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Browsing by Subject "exponential integrators"

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    Item type:Book Chapter, Access status: Open Access ,
    EXPBrain: Exponential Integrators for Glioblastoma Brain Tumor Simulations
    (Springer, 2025) Pabisz, Magdalena; Ciupek, Dominika; Vilkha, Askold; Paszyński, Maciej; Paszynski, M., Barnard, A.S., Zhang, Y.J. (eds)
    Wydział Informatyki
    NOTE. This is a preprint of the paper with the same name in the Lecture Notes in Computer Science Journal. This preprint has not undergone peer review (when applicable) or any post-submission improvements or corrections. The Version of Record of this contribution is published in Paszynski, M., Barnard, A.S., Zhang, Y.J. (eds) Computational Science – ICCS 2025 Workshops. ICCS 2025. Lecture Notes in Computer Science, vol 15907, and is available online at: https://doi.org/10.1007/978-3-031-97554-7_10 In this paper we discuss a MATLAB implementation of the exponential integrators method employed for simulating of the brain tumor progression. As the input data we utilize publicly available T1-weighted magnetic resonance imaging dataset ds003826, representing healthy individuals. The data from these datasets are originally stored using NIfTI format. We select randomly one anonimized individual from the considered dataset. We normalize the brain scan data using min-max normalization to a range of 0 to 255. In the data from the dataset ds003826 the voxel resolution is not isotropic in all directions, so we interpolate the data from dimensions 176×248×256 into 194×248×256 in order to have proper proportions of the human brain. We set the data asa sequence of 256 PNG files with the resolution of 194 × 248. Having the MRI scan data, we run the exponential integrators method simulating the glioblastoma tumor growth using the Fisher-Kolmogorov diffusion-reaction model with logistic growth. We assume the initial tumor location and run the simulation predicting two years forward tumor growth. For the spatial discretization we employ the finite difference method, and for the temporal discretization we use the ultra-fast exponential integrators method. Our simulator generates the simulational results suitable for visualization using the ParaView tool.