Application of Monte Carlo Simulation in Treatment Planning for Radiation Oncology

In radiotherapy, the dose delivered to patients needs to be determined before the treatment. Therefore, it is necessary to have an accurate method for predicting the dose distribution. In the past, planning computers calculate radiation dose using data obtained by measurement in a water phantom. This will lead to about 3% to 10% error in the situations where inhomogeneity and lateral electron disequilibrium occur, especially in small field sizes (Jones AO &Das IJ, 2005). For these situations, Monte Carlo Simulation (MCS) has been proposed to give the most accurate solution. The first available MCS code for treatment planning was developed as part of the OMEGA project, which started in the early 90s by the National Research Council of Canada and the University of Wisconsin inMadison. TheMCS code used was called EGS4 and it modelled the transport of photons and electrons. For radiotherapy, the BEAM system (which was based on EGS4) was used for modelling of radiotherapy sources. These days, EGS4 and BEAM have been replaced by EGSnrc and BEAMnrc. MCS models the transport of photon and electron by recording the interactions of each particle until it reaches the preset threshold energy. Therefore, MCS requires an extensive use of computer power. In the early 90s when the processing speed of computers was slow, one simulation would require several days before a result was available. The time required was impractical for clinical purposes. As an alternative, the Convolution algorithm was developed for treatment planning. It calculates the dose delivered to a volume by convolving the interaction sites with the dose deposition kernel derived from the output spectrum of the linear accelerator. The dose deposition kernel is pre-calculated by MCS. Convolution algorithm has improved dose calculation accuracy but it still has limitation compared to real MCS. For example, convolution algorithm uses density scaling to correct for tissue inhomogeneity. This method breaks down in situation where there is a high atomic number material present, e.g. a steel prosthesis. Nowadays, the speed of the computers has increased and parallel processing is more popular. Therefore, it is now feasible to use MCS for treatment planning. Computer Medical System (CMS) has launched a commercial product called Monaco which uses a fast computer and variance reduction techniques to speed up the MCS calculation for radiotherapy treatment planning. MCS can also be used as a quality assurance (QA) tool for other MCS treatment planning systems. It is possible to do so because each MCS utilises a different coding and variance reduction technique therefore each calculation is considered independent. All the work in this chapter is accomplished with a free Monte Carlo Software called MCBEAM which is developed by Fox Chase Cancer Care Centre. MCBEAM originates from 9