A Lightweight Python-based Monte Carlo Framework for Visualizing Energy-dependent Dose Calibrator Response: A Nuclear Medicine Education Tool
Anil Kumar Pandey, Shanshila Rayamajhi, Jagrati Chaudhary, Param Dev Sharma, Rakesh KumarAbstract
Purpose:
Trainees often have limited understanding of radionuclide dose-calibrator physics because access to radioactive materials is restricted by radiation-safety requirements, and the complexity of established Monte Carlo (MC) platforms can present a barrier to early learning. We present a lightweight, Python-based MC simulation framework designed to visualize energy-dependent photon transport and ionization-chamber response for nuclear medicine education.
Materials and Methods:
A spherical gas-filled ionization chamber (radius 5 cm) was modelled. Photon free-path lengths were sampled from exponential attenuation distributions; deposited energy was assigned via a Beta-distributed stochastic transfer fraction (biased toward partial deposition). Ionization yield was estimated assuming 33.97 eV is required to produce one ion pair. Three clinically representative radionuclides – 99m Tc (140 keV, μ = 0.030 cm −1 ), 131 I (364 keV, μ = 0.015 cm −1 ), and 18 F (511 keV, μ = 0.011 cm −1 ) – were each simulated with 100,000 independent photon histories.
Results:
Intrinsic efficiency decreased monotonically with photon energy: 13.65% ( 99m Tc), 7.01% ( 131 I), and 5.25% ( 18 F). Median deposited energy increased from 37.16 keV to 95.87 keV and 132.07 keV; mean ion-pair yield rose from 1180, 3046, and 4254. Detector response increased nonlinearly from 161 to 214 and 223 ion pairs per emitted photon. All trends were consistent with exponential attenuation theory.
Conclusions:
The framework reproduces first-order ionization-chamber physics without specialist software or radioactive sources. Its transparency, rapid execution, and interactive visualizations make it well suited for classroom demonstrations and self-directed nuclear medicine physics training, while its explicit limitations underscore the gap between educational modelling and clinical-grade Monte Carlo simulation.