Technical note: Evaluation of deep learning based synthetic CTs clinical readiness for dose and NTCP driven head and neck adaptive proton therapy.

Journal: Medical physics
PMID: 37831597

Abstract

BACKGROUND: Adaptive proton therapy workflows rely on accurate imaging throughout the treatment course. Our centre currently utilizes weekly repeat CTs (rCTs) for treatment monitoring and plan adaptations. However, deep learning-based methods have recently shown to successfully correct CBCT images, which suffer from severe imaging artifacts, and generate high quality synthetic CT (sCT) images which enable CBCT-based proton dose calculations.

Authors

  • Rutger J C de Koster
    Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  • Adrian Thummerer
    Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  • Daniel Scandurra
    Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  • Johannes A Langendijk
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
  • Stefan Both
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.

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