Monte Carlo simulations of a nozzle for the treatment of ocular tumours with high-energy proton beams.

Newhauser W, Koch N, Hummel S, Ziegler M, Titt U
Phys Med Biol. 2005 50 (22): 5229-49

PMID: 16264250 · DOI:10.1088/0031-9155/50/22/002

By the end of 2002, 33 398 patients worldwide had been treated with proton radiotherapy, 10 829 for eye diseases. The dose prediction algorithms used today for ocular proton therapy treatment planning rely on parameterizations of measured proton dose distributions, i.e., broad-beam and pencil-beam techniques, whose predictive capabilities are inherently limited by severe approximations and simplifications in modelling the radiation transport physics. In contrast, the Monte Carlo radiation transport technique can, in principle, provide accurate predictions of the proton treatment beams by taking into account all the physical processes involved, including coulombic energy loss, energy straggling, multiple Coulomb scattering, elastic and nonelastic nuclear interactions, and the transport of secondary particles. It has not been shown, however, whether it is possible to commission a proton treatment planning system by using data exclusively from Monte Carlo simulations of the treatment apparatus and a phantom. In this work, we made benchmark comparisons between Monte Carlo predictions and measurements of an ocular proton treatment beamline. The maximum differences between absorbed dose profiles from simulations and measurements were 6% and 0.6 mm, while typical differences were less than 2% and 0.2 mm. The computation time for the entire virtual commissioning process is less than one day. The study revealed that, after a significant development effort, a Monte Carlo model of a proton therapy apparatus is sufficiently accurate and fast for commissioning a treatment planning system.

MeSH Terms (11)

Computer Simulation Dose-Response Relationship, Radiation Eye Neoplasms Humans Monte Carlo Method Particle Accelerators Phantoms, Imaging Protons Radiation Dosage Radiotherapy, High-Energy Radiotherapy Planning, Computer-Assisted

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