Comparative analysis evaluates gamma radiation shielding in medical concrete barriers, suggesting optimized methods for improved design accuracy.
This study compares computational approaches for gamma radiation shielding design in medical concrete barriers, focusing on high-activity Cobalt-60 (Co-60) teletherapy sources—still relevant in resource-limited settings despite the global shift to linear accelerators. An open-source Python-based Monte Carlo framework was developed to model photon transport through standard concrete (density 2.35 g/cm³), incorporating NIST XCOM energy-dependent cross-sections, explicit dual gamma emissions of Co-60 (1.173 MeV and 1.332 MeV), material composition effects, and buildup factors using ANSI/ANS-6.4.3 Berger parameters. The framework was validated against NIST XCOM data (agreement <3% at Co-60 energies), narrow-beam Beer-Lambert law (<2% deviation), and OpenMC benchmarks using the ENDF/B-VIII.0 nuclear data library (agreement within 1.2–8%, ~7.4% difference at 195 cm). Compared to the conservative NCRP Report 49 regulatory method—which employs simplified assumptions and built-in safety margins for compliance—the Monte Carlo approach required 195 cm of concrete for a representative 10,000 Ci Co-60 source at 5 m from an occupied area (dose constraint 0.1 μSv/h), versus 147 cm by NCRP 49—a 33% difference. This arises mainly from explicit dual-energy modeling (~15%), detailed buildup inclusion in thick shields (~65%), and precise cross-sections (~20%). While NCRP 49 (and successor NCRP 151) provides practical, conservative tools for routine shielding design, Monte Carlo simulations deliver enhanced physical accuracy for thick barriers and high-activity sources. The findings advocate complementary use: regulatory methods for compliance documentation and Monte Carlo for design optimization. This work offers an accessible open-source Python tool and evidence-based recommendations for clinical shielding workflows, while noting Co-60’s ongoing relevance in select global contexts (IAEA DIRAC data).
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Oo et al. (2026) studied this question.
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