Convection-Enhanced Delivery (CED) is a technique where a therapeutic agent is infused under positive pressure directly into the brain tissue. CED is a promising method for treatment of patients with brain tumors. However, since the final concentration distribution is highly dependent on the location of the infusion, heterogeneity and anisotropy of the brain tissue, the infused agent may not reach the targeted region. In order to predict the final concentration distribution, optimize the location and infusion rate, numerical models can be of great help. In this study patient-specific geometry and parameters, obtained from Diffusion Tensor Imaging (DTI), were implemented in a numerical model which describes the flow and transport in an elastic deformable matrix. Fractional anisotropy (FA) was used to distinguish between grey and white matter and tortuosity to differentiate between inside and outside the brain tissue. However, to completely resolve the geometrical boundaries a higher resolution of the DT-images than used here (2mm) is necessary. The DT-images were in addition used to determine the orientation of the white matter fiber tracts and calibrate permeability and diffusion coefficients found from the literature. In the current study information about the porosity and Lamé parameters were also based on values found from the literature. Unfortunately, these values vary with several orders of magnitude and before the model presented here can produce reliable results consistent parameter values are needed. Finally, the obtained parameters were then read in from file based on a voxel approach, where one voxel in the DT-image is represented by one element in the grid used for the simulations. This allows a realistic representation of the physical boundaries using a structured grid. Given realistic literature values the calibration of the permeability and diffusion tensors were shown to be successful and resulted, as expected, in preferential flow in the direction of the white matter fiber tracts
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