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In this paper, we discuss MATLAB implementation of the exponential integrators method employed for simulations of brain tumor progression. As the input data, we utilize the publicly available T1-weighted magnetic resonance imaging dataset ds003826, representing healthy individuals. The data is originally stored using NIfTI format. We randomly select one anonymized 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 ds003826, the voxel resolution is not isotropic in all directions, so we interpolate the data from dimensions \(176\times 248 \times 256\) to \(194 \times 248 \times 256\) in order to have proper proportions of the human brain. We set the data as a sequence of 256 PNG files with the resolution of \(194 \times 248\) . 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 the tumor growth two years forward. 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 results suitable for visualization using the ParaView tool.

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EXPBrain: Exponential Integrators for Glioblastoma Brain Tumor Simulations

  • 会议论文

  • 发表日期：

    2025年

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  • Magdalena Pabisz,
  • Dominika Ciupek,
  • Askold Vilkha,
  • Maciej Paszyński

摘要

In this paper, we discuss MATLAB implementation of the exponential integrators method employed for simulations of brain tumor progression. As the input data, we utilize the publicly available T1-weighted magnetic resonance imaging dataset ds003826, representing healthy individuals. The data is originally stored using NIfTI format. We randomly select one anonymized 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 ds003826, the voxel resolution is not isotropic in all directions, so we interpolate the data from dimensions \(176\times 248 \times 256\) to \(194 \times 248 \times 256\) in order to have proper proportions of the human brain. We set the data as a sequence of 256 PNG files with the resolution of \(194 \times 248\) . 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 the tumor growth two years forward. 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 results suitable for visualization using the ParaView tool.