Personalized brachytherapy dose reconstruction using deep learning.

Journal: Computers in biology and medicine
Published Date: Sep 1, 2021

Abstract

BACKGROUND AND PURPOSE: Accurate calculation of the absorbed dose delivered to the tumor and normal tissues improves treatment gain factor, which is the major advantage of brachytherapy over external radiation therapy. To address the simplifications of TG-43 assumptions that ignore the dosimetric impact of medium heterogeneities, we proposed a deep learning (DL)-based approach, which improves the accuracy while requiring a reasonable computation time.

Authors

  • Azadeh Akhavanallaf
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
  • Reza Mohammadi
    Department of Operation Management, Amsterdam Business School, University of Amsterdam, Amsterdam, Netherlands.
  • Isaac Shiri
    Biomedical and Health Informatics, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
  • Yazdan Salimi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
  • Hossein Arabi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
  • Habib Zaidi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland. habib.zaidi@hcuge.ch.

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