WM.dat, GM.dat

white matter binary mask, gray matter binary mask

InputParameters.txt

diffusivity in white matter [cm^2/day] (Dw)
proliferation rate [1/day] (rho)
final simulation time [day] (tend)

TumorIC.txt

tumor initial location (icx, icy, icz)

HGG_data.dat

tumor cell density [fraction]

tumFLAIR.dat

binary mask, FLAIR MRI scan

tumT1c.dat

T1 gadolinium enhanced (T1Gd) scan, categorical 0, 1, 2, 4

tumPET.dat

PET-FET scan, float

LikelihoodInput.txt

PETsigma2, PETscale, T1uc, T2uc, slope

$ python -m pip install -e .

$ ./fun.py
2.3475835715578407e+07
256x256x256le.raw

:; ./likelihood/likelihood
2.5588270414102585e+07

Coverage report

$ make -B 'CXXFLAGS = -fprofile-arcs -ftest-coverage' 'CFLAGS = -fprofile-arcs -ftest-coverage' 'LDFLAGS = -lgcov' 'LDDFLAGS = -lgcov'
$ ./brain
$ gcovr --html-details index.html

1. http:https://tdo.sk/~janka/GliomaWebsite/index.html

2. Lipková, J., Angelikopoulos, P., Wu, S., Alberts, E., Wiestler, B., Diehl, C., ... & Menze, B. (2019). Personalized radiotherapy design for glioblastoma: Integrating mathematical tumor models, multimodal scans, and bayesian inference. IEEE transactions on medical imaging, 38(8), 1875-1884. 10.1109/TMI.2019.2902044