Proton therapy dose is affected by relative biological effectiveness differently than X-ray therapies. The current clinically accepted weighting factor is 1.1 at all positions along the depth-dose profile. However, the relative biological effectiveness correlates with the linear energy transfer, cell or tissue type, and the dose per fraction causing variation of relative biological effectiveness along the depth-dose profile. In this article, we present a simple relative biological effectiveness-weighted treatment planning risk assessment algorithm in 2-dimensions and compare the results with those derived using the standard relative biological effectiveness of 1.1. The isodose distribution profiles for beams were accomplished using matrices that represent coplanar intersecting beams. These matrices were combined and contoured using MATLAB to achieve the distribution of dose. There are some important differences in dose distribution between the dose profiles resulting from the use of relative biological effectiveness ¼ 1.1 and the empirically derived depth-dependent values of relative biological effectiveness. Significant hot spots of up to twice the intended dose are indicated in some beam configurations. This simple and rapid risk analysis could quickly evaluate the safety of various dose delivery schema.

The determination of x-ray spectra near the maze entrance of linear accelerator (LINAC) rooms is challenging due to the pulsed nature of the LINAC source. Mathematical methods to account for pulse pileup have been examined. These methods utilize the highly periodic pulsing structure of the LINAC, differing from the effects of high-intensity radioactive sources. Methods: Sodium iodide (NaI) and plastic scintillation detectors were used to determine the energy spectra at different points near the maze entrance of a medical LINAC. Monte Carlo calculations of the energy distribution of scattered photons were used to simulate the energy spectrum at the maze entrance. The proposed algorithm uses the Monte Carlo code, FLUKA, to calculate a response function for both detectors. To determine the effects of the pileup in the spectra, the Poisson distribution was used, employing the average number of photons per pulse (l) interacting with the detector. The quantity, l, was obtained from the ratio of the number of events detected to the number of pulses delivered. The energy spectra at various distances from the maze entrance were measured using NaI and plastic scintillation detectors. From these measurements, the values of µ were calculated, and the pileup probability was determined. The FLUKA Monte Carlo code was used to calculate the spectrum at the maze entrance and the response matrices of the NaI and plastic scintillation detectors. The algorithm based on the Poisson distribution was applied to calculate the spectrum. Results: The agreement between the calculated and measured spectra was within the first standard deviation of the variance expected in µ. This agreement confirms that photons at the maze entrance have energies between 30 and 240 keV for a maze with three turns, with an average energy of around 85 keV. After pileup correction, the range of the pulse height distribution with the plastic scintillation detector, which has a low atomic number, was decreased (0 to 140 keV). In contrast, the range of the pulse height distribution with the NaI scintillation detector was closer to the photon spectrum (0 to 240 keV). Conclusions: The corrected spectrum demonstrates that using a FLUKA Monte Carlo code and an algorithm based on the Poisson distribution are effective methods in removing the distortion due to the pileup in LINAC spectra when measuring with NaI and plastic scintillation detectors. The agreement between the corrected and measured spectra indicates that Monte Carlo modeling can accurately determine the spectrum of a LINAC machine at the maze entrance.

There is an increased interest in determining the photon reflection coefficient for layered systems consisting of lead (Pb) and concrete. The generation of accurate reflection coefficient data has implications for many fields, especially radiation protection, industry, and radiotherapy room design. Therefore, this study aims to calculate the reflection coefficients of photons for various lead thicknesses covering the concrete. This new data for lead, layered over concrete, supports various applications, such as an improved design of the mazes used for radiotherapy rooms, which helps to reduce cost and space requirements. The FLUKA Monte Carlo code was used to calculate photon reflection coefficients for a concrete wall with different energies. The reflection coefficient was also calculated for a concrete wall covered by varying thicknesses of lead to study the effect of lining this metal on the concrete wall. The concrete's reflection coefficient data were compared to internationally published data and showed that Monte Carlo calculations differed significantly from some of the extrapolated data. The absorbed dose of backscattered photons for various thicknesses of lead covering the ordinary concrete has been tabulated as a function of the reflection angle. Also, the reflection coefficient as a function of the Pb thicknesses covering the ordinary concrete has been figured to study the dose reduction factor. The generation of accurate data for reflection coefficients is vital for many fields, especially for radiation protection and radiotherapy room design. The new data have been presented for lead layered over concrete in various applications, such as an improvement in the design of the mazes used for radiotherapy rooms, thereby reducing the cost and space requirements. In addition, the Monte Carlo method enables calculating the energy distribution of reflected photons, and these were shown for a range of angles.