Speaker
Description
Purpose: The overarching goal is to develop a novel microdosimetry system for investigating energy deposition and cellular responses to low-dose radiation using Raman spectroscopy (RS) and Monte Carlo (MC) simulations. The present work focuses on establishing the MC simulation framework used to quantify stochastic energy deposition within cells and at the cell-detector interface.
Methods: MC38 murine cancer cells are cultured directly on the radiochromic film (RCF), forming an integrated cell-detector system for simultaneous assessment of cellular dose deposition and biological response. Each plate containing the cells-RCF setup is irradiated using a 6-MV clinical linac photon beam with doses ranging from 0-500 mGy. MC simulations of radiation transport and energy deposition are performed using EGSnrc (egs_brachy) to replicate the experimental irradiations. Specific energy (z = energy/mass) is quantified within (i) individual cell nuclei (ii) RS sampling volumes ($6~\mu\text{m}^3$, 3×3 grid) within the nucleus, and (iii) the active layer of the RCF to investigate cell-detector dose correlations.
Results: MC-derived specific energy distributions reveal pronounced stochastic variation in energy deposition in cells at low doses. The relative standard deviation of specific energy ($\sigma_z$/ z̄) within cell nuclei decreases with increasing dose, from 52% at 3.5 mGy to 4% at 500 mGy, reflecting reduced variation in energy deposition at higher doses. Comparison of the mean specific energy (z̄) deposited in cell nuclei (n=189) with that deposited in the RCF active layer directly beneath the cells shows close agreement, with differences <2% across all doses. The mean specific energy within RS-relevant sampling volumes also agrees with that in the RCF within 2% across the full dose range.
Conclusion: MC simulations show close agreement between energy deposited in cell nuclei and underlying RCF validating the integrated cell-RCF system for microdosimetry. Ongoing work includes RS analysis of irradiated cells, complementary cell viability and toxicity assays, and the application of machine learning methods to directly correlate MC-derived microdosimetric quantities with radiation-induced biochemical changes measured by RS.
| Keyword-1 | Microdosimetry |
|---|---|
| Keyword-2 | Monte-Carlo simulations |
| Keyword-3 | Low-dose radiation response |