Training a deep learning model to predict the anatomy irradiated in fluoroscopic x-ray images.

Journal: International journal of computer assisted radiology and surgery
Published Date: May 26, 2025

Abstract

PURPOSE: Accurate patient dosimetry estimates from fluoroscopically-guided interventions (FGIs) are hindered by limited knowledge of the specific anatomy that was irradiated. Current methods use data reported by the equipment to estimate the patient anatomy exposed during each irradiation event. We propose a deep learning algorithm to automatically match 2D fluoroscopic images with corresponding anatomical regions in computational phantoms, enabling more precise patient dose estimates.

Authors

  • Lunchi Guo
    Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, USA.
  • Dennis Trujillo
    Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63130, USA.
  • James R Duncan
    Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63130, USA.
  • M Allan Thomas
    Department of Imaging Physics, M.D. Anderson Cancer Center, University of Texas, Houston, Texas, USA.

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