Skip to main content

Python tools for GATE GAN simulations

Project description

GAGA = GAN for GATE

pip install gaga-phsp

Scripts associated with the publication : Phys Med Biol. 2019 doi: https://doi.org/10.1088/1361-6560/ab3fc1 Generative adversarial networks (GAN) for compact beam source modelling in Monte Carlo simulations Sarrut D, Krah N, Létang JM. https://www.ncbi.nlm.nih.gov/pubmed/31470418

A method is proposed and evaluated to model large and inconvenient phase space files used in Monte Carlo simulations by a compact Generative Adversarial Network (GAN). The GAN is trained based on a phase space dataset to create a neural network, called Generator (G), allowing G to mimic the multidimensional data distribution of the phase space. At the end of the training process, G is stored with about 0.5 million weights, around 10MB, instead of few GB of the initial file. Particles are then generated with G to replace the phase space dataset.
 
 This concept is applied to beam models from linear accelerators (linacs) and from brachytherapy seed models. Simulations using particles from the reference phase space on one hand and those generated by the GAN on the other hand were compared. 3D distributions of deposited energy obtained from source distributions generated by the GAN were close to the reference ones, with less than 1% of voxel-by-voxel relative difference. Sharp parts such as the brachytherapy emission lines in the energy spectra were not perfectly modeled by the GAN. Detailed statistical properties and limitations of the GAN-generated particles still require further investigation, but the proposed exploratory approach is already promising and paves the way for a wide range of applications

Examples : https://github.com/OpenGATE/GateContrib/tree/master/dosimetry/gaga-phsp

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for gaga-phsp, version 0.5.1
Filename, size File type Python version Upload date Hashes
Filename, size gaga_phsp-0.5.1-py3-none-any.whl (8.6 kB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size gaga-phsp-0.5.1.tar.gz (6.1 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page