- GDCM (Please refer to GDCM v2 installation guide) (libgdcm-dev in Ubuntu 22)
- If you get spurious warnings in CMake, and they annoy you, consider installing (Ubuntu 22): libgdcm-tools libvtkgdcm-cil libvtkgdcm-dev libvtkgdcm-java python3-vtkgdcm
- CUDA
- ZLIB
- The code has been tested with GDCM v2 and CUDA v10.2, as well as GDCM v3 and CUDA v8.0
- Python3 (for phantom example)
$ git clone https://github.com/ferdymercury/moquimc.git
$ cd moquimc
$ mkdir build
$ cd build
$ cmake ..
$ make
- You can specify a custom CUDA path in the cmake command, for example:
-DCUDAToolkit_ROOT=/opt/cuda-8.0 -DCMAKE_CUDA_COMPILER=/opt/cuda-8.0/bin/nvcc
. The default is the nvcc found within thhe PATH environment variable. - You can specify a custom CUDA compute capability via
-DCMAKE_CUDA_ARCHITECTURES=20
. The default is to use CUDA compute capability 7.5
$ python create_phantom.py # create water phantom in /tmp/, find script inside tests/mc/phantom folder
$ ./tests/mc/phantom/phantom_env --lxyz 100 100 350 --pxyz 0.0 0.0 -175 --nxyz 200 200 350 --spot_energy 200.0 0.0 --spot_position 0 0 0.5 --spot_size 30.0 30.0 --histories 100000 --phantom_path /tmp/water_phantom.raw --output_prefix ./ --gpu_id 0 > ./log.out
Or simply:
$ ctest -V -R phantom_env
Hoyeon Lee
Jungwook Shin
Joost M. Verburg
Mislav Bobić
Brian Winey
Jan Schuemann
Harald Paganetti
This work is supported by NIH/NCI R01 234210 "Fast Individualized Delivery Adaptation in Proton Therapy"