Tool for simulating tumor dynamics in patient-specific anatomy reconstructed from the medical scans.
- at least 8 GB of RAM (brain256 consumes up to 5.3 GB)
- up to 8GB of free disk space
- A licensed MATLAB installation accessible via the command-line (has to be in the PATH variable)
- The MATLAB toolbox "Image Processing Toolbox"
- Tumor growth (Reaction-diffusion model)
- Tumor mass effect and intracranial pressure (Brain deformation model)
- Tumor hypoxia and necrosis
- Bayesian model calibration for personalized radiotherapy planning
- Fast execution thanks to highly-parallel architecture and adaptive grid refinement
- User-friendly; just provide patient anatomy and select the tumor model
- Developer-friendly: the models are implemented as modules, which allows an addition of new models and a combination of the existing ones
- Transferable: can be applied to other types of infiltrative tumors (e.g. multiple myeloma, liver lesions,...)
- Open source, self-contained C++ code
- Compatible with Linux/Mac OS
Please visit the GliomaSolver homepage for installation, tutorials, sample of glioma data, and much more 🐼.
For additional data and softwares, please visit the solver's homepage, section References. The most used links are also listed below.
- Data used for the personalized radiotherapy planning in [1] are available here
- A brain atlas and precomputed phase-field function, used for the deformation model, can be found here
- For automated image-registration tool see here
- For automated skull-stripping and brain tissue segmentation tool see here
In publications using GliomaSolver or data released with this solver please cite:
[1] Lipkova et al., Personalized Radiotherapy Design for Glioblastoma Using Mathematical Tumor Modelling, Multimodal Scans and Bayesian Inference. IEEE Transactions on Medical Imaging (2019).