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Updated: Sep 24, 2025

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Published on: July 29, 2013
Comparison of organ and effective dose estimations from different Monte Carlo simulation-based software methods in
Michael Lawson1,2, Kemal Berk3, Mohamed Badawy1,4
1Monash Health Imaging, Monash Health, Clayton, Victoria, Australia.
Purpose:
Computational dosimetry software is routinely used to evaluate the organ and effective doses from computed tomography (CT) examinations. Studies have shown a significant variation in dose estimates between software in adult cohorts, and few studies have evaluated software for pediatric dose estimates. This study aims to compare the primary organ and effective doses estimated by four commercially available CT dosimetry software to thermoluminescent dosimeter (TLD) measurements in a 1-year-old phantom.
Methods:
One hundred fifteen calibrated LiF (Mg, Cu, P)-TLD 100-H chips were embedded within an anthropomorphic phantom representing a 1-year-old child at positions that matched the approximate location of organs within an infant. The phantom was scanned under three protocols, each with whole-body coverage. The mean absorbed doses from 25 radiosensitive organs and skeletal tissues were determined from the TLD readings. Effective doses for each of the protocols were subsequently calculated using ICRP 103 formalism. Dose estimates by the four Monte Carlo-based dose calculation systems were determined and compared to the directly measured doses.
Results:
Most organ doses determined by computation dosimetry software aligned to phantom measurements within 20%. Additionally, comparisons between effective doses are calculated using computational and direct measurement methods aligned within 20% across the three protocols. Significant variances were found in bone surface dose estimations among dosimetry methods, likely caused by differences in bone tissue modeling.
Conclusion:
All four-dosimetry software evaluated in this study provide adequate primary organ and effective dose estimations. Users should be aware, however, of the possible estimated uncertainty associated with each of the programs.
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