Monte Carlo (MC) simulations have traditionally been used for single field relative comparisons with experimental data or commercial treatment planning systems (TPS). However, clinical treatment plans commonly involve more than one field. Since the contribution of each field must be accurately quantified, multiple field MC simulations are only possible by employing absolute dosimetry. Therefore, we have developed a rigorous calibration method that allows the incorporation of monitor units (MU) in MC simulations. This absolute dosimetry formalism can be easily implemented by any BEAMnrc/DOSXYZnrc user, and applies to any configuration of open and blocked fields, including intensity-modulated radiation therapy (IMRT) plans. Our approach involves the relationship between the dose scored in the monitor ionization chamber of a radiotherapy linear accelerator (linac), the number of initial particles incident on the target, and the field size. We found that for a 10 x 10 cm2 field of a 6 MV photon beam, 1 MU corresponds, in our model, to 8.129 x 10(13) +/- 1.0% electrons incident on the target and a total dose of 20.87 cGy +/- 1.0% in the monitor chambers of the virtual linac. We present an extensive experimental verification of our MC results for open and intensity-modulated fields, including a dynamic 7-field IMRT plan simulated on the CT data sets of a cylindrical phantom and of a Rando anthropomorphic phantom, which were validated by measurements using ionization chambers and thermoluminescent dosimeters (TLD). Our simulation results are in excellent agreement with experiment, with percentage differences of less than 2%, in general, demonstrating the accuracy of our Monte Carlo absolute dose calculations.

The accuracy and flexibility of the Monte Carlo based RapidArc QA system were demonstrated. Good machine performance and accurate dose distribution delivery of RapidArc plans were observed. The sampling used in the TPS optimization algorithm was found to be adequate.

This work introduces an EGSnrc-based Monte Carlo (MC) beamlet does distribution matrix into a direct aperture optimization (DAO) algorithm for IMRT inverse planning. The technique is referred to as Monte Carlo-direct aperture optimization (MC-DAO). The goal is to assess if the combination of accurate Monte Carlo tissue inhomogeneity modeling and DAO inverse planning will improve the dose accuracy and treatment efficiency for treatment planning. Several authors have shown that the presence of small fields and/or inhomogeneous materials in IMRT treatment fields can cause dose calculation errors for algorithms that are unable to accurately model electronic disequilibrium. This issue may also affect the IMRT optimization process because the dose calculation algorithm may not properly model difficult geometries such as targets close to low-density regions (lung, air etc.). A clinical linear accelerator head is simulated using BEAMnrc (NRC, Canada). A novel in-house algorithm subdivides the resulting phase space into 2.5 X 5.0 mm2 beamlets. Each beamlet is projected onto a patient-specific phantom. The beamlet dose contribution to each voxel in a structure-of-interest is calculated using DOSXYZnrc. The multileaf collimator (MLC) leaf positions are linked to the location of the beamlet does distributions. The MLC shapes are optimized using direct aperture optimization (DAO). A final Monte Carlo calculation with MLC modeling is used to compute the final dose distribution. Monte Carlo simulation can generate accurate beamlet dose distributions for traditionally difficult-to-calculate geometries, particularly for small fields crossing regions of tissue inhomogeneity. The introduction of DAO results in an additional improvement by increasing the treatment delivery efficiency. For the examples presented in this paper the reduction in the total number of monitor units to deliver is approximately 33% compared to fluence-based optimization methods.

We present two new Monte Carlo sources for the DOSXYZnrc code, which can be used to compute dose distributions due to continuously variable beam configurations. These sources support a continuously rotating gantry and collimator, dynamic multileaf collimator (MLC) motion, variable monitor unit (MU) rate, couch rotation and translation in any direction, arbitrary isocentre motion with respect to the patient and variable source-to-axis distance (SAD). These features make them applicable to Monte Carlo simulations for RapidArc, Elekta VMAT, TomoTherapy and CyberKnife. Unique to these sources is the synchronization between the motion in the DOSXYZnrc geometry and the motion within the linac head, represented by a shared library (either a BEAMnrc accelerator with dynamic component modules, or an external library). The simulations are achieved in single runs, with no intermediate phase space files.

The purpose of this study is to present a novel method for generating Monte Carlo 4D dose distributions in a single DOSXYZnrc simulation. During a standard simulation, individual energy deposition events are summed up to generate a 3D dose distribution and their associated temporal information is discarded. This means that in order to determine dose distributions as a function of time, separate simulations would have to be run for each interval of interest. Consequently, it has not been clinically feasible until now to routinely perform Monte Carlo simulations of dose rate, time-resolved dose accumulation, or EPID cine-mode images for VMAT plans. To overcome this limitation, we modified DOSXYZnrc and defined new input and output variables that allow a time-like parameter associated with each particle history to be binned in a user-defined manner. Under the new code version, computation times are the same as for a standard simulation, and the time-integrated 4D dose is identical to the standard 3D dose. We present a comparison of scintillator measurements and Monte Carlo simulations for dose rate during a VMAT beam delivery, a study of dose rate in a VMAT Total Body Irradiation plan, and simulations of transit (through-patient) EPID cine-mode images.