Dataset Open Access
Data publication: Deep learning for dose-averaged linear energy transfer estimation in pencil-beam scanning and double scattering proton plans with uncertainty-aware external validation
Kieslich, Aaron Markus;
Singh, Yerik;
Palkowitsch, Martina;
Starke, Sebastian;
Hennings, Fabian;
Troost, Esther Gera Cornelia;
Krause, Mechthild;
Bensberg, Jona;
Lühr, Armin;
Heinzelmann, Feline;
Bäumer, Christian;
Timmermann, Beate;
Depauw, Nicolas;
Shih, Helen A.;
Löck, Steffen
This repository contains the outputs, model checkpoints and result data of our deep-learning-based experiments for the approximation of Monte-Carlo-simulated linear energy transfer distributions and uncertainty estimation, which build the foundation for the corresponding article.
Files
(1.8 GB)
| Name | Size | |
|---|---|---|
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RODARE.zip
md5:f10a43bf976fb3efb9598d5fad05008d |
1.8 GB | Download |
338
14
views
downloads
| All versions | This version | |
|---|---|---|
| Views | 338 | 338 |
| Downloads | 14 | 14 |
| Data volume | 25.4 GB | 25.4 GB |
| Unique views | 333 | 333 |
| Unique downloads | 14 | 14 |
- Publication date:
- December 16, 2025
- DOI:
-
- Keyword(s):
- proton radiotherapy linear energy transfer deep learning pencil beam scanning double scattering uncertainty quantification
- Related identifiers:
- Identical to:
https://www.hzdr.de/publications/Publ-42451 - Referenced by:
https://www.hzdr.de/publications/Publ-42419 - Communities:
- License (for files):
- Creative Commons Attribution 4.0 International