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Monte Carlo Modeling of Elekta VERSA HD for Out-of-Field Dose Characterization

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  • معلومة اضافية
    • Contributors:
      Imagerie Tomographique et Radiothérapie; Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS); Université Claude Bernard Lyon 1 (UCBL); Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS); Radiothérapie Moléculaire et Innovation Thérapeutique (RaMo-IT); Institut Gustave Roussy (IGR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay; David Sarrut; Simon Rit
    • بيانات النشر:
      CCSD
    • الموضوع:
      2024
    • Collection:
      Université de Lyon: HAL
    • الموضوع:
      lyon; France
    • نبذة مختصرة :
      International audience ; Modern radiotherapy has significantly improved cancer patient survival rates, yet challenges remain in optimizing both tumor response and quality of life. Out-of-field doses play a role potentially impacting patient overall survival, but are not well-estimated by the conventional treatment planning systems. We propose here the development of a full Monte Carlo simulation model of the Elekta VERSA HD accelerator to assess the out-of-field deposited doses. We introduce an original Compton splitting method aiming to compensate for simulation low convergence in the out-of-field region. Results emphasize that achieving a 5% statistical precision on the far out-of-field deposited dose requires 200 days of simulations on a single thread. The proposed variance reduction technique led to a 1.4 efficiency gain. This model will be used to generate out-of-field dose maps on a large database of patients that will be used to train a deep learning model for quick dose calculation [1].
    • الدخول الالكتروني :
      https://hal.science/hal-04905648
      https://hal.science/hal-04905648v1/document
      https://hal.science/hal-04905648v1/file/iccr2024_versa_oof.pdf
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.3C6B287F