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NEURAL NETWORK MODELLING OF CARDIAC DOSE CONVERSION COEFFICIENT FOR ARBITRARY X-RAY SPECTRA
1Department of Radiological Sciences, College of Applied Medical Sciences, King Saud University, PO Box 10219, Riyadh 11433, Kingdom of Saudi Arabia.
Radiation Protection Dosimetry
|October 31, 2015
Summary
Artificial neural networks (ANNs) efficiently model dose conversion coefficients (DCCs) for the High-Definition Reference Korean-Man (HDRK-Man) computational phantom. This ANN approach offers a significant advancement over time-consuming Monte Carlo simulations for radiation dosimetry.
Area of Science:
- Medical Physics
- Computational Dosimetry
- Radiological Protection
Background:
- Accurate dose conversion coefficients (DCCs) are crucial for radiation dosimetry and radiological protection.
- Traditional Monte Carlo (MC) simulations for DCC calculation are computationally intensive and time-consuming.
- The High-Definition Reference Korean-Man (HDRK-Man) voxel phantom provides a detailed anatomical model for radiation transport studies.
Purpose of the Study:
- To develop and validate an artificial neural network (ANN) approach for computing DCCs using the HDRK-Man phantom.
- To assess the impact of patient size variations on DCC values.
- To compare ANN-based DCC calculations with traditional MC simulations.
Main Methods:
- Implementation of the HDRK-Man voxel phantom within the GEANT4 toolkit for MC simulations.
- Calculation of DCCs for over 30 tissues/organs using monoenergetic photons (15-150 keV).
- Development and application of ANNs to model DCCs, validated against MC results for various body sizes (80-120% magnification).
Main Results:
- Good agreement was observed between ANN calculations and MC simulation results for DCCs.
- ANNs demonstrated efficiency in modeling DCCs for the HDRK-Man phantom across different photon energies and body sizes.
- The study specifically focused on computing DCCs for the human heart.
Conclusions:
- Artificial neural networks provide an efficient and accurate method for calculating dose conversion coefficients.
- The ANN approach represents a significant advancement, reducing the computational burden compared to MC methods.
- This study validates the use of ANNs for dosimetry in computational phantoms, enhancing radiation protection research.
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