MIMC-<i>β</i>: microdosimetric assessment method for internal exposure of<i>β</i>-emitters based on mesh-type cell cluster model.

Abstract

The method combining Monte Carlo (MC) simulation and mesh-type cell models provides a way to accurately assess the cellular dose induced by<i>β</i>-emitters. Although this approach allows for a specific evaluation of various nuclides and cell type combinations, the associated time cost for obtaining results is relatively high. In this work, we propose a Microdosimetric assessment method for Internal exposure of<i>β</i>-emitters based on Mesh-type Cell cluster models (abbreviated as MIMC-<i>β</i>). This approach is applied to evaluate the dose in various types of cells (human bronchial epithelial cells, BEAS-2B; normal human liver cells, L-O2; and normal human small intestine epithelial cells, FHs74Int) exposed to<i>β</i>-emitters. Furthermore, microdosimetric quantity based on the cell cluster model are employed to estimate the relative biological effectiveness (RBE) of<i>β</i>-emitters. The results indicate that this method can accurately and rapidly predict cellular doses caused by different types of<i>β</i>-emitters, significantly mitigating the efficiency challenges associated with directly employing MC to estimate the overall dose of the mesh-type cell cluster model. In comparison with results obtained from direct simulations of uniform administration of<i>β</i>- sources using PHITS for validation, the cellular cluster overall<i>S</i>-values obtained through MIMC-<i>β</i>show discrepancies mostly below 5%, with the minimum deviation reaching 1.35%. Small sampling sizes within the cell nucleus led to larger average lineal energies. In comparison to C-14, the differences in cellular cluster average lineal energy for Cs-134, Cs-137, and I-131 are negligible, resulting in close numerical estimations of RBE based on lineal energy. The MIMC-<i>β</i>can be extended to diverse cell types and<i>β</i>-emitters. Additionally, the RBE assessment based on the cell cluster model offers valuable insights for predicting radiobiological damage resulting from internal exposure by<i>β</i>-emitters. This method is expected to find applicability in various realistic scenarios, including radiation protection and radioligand therapy.