Calculated beam quality correction factors for ionization chambers in MV photon beams.

The beam quality correction factor, [Formula: see text], which corrects for the difference in the ionization chamber response between the reference and clinical beam quality, is an integral part of radiation therapy dosimetry. The uncertainty of [Formula: see text] is one of the most significant sources of uncertainty in the dose determination. To improve the accuracy of available [Formula: see text] data, four partners calculated [Formula: see text] factors for 10 ionization chamber models in linear accelerator beams with accelerator voltages ranging from 6 MV to 25 MV, including flattening-filter-free (FFF) beams. The software used in the calculations were EGSnrc and PENELOPE, and the ICRU report 90 cross section data for water and graphite were included in the simulations. Volume averaging correction factors were calculated to correct for the dose averaging in the chamber cavities. A comparison calculation between partners showed a good agreement, as did comparison with literature. The [Formula: see text] values from TRS-398 were higher than our values for each chamber where data was available. The [Formula: see text] values for the FFF beams did not follow the same [Formula: see text], [Formula: see text] relation as beams with flattening filter (values for 10 MV FFF beams were below fits made to other data on average by 0.3%), although our FFF sources were only for Varian linacs.

Impact of machine log-files uncertainties on the quality assurance of proton pencil beam scanning treatment delivery.

Irradiation log-files store useful information about the plan delivery, and together with independent Monte Carlo dose engine calculations can be used to reduce the time needed for patient-specific quality assurance (PSQA). Nonetheless, machine log-files carry an uncertainty associated to the measurement of the spot position and intensity that can influence the correct evaluation of the quality of the treatment delivery. This work addresses the problem of the inclusion of these uncertainties for the final verification of the treatment delivery. Dedicated measurements performed in an IBA Proteus Plus gantry with a pencil beam scanning (PBS) dedicated nozzle have been carried out to build a 'room-dependent' model of the spot position uncertainties. The model has been obtained through interpolation of the look-up tables describing the systematic and random uncertainties, and it has been tested for a clinical case of a brain cancer patient irradiated in a dry-run. The delivered dose has been compared with the planned dose with the inclusion of the errors obtained applying the model. Our results suggest that the accuracy of the treatment delivery is higher than the spot position uncertainties obtained from the log-file records. The comparison in terms of DVHs shows that the log-reconstructed dose is compatible with the planned dose within the 95% confidence interval obtained applying our model. The initial mean dose difference between the calculated dose to the patient based on the plan and recorded data is around 1%. The difference is essentially due to the log-file uncertainties and it can be removed with a correct treatment of these errors. In conclusion our new PSQA protocol allows for a fast verification of the dose delivered after every treatment fraction through the use of machine log-files and an independent Monte Carlo dose engine. Moreover, the inclusion of log-file uncertainties in the dose calculation allows for a correct evaluation of the quality of the treatment plan delivery.

Proton beam monitor chamber calibration.

The first goal of this paper is to clarify the reference conditions for the reference dosimetry of clinical proton beams. A clear distinction is made between proton beam delivery systems which should be calibrated with a spread-out Bragg peak field and those that should be calibrated with a (pseudo-)monoenergetic proton beam. For the latter, this paper also compares two independent dosimetry techniques to calibrate the beam monitor chambers: absolute dosimetry (of the number of protons exiting the nozzle) with a Faraday cup and reference dosimetry (i.e. determination of the absorbed dose to water under IAEA TRS-398 reference conditions) with an ionization chamber. To compare the two techniques, Monte Carlo simulations were performed to convert dose-to-water to proton fluence. A good agreement was found between the Faraday cup technique and the reference dosimetry with a plane-parallel ionization chamber. The differences-of the order of 3%-were found to be within the uncertainty of the comparison. For cylindrical ionization chambers, however, the agreement was only possible when positioning the effective point of measurement of the chamber at the reference measurement depth-i.e. not complying with IAEA TRS-398 recommendations. In conclusion, for cylindrical ionization chambers, IAEA TRS-398 reference conditions for monoenergetic proton beams led to a systematic error in the determination of the absorbed dose to water, especially relevant for low-energy proton beams. To overcome this problem, the effective point of measurement of cylindrical ionization chambers should be taken into account when positioning the reference point of the chamber. Within the current IAEA TRS-398 recommendations, it seems advisable to use plane-parallel ionization chambers-rather than cylindrical chambers-for the reference dosimetry of pseudo-monoenergetic proton beams.

Spencer-Attix water/medium stopping-power ratios for the dosimetry of proton pencil beams.

This paper uses Monte Carlo simulations to calculate the Spencer-Attix water/medium stopping-power ratios (sw, med) for the dosimetry of scanned proton pencil beams. It includes proton energies from 30 to 350 MeV and typical detection materials such as air (ionization chambers), radiochromic film, gadolinium oxysulfide (scintillating screens), silicon and lithium fluoride. Track-ends and particles heavier than protons were found to have a negligible effect on the water/air stopping-power ratios (sw, air), whereas the mean excitation energy values were found to carry the largest source of uncertainty. The initial energy spread of the beam was found to have a minor influence on the sw, air values in depth. The water/medium stopping-power ratios as a function of depth in water were found to be quite constant for air and radiochromic film-within 2.5%. Also, the sw, med values were found to have no clinically relevant dependence on the radial distance-except for the case of gadolinium oxysulfide and proton radiography beams. In conclusion, the most suitable detection materials for depth-dose measurements in water were found to be air and radiochromic film active layer, although a small correction is still needed to compensate for the different sw, med values between the plateau and the Bragg peak region. Also, all the detection materials studied in this work-except for gadolinium oxysulfide-were found to be suitable for lateral dose profiles and field-specific dose distribution measurements in water.

CloudMC: a cloud computing application for Monte Carlo simulation.

This work presents CloudMC, a cloud computing application-developed in Windows Azure®, the platform of the Microsoft® cloud-for the parallelization of Monte Carlo simulations in a dynamic virtual cluster. CloudMC is a web application designed to be independent of the Monte Carlo code in which the simulations are based-the simulations just need to be of the form: input files → executable → output files. To study the performance of CloudMC in Windows Azure®, Monte Carlo simulations with penelope were performed on different instance (virtual machine) sizes, and for different number of instances. The instance size was found to have no effect on the simulation runtime. It was also found that the decrease in time with the number of instances followed Amdahl's law, with a slight deviation due to the increase in the fraction of non-parallelizable time with increasing number of instances. A simulation that would have required 30 h of CPU on a single instance was completed in 48.6 min when executed on 64 instances in parallel (speedup of 37 ×). Furthermore, the use of cloud computing for parallel computing offers some advantages over conventional clusters: high accessibility, scalability and pay per usage. Therefore, it is strongly believed that cloud computing will play an important role in making Monte Carlo dose calculation a reality in future clinical practice.