Abstract

Dosimetric modeling of radiation transport in skeletal bone tissues using computational phantoms provides the doses of internal exposure to active marrow. Computational phantoms of ICRP are created for reference people with anatomical and physiological characteristics typical of an average individual. The doses calculated with such phantoms will correspond to certain population-average values. Individual variability will introduce a stochastic component of uncertainty into the dose estimation. The objective of this study is to assess the influence of variability of chemical composition and bone density on the results of dosimetric modeling. The phantoms are represented by simple geometry figures filled with trabecular structures and bone marrow and covered with a cortical layer. Radiation transport was simulated using the Monte Carlo method. The dose factors to convert the radionuclide activity concentration to absorbed dose rates in active marrow were calculated assuming uniform radionuclide distribution in the volume of the trabecular and cortical bone. As a result of the numerical experiments, it has been shown that variations in chemical composition do not introduce an error of more than ± 4% into dosimetric modeling. The effect of bone density on active marrow dose formation depends on the size of a phantom. For computational phantoms with linear dimensions exceeding two electron free path lengths (~ 0.44 cm), variability of bone density within ± 3% leads to a similar relative uncertainty of the dose conversion factor. However, for smaller phantoms, bone density variability leads to uncertainties of 6% or 13% for a source deposited in the trabecular or cortical bone, respectively. The results obtained will be used to assess the uncertainty of bone marrow dosimetry, taking into account the uncertainty of all parameters including the variability of morphometric characteristics of bones, the variability of the active marrow distribution in skeletal sites, as well as the uncertainties introduced by model approximations.

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