Data publication: A deep-learning-based surrogate model for Monte-Carlo simulations of the linear energy transfer in primary brain tumor patients treated with proton-beam radiotherapy

DOI

This repository contains the outputs and result data of our deep-learning-based experiments for the approximation of Monte-Carlo-simulated linear energy transfer distributions, which build the foundation for the corresponding article.

The Pytorch checkpoint of our finally chosen SegResNet architecture trained on the UPTD dose distributions is located at dd_pbs/Dose-LETd/clip_let_below_0.04/segresnet/all_trainvalid_data/training/lightning_logs/version_6358843/checkpoints/last.ckpt.

 

Moreover, we provide an exemplary data sample from a water phantom for trying our analysis pipeline.

Identifier
DOI https://doi.org/10.14278/rodare.2764
Related Identifier IsIdenticalTo https://www.hzdr.de/publications/Publ-38860
Related Identifier IsReferencedBy https://www.hzdr.de/publications/Publ-38858
Related Identifier IsPartOf https://doi.org/10.14278/rodare.2763
Related Identifier IsPartOf https://rodare.hzdr.de/communities/rodare
Metadata Access https://rodare.hzdr.de/oai2d?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:rodare.hzdr.de:2764
Provenance
Creator Starke, Sebastian ORCID logo; Kieslich, Aaron Markus; Palkowitsch, Martina; Hennings, Fabian; Troost, Esther Gera Cornelia ORCID logo; Krause, Mechthild ORCID logo; Bensberg, Jona; Hahn, Christian; Heinzelmann, Feline; Bäumer, Christian; Lühr, Armin ORCID logo; Timmermann, Beate; Löck, Steffen ORCID logo
Publisher Rodare
Publication Year 2024
Rights Creative Commons Attribution 4.0 International; Open Access; https://creativecommons.org/licenses/by/4.0/legalcode; info:eu-repo/semantics/openAccess
OpenAccess true
Contact https://rodare.hzdr.de/support
Representation
Resource Type Dataset
Discipline Life Sciences; Natural Sciences; Engineering Sciences