Accurate radiation dosimetry demands that all information relating to the modelled system be used, including a quantification of its limitations.
McMC bridges the gap between detailed MC simulations and traditionally dosimetry techniques by enabling a means to incorporate empirical information in a Monte Carlo calculation.
Other possibilities for Markov chains in radiation dosimetry include correlated and anti-thetic sampling of known distributions and more efficient sampling of complex distributions.
The techniques used to investigate energy resolution limits in electron dosimetry are an appropriate technique for optimisation in imaging.
Finally, the potential for McMC as an inverse Monte Carlo method in inverse planning is of great interest.