A biphasic hyperelastic model for the analysis of fluid and mass transport in brain tissue

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A biphasic hyperelastic model for the analysis of fluid and mass transport in brain tissue

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dc.contributor.author Smith, Joshua H.
dc.contributor.author Garcia, J. J.
dc.date.accessioned 2010-01-14T21:49:02Z
dc.date.available 2010-01-14T21:49:02Z
dc.date.issued 2009
dc.identifier.citation Garcia, J. J. and J. H. Smith (2009). "A biphasic hyperelastic model for the analysis of fluid and mass transport in brain tissue." Annals of Biomedical Engineering 37.2: 375-386 en_US
dc.identifier.uri http://hdl.handle.net/10385/566
dc.description.abstract A biphasic hyperelastic finite element model is proposed for the description of the mechanical behavior of brain tissue. The model takes into account finite deformations through an Ogden-type hyperelastic compressible function and a hydraulic conductivity dependent on deformation. The biphasic equations, implemented here for spherical symmetry using an updated Lagrangian algorithm, yielded radial coordinates and fluid velocities that were used with the convective-diffusive equation in order to predict mass transport in the brain. Results of the model were equal to those of a closed-form solution under infinitesimal deformations, however, for a wide range of material parameters, the model predicted important increments in the infusion sphere, reductions of the fluid velocities, and changes in the species content distribution. In addition, high localized deformation and stresses were obtained at the infusion sphere. Differences with the infinitesimal solution may be mainly attributed to geometrical nonlinearities related to the increment of the infusion sphere and not to material nonlinearities. en_US
dc.publisher Annals of Biomedical Engineering en_US
dc.subject infusion en_US
dc.subject hydrated tissues en_US
dc.subject convection enhanced delivery en_US
dc.title A biphasic hyperelastic model for the analysis of fluid and mass transport in brain tissue en_US
dc.type Article en_US
dc.identifier.doi http://dx.doi.org/10.1007/s10439-008-9610-0

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