March 15, 2024 Dataset Open Access

Starke, Sebastian; Kieslich, Aaron Markus; Palkowitsch, Martina; Hennings, Fabian; Troost, Esther Gera Cornelia; Krause, Mechthild; Bensberg, Jona; Hahn, Christian; Heinzelmann, Feline; Bäumer, Christian; Lühr, Armin; Timmermann, Beate; Löck, Steffen

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{
  "issued": {
    "date-parts": [
      [
        2024, 
        3, 
        15
      ]
    ]
  }, 
  "abstract": "<p>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.</p>\n\n<p>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.</p>\n\n<p>&nbsp;</p>\n\n<p>Moreover, we provide an exemplary data sample from a water phantom for trying our analysis pipeline.</p>", 
  "DOI": "10.14278/rodare.2764", 
  "id": "2764", 
  "title": "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", 
  "type": "dataset", 
  "publisher": "Rodare", 
  "author": [
    {
      "family": "Starke, Sebastian"
    }, 
    {
      "family": "Kieslich, Aaron Markus"
    }, 
    {
      "family": "Palkowitsch, Martina"
    }, 
    {
      "family": "Hennings, Fabian"
    }, 
    {
      "family": "Troost, Esther Gera Cornelia"
    }, 
    {
      "family": "Krause, Mechthild"
    }, 
    {
      "family": "Bensberg, Jona"
    }, 
    {
      "family": "Hahn, Christian"
    }, 
    {
      "family": "Heinzelmann, Feline"
    }, 
    {
      "family": "B\u00e4umer, Christian"
    }, 
    {
      "family": "L\u00fchr, Armin"
    }, 
    {
      "family": "Timmermann, Beate"
    }, 
    {
      "family": "L\u00f6ck, Steffen"
    }
  ]
}