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Fast 3D whole-body occupational dose estimation in interventional radiology using physics-informed deep learning.

Hussein Harb1, Didier Benoit2, Chi-Hieu Pham2

  • 1LaTIM, INSERM UMR1101, University of Brest, 29200, Brest, France. hussein.harb@univ-brest.fr.

Radiological Physics and Technology
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning framework to accurately predict 3D radiation dose distributions for physicians in interventional radiology, improving occupational radiation protection.

Keywords:
Interventional radiologyMonte Carlo simulationOccupational dosimetryPhysics-informed deep learningRadiation protection

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Area of Science:

  • Medical Physics
  • Radiology
  • Artificial Intelligence

Background:

  • Occupational radiation exposure in interventional radiology is complex and not well-measured by current methods.
  • Traditional dosimetry struggles with the spatially varying nature of scattered radiation.

Purpose of the Study:

  • To develop a fast, physics-informed deep learning framework for predicting 3D physician dose distributions.
  • To overcome limitations of conventional dosimetry in interventional radiology.

Main Methods:

  • Utilized GPU-accelerated Monte Carlo simulations to generate 3D dose maps.
  • Trained residual and transformer-based 3D U-Net architectures on simulated data.
  • Evaluated model performance using error metrics, gamma analysis, and personal dose equivalents.

Main Results:

  • The residual 3D U-Net model achieved high accuracy with mean absolute errors < 0.06 nGy and >90% gamma passing rates.
  • Predicted personal dose equivalents closely matched Monte Carlo references.
  • Demonstrated anatomically resolved dose estimation for various tissues.

Conclusions:

  • The proposed framework enables rapid, accurate, and detailed occupational dose estimation.
  • This technology can enhance real-time radiation awareness and guidance systems.
  • Potential for significant improvements in operator radiation protection in interventional procedures.