The Monte Carlo method is generally considered an accurate method for predicting radiation dose distributions for planning radiation treatments.  In particular, for large numbers of source radiation particles (typically above 107), the Monte Carlo method typically produces an accurate representation of the dose distribution.  For these reasons, the Monte Carlo method is typically preferred for the calculation of radiation dose in radiotherapy. 
Unfortunately, the Monte Carlo method generally requires a large number of computations to generate a sufficient number of data points to provide an accurate representation of the resulting dose distribution in a patient.  That is, the Monte Carlo method has no well-defined preset: “finish” time and a typical simulation results in dose distributions being continually calculated until the noise level falls below a level deemed acceptable by the user. 
In some cases, radiotherapy treatment planners may wish to compare many dose distributions before selecting a final distribution for treatment.  Therefore, there exists a need for dose modeling which is as accurate as the Monte Carlo method but which has greater computational efficiency than the Monte Carlo method.