PHITS simulations of absorbed dose out-of-field and neutron energy spectra for ELEKTA SL25 medical linear accelerator
Journal article, 2015
Monte Carlo (MC) based calculation methods for modeling photon and particle transport, have several potential applications in radiotherapy. An essential requirement for successful radiation therapy is that the discrepancies between dose distributions calculated at the treatment planning stage and those delivered to the patient are minimized. It is also essential to minimize the dose to radiosensitive and critical organs. With MC technique, the dose distributions from both the primary and scattered photons can be calculated. The out-of-field radiation doses are of particular concern when high energy photons are used, since then neutrons are produced both in the accelerator head and inside the patients. Using MC technique, the created photons and particles can be followed and the transport and energy deposition in all the tissues of the patient can be estimated. This is of great importance during pediatric treatments when minimizing the risk for normal healthy tissue, e.g. secondary cancer. The purpose of this work was to evaluate 3D general purpose PHITS MC code efficiency as an alternative approach for photon beam specification. In this study, we developed a model of an ELEKTA SL25 accelerator and used the transport code PHITS for calculating the total absorbed dose and the neutron energy spectra infield and outside the treatment field. This model was validated against measurements performed with bubble detector spectrometers and Boner sphere for 18 MV linacs, including both photons and neutrons. The average absolute difference between the calculated and measured absorbed dose for the out-of-field region was around 11%. Taking into account a simplification for simulated geometry, which does not include any potential scattering materials around, the obtained result is very satisfactorily. A good agreement between the simulated and measured neutron energy spectra was observed while comparing to data found in the literature.