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Data-driven particle transport algorithm for fast dose calculations.

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Dose calculation via Transformers

This repository contains the code to calculate the dose deposited by a mono-energetic beam of protons, for arbitrary patient geometries and beam energies.

  • GNU General Public License 3.0
  • Copyright: Oscar Pastor-Serrano, TU Delft

The 'main' branch contains the code for proton beamlets, while the 'dev_photons' branch contains the code for predictin full photon beams.

Credits

If you like this repository, please click on Star!

If you use the code for your research, please consider citing:

Pastor-Serrano, O., & Perkó, Z. (2021). Learning the Physics of Particle Transport via Transformers. https://doi.org/10.1609/aaai.v36i11.21466

Pastor-Serrano, O., & Perkó, Z. (2022). Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy. https://doi.org/10.1088/1361-6560/ac692e

Pastor-Serrano, O., Dong, P., Huang, C., Xing, L., & Perkó, Z. (2023). Sub-second photon dose prediction via transformer neural networks. https://doi.org/10.1002/mp.16231

This project is supported by the following institutions:

  • KWF Kanker Bestrijding
  • Department of Radiation Science and Technology (TU Delft)

Requirements

  • Tensorflow 2.5 or higher
  • pymedphys
  • tensorflow-addons

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