Uncertainty-aware spot rejection rate as quality metric for proton therapy

Alexander Schilling on behalf of the Bergen pCT collaboration and the SIVERT research training group

Implementation of the paper "Uncertainty-aware spot rejection rate as quality metric for proton therapy."

Repository Structure

src contains the Python source code. Executable scripts are at the top level and all modules are in sub-directories.

data contains supplementary data and the directory is used as output of the scripts.

Everything is implemented in Python with the required 3rd-party libraries listed in requirements.txt, which can be installed via pip:

$ pip install -r requirements.txt

Simulation Data

36258 treatment spot simulations in an anthropomorphic head phantom (715-HN) (Giacometti et al. 2017) and 35673 simulations in a second anthropomorphic head phantom (VHF) (Ackermann et al. 1995) with 1e7 primary protons each have been carried out. Used spot/rotation/energy combinations were determined through probing simulations, the results of which can be found in data/probing_results.csv and data/probing_results_vhf.csv. The simulation data is publicly available:

Feature Generation

Feature generation requires a MetaImage file of the head phantom you want to use and a Singularity image of the simulation environment (for lateral shift simulation).

The raw simulation data can be used to generate features for machine learning through the generate_features.py script:

$ cd src
$ python generate_features.py -o ../data/features.csv "../data/sims/*/*.json"

By default, this uses the 715-HN phantom at data/imageDump.mhd. --phantom and --rsp-file can be used to change these defaults.

By specifying shift parameters (e.g., -s 5), the script generates features for cases with errors in the form of lateral shifts from 1 mm up to the given value in 1 mm intervals.

The resulting feature sets (train, validation, test, and shifts) are publicly available:

Range Verification

To run a full evaluation of the range verification method on the above features, run the eval.py script:

$ cd src
$ python eval.py -c config/example.json -w ../data/eval -d cuda:0

The parameters of the run are defined in the configuration file at src/config/example.json. This should be adjusted to point to the correct feature files. The rest of the parameters are set to reproduce the paper.

Timing

To measure the runtime of different parts of the implementation, use timing.py.

This requires a fully trained model in a given working directory and a number of input simulation metafiles.

$ cd src
$ python timing.py -c config/example.json -w ../data/eval -d cuda:0 ../data/sims/*/*.json

References

Ackerman M. J., Spitzer V. M., Scherzinger A. L., and Whitlock D. G., 1995. The Visible Human data set: an image resource for anatomical visualization Medinfo 8 Pt 2, pp. 1195–1198.

Giacometti, V., Guatelli, S., Bazalova-Carter, M., Rosenfeld, A. B. and Schulte, R. W., 2017. Development of a high resolution voxelised head phantom for medical physics applications. Physica Medica, 33, pp.182-188.