Monte Carlo simulations in quality assurance of dosimetry and clinical dose calculations in radiotherapy

The status of radiotherapy as an important treatment modality for cancer is indisputable. In external beam radiotherapy, usually delivered with linear accelerators (linacs), there is a total uncertainty involved in the treatment process, in which the accuracy of the dose calculation is a significant factor. In patient dose calculation, the radiation beam produced by the linac is modelled and delivered to the calculation phantom, which is based on computed tomography (CT) datasets. Most of the clinical dose calculation algorithms implemented in treatment planning systems (TPSs) have been based on analytical or semi-analytical principles, but statistical Monte Carlo (MC) methods have been shown to provide the most accurate representation of dose distributions in the patient and other calculation phantoms. However, long calculation times have prohibited the implementation of full MC methods to clinical patient dose calculation. In this study, the aim was to develop a full MC-based dose calculation tool to serve as a reference method for TPS dose calculation algorithm benchmarking, but also for dosimetry purposes. The MC-based model constructed for both photon and electron beams was first benchmarked against measurements in water. Finally, the value of the absolute dose calibrated MC model was assessed by applying it to specific problems in dosimetry and dose calculations. The performance of the MC model in this study in a water phantom was shown to be equal or better than that reported in other studies. During the stage in which the multileaf collimator (MLC) part of the MC model was benchmarked, the MC-based results were used to assess the performance of various measurement detectors in small aperture dosimetry. Eventually, the MC model was shown to provide reference dose distributions both in virtual and CT-based phantom geometries, where accurate measurements are difficult or impossible to perform. With photon beams, the MC model was used to benchmark the TPS algorithms in cases where large uncertainties have been reported, i.e. in the stereotactic body radiotherapy (SBRT) of the lung and in the presence of high atomic number material as a metallic hip implant. With electron beams, the MC model was applied to assess the accuracy of the TPS algorithms in chest wall radiotherapy. With the described use, in addition to performed TPS configuration data validation, the MC model has the potential to have a positive influence on the total uncertainty involved in radiotherapy. Furthermore, the MC model can be used in the development of new treatment techniques, protocols and detectors for dosimetry and dose calculation algorithms. The time when full MC-based calculations are implemented into clinical treatment planning is yet to come