Szczegóły publikacji
Opis bibliograficzny
EXPBrain: exponential integrators for glioblastoma brain tumor simulations / Magdalena Pabisz, Dominika Ciupek, Askold VILKHA, Maciej PASZYŃSKI // W: Computational Science – ICCS 2025 Workshops : 25th international conference : Singapore, Singapore, July 7–9, 2025 : proceedings, Pt. 1 / eds. Maciej Paszyński, Amanda S. Barnard, Yongjie Jessica Zhang. — Cham : Springer Nature Switzerland, cop. 2025. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 15907). — ISBN: 978-3-031-97553-0; e-ISBN: 978-3-031-97554-7. — S. 126–141. — Bibliogr., Abstr. — Publikacja dostępna online od: 2025-07-07
Autorzy (4)
- AGHPabisz Magdalena
- Ciupek Dominika
- AGHVilkha Askold
- AGHPaszyński Maciej
Dane bibliometryczne
| ID BaDAP | 161024 |
|---|---|
| Data dodania do BaDAP | 2025-07-18 |
| DOI | 10.1007/978-3-031-97554-7_10 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2025 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
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 x 248 x 256 to 194 x 248 xz 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 x 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.
