MOnte carlo code for QUIck proton dose calculation (moqui)

Installation

Requirements

• GDCM > 2
  • Please refer to GDCM installation guide
  • You can also install GDCM v3 using package manager (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
• The code has been tested with
  • GDCM v2 and CUDA v10.2 on Ubuntu 20.04
  • GDCM v3 and CUDA v11.8 on Ubuntu 22.04
  • GDCM v3 and CUDA v8.0 on Ubuntu 22.04
  • GDCM v3 and CUDA v12.6 on Alma Linux 9
• ZLIB
• Python3 (for phantom example)

Obtaining the code

$ git clone https://github.com/ferdymercury/moquimc.git

Compile the phantom case

$ 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

Running the phantom example

$ python3 ../tests/mc/phantom/create_phantom.py # create water phantom in /tmp/, you need to install numpy
$ ./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

Update Dec/26/2023

• There have been large updates in moqui and we added new features
  • Statistical uncertainty based stopping criteria (Please refer to the example input parameter Statistical stopping criteria)
  • Robust options (setup errors and density scaling, Please refer to the example input parameter Robust options)
  • Aperture handling
  • Support multiple calibration curves (You can override machine selection and define multiple calibration curves for a machine)
  • Unit weights per spot for Dij calculation (This only works for Dij scorer. The UnitWeight will be the absolute number of particles simulated)

Getting calibration curves

• moqui uses fitted functions for calibration curves
• You need to obtain stopping power ratio to water and radiation length per density and define compute_rsp_ and compute_rl_ functions in patient_material_t
• To obtain the curves:
  1. Obtain material information using TOPAS
  2. Calculate correction factors for desired SPR curve
  3. Calculate fitting curves and implement them in moqui
  4. You can refer to the fit_rsp.py for the curve fitting
• You can find the TOPAS extensions and example parameter file under treatment_machines/TOPAS
• These are updated version of the HU extension in TOPAS (https://github.com/topasmc/extensions/tree/master/HU)

Authors

Hoyeon Lee ([email protected]) Jungwook Shin Joost M. Verburg Mislav Bobić Brian Winey Jan Schuemann Harald Paganetti

Notes

You might need for old Tesla C2070 commands such as:

• Install patched nvidia-390 driver on Ubuntu 22: https://launchpad.net/%7Edtl131/+archive/ubuntu/nvidiaexp
• Install gcc5 and cuda8: https://askubuntu.com/questions/1442001/cuda-8-and-gcc-5-on-ubuntu-22-04-for-tesla-c2070
• Error with stncpy: https://stackoverflow.com/questions/76531467/nvcc-cuda8-gcc-5-3-no-longer-compiles-with-o1-on-ubuntu-22-04
• Error with float128: https://askubuntu.com/questions/1442001/cuda-8-and-gcc-5-on-ubuntu-22-04-for-tesla-c2070
• cmake ../ -DCUDAToolkit_ROOT=/opt/cuda-8.0 -DCMAKE_CUDA_COMPILER=/opt/cuda-8.0/bin/nvcc -DCMAKE_C_COMPILER=/opt/gcc5/gcc -DCMAKE_CXX_COMPILER=/opt/gcc5/g++ -DCMAKE_CUDA_ARCHITECTURES=20
• This might also be needed depending on the platform or CMake version: export PATH=/opt/gcc5:$PATH
• Need to fine-tune QtCreator adding a new custom compiler /opt/cuda-8.0/bin/nvcc and edit .config/clangd/config.yaml file with

CompileFlags:
Add:
  [
    '--cuda-path="/opt/cuda-8.0/"',
    --cuda-gpu-arch=sm_20,
    '-L"/opt/cuda-8.0/lib64/"',
    -lcudart,
  ]

• See clangd/clangd#858 and clangd/clangd#1815

For an Ampere GPU NVIDIA A40:

• cmake -DCUDAToolkit_ROOT=/usr/local/cuda-12.6 -DCMAKE_CUDA_ARCHITECTURES=86 -DCMAKE_CUDA_COMPILER=/usr/local/cuda-12.6/bin/nvcc

Acknowledgements

This work is supported by NIH/NCI R01 234210 "Fast Individualized Delivery Adaptation in Proton Therapy"