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Fast operating room scattered radiation calculation in x-ray guided interventions by using deep learning.

Hussein Harb1, Mateo Villa1, Didier Benoit1

  • 1LaTIM, University of Brest, INSERM UMR1101, Brest, France.

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Summary
This summary is machine-generated.

This study introduces a novel method using Monte Carlo simulations and deep learning to estimate 3D scattered radiation in real-time, enhancing safety for medical personnel during X-ray procedures.

Keywords:
Monte Carlo simulationdeep learninginterventional radiologyoperating roomscattered x-ray

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Area of Science:

  • Medical Physics
  • Radiological Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Medical personnel face risks from scattered ionizing radiation during X-ray procedures.
  • Accurate real-time monitoring of radiation exposure remains a challenge due to the invisible nature of X-rays and limitations of current dosimeters.

Purpose of the Study:

  • To develop and validate a real-time system for estimating three-dimensional (3D) scattered radiation in operating rooms.
  • To improve the safety of healthcare professionals by providing accurate radiation exposure maps.

Main Methods:

  • Combined Monte Carlo (MC) simulations with deep learning (DL) to create a neural network.
  • Trained the neural network to predict radiation maps based on patient morphology and imaging parameters.
  • Utilized modern GPUs for efficient computation of 3D radiation maps.

Main Results:

  • The system achieved real-time computation of 3D radiation maps in 11 ms.
  • Validation showed a mean absolute percentage error of 10.97% compared to MC simulations.
  • Computed organ doses with a global average error of 8.2 ± 4.1%.

Conclusions:

  • The integration of MC simulations and DL offers a powerful and efficient solution for real-time scattered radiation estimation.
  • This approach significantly enhances the ability to protect medical personnel from harmful radiation during interventional procedures.