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• W2800761796 abstract "Hadrontherapy is a method for treating cancer with very targeted dose distributions and enhanced radiobiological effects. To fully exploit these advantages, in vivo range monitoring systems are required. These devices measure, preferably during the treatment, the secondary radiation generated by the beam-tissue interactions. However, since correlation of the secondary radiation distribution with the dose is not straightforward, Monte Carlo (MC) simulations are very important for treatment quality assessment. The INSIDE project constructed an in-beam PET scanner to detect signals generated by the positron-emitting isotopes resulting from projectile-target fragmentation. In addition, a FLUKA-based simulation tool was developed to predict the corresponding reference PET images using a detailed scanner model. The INSIDE in-beam PET was used to monitor two consecutive proton treatment sessions on a patient at the Italian Center for Oncological Hadrontherapy (CNAO). The reconstructed PET images were updated every 10 s providing a near real-time quality assessment. By half-way through the treatment, the statistics of the measured PET images were already significant enough to be compared with the simulations with average differences in the activity range less than 2.5 mm along the beam direction. Without taking into account any preferential direction, differences within 1 mm were found. In this paper, the INSIDE MC simulation tool is described and the results of the first in vivo agreement evaluation are reported. These results have justified a clinical trial, in which the MC simulation tool will be used on a daily basis to study the compliance tolerances between the measured and simulated PET images." @default.
• W2800761796 created "2018-05-17" @default.
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• W2800761796 date "2018-07-01" @default.
• W2800761796 modified "2023-09-26" @default.
• W2800761796 title "Monte Carlo simulation tool for online treatment monitoring in hadrontherapy with in-beam PET: A patient study" @default.
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• W2800761796 doi "https://doi.org/10.1016/j.ejmp.2018.05.002" @default.
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