy function, like the homogenized model. The RVE was subjected to several load cases from which the constants of the polynomial function of the homogenized tissue were fitted in the least squares sense. The results confirm that the fat volume ratio is a key factor in determining the properties of the homogenized tissue, but the spatial distribution of fat is not so important. Finally, a simplified model of a breast was developed to check the validity of the homogenized model in a geometry similar to the actual one.
|Ministerio de Economia, Industria y Competitividad (MINECO). España
|Calvo Gallego, J.L., Martínez Reina, F.J. y Domínguez Abascal, J. (2015). A polynomial hyperelastic model for the mixture of fat and glandular tissue in female breast. International Journal for Numerical Methods in Biomedical Engineering, 31 (9), e02723. https://doi.org/10.1002/cnm.2723.
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