Terrón, J.A. (José Antonio)

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    Peripheral organ equivalent dose estimation procedure in proton therapy
    (2022) Nieto, B. (Beatriz); Nieto-Camero, J.J. (Jaime J.); Domingo, C. (Carles); Sánchez-Doblado, F. (Francisco); Romero-Expósito, M. (Maite); Irazola, L. (Leticia); Terrón, J.A. (José Antonio); Lagares, J.I. (Juan Ignacio); Dasu, A. (Alexandru)
    The aim of this work is to present a reproducible methodology for the evaluation of total equivalent doses in organs during proton therapy facilities. The methodology is based on measuring the dose equivalent in representative locations inside an anthropomorphic phantom where photon and neutron dosimeters were inserted. The Monte Carlo simulation was needed for obtaining neutron energy distribution inside the phantom. The methodology was implemented for a head irradiation case in the passive proton beam of iThemba Labs (South Africa). Thermoluminescent dosimeter (TLD)-600 and TLD-700 pairs were used as dosimeters inside the phantom and GEANT code for simulations. In addition, Bonner sphere spectrometry was performed inside the treatment room to obtain the neutron spectra, some relevant neutron dosimetric quantities per treatment Gy, and a percentual distribution of neutron fluence and ambient dose equivalent in four energy groups, at two locations. The neutron spectrum at one of those locations was also simulated so that a reasonable agreement between simulation and measurement allowed a validation of the simulation. Results showed that the total out-of-field dose equivalent inside the phantom ranged from 1.4 to 0.28 mSv/Gy, mainly due to the neutron contribution and with a small contribution from photons, 10% on average. The order of magnitude of the equivalent dose in organs was similar, displaying a slow reduction in values as the organ is farther from the target volume. These values were in agreement with those found by other authors in other passive beam facilities under similar irradiation and measurement conditions.
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    Monte Carlo verification of radiotherapy treatments with CloudMC
    (2018) Miras, H. (Héctor); Ortiz, A. (Antonio); Jiménez, R. (Rubén); Bertolet, A. (Alejandro); Macías, J. (José); Terrón, J.A. (José Antonio); Perales-Molina, A. (Álvaro)
    Background A new implementation has been made on CloudMC, a cloud-based platform presented in a previous work, in order to provide services for radiotherapy treatment verification by means of Monte Carlo in a fast, easy and economical way. A description of the architecture of the application and the new developments implemented is presented together with the results of the tests carried out to validate its performance. Methods CloudMC has been developed over Microsoft Azure cloud. It is based on a map/reduce implementation for Monte Carlo calculations distribution over a dynamic cluster of virtual machines in order to reduce calculation time. CloudMC has been updated with new methods to read and process the information related to radiotherapy treatment verification: CT image set, treatment plan, structures and dose distribution files in DICOM format. Some tests have been designed in order to determine, for the different tasks, the most suitable type of virtual machines from those available in Azure. Finally, the performance of Monte Carlo verification in CloudMC is studied through three real cases that involve different treatment techniques, linac models and Monte Carlo codes. Results Considering computational and economic factors, D1_v2 and G1 virtual machines were selected as the default type for the Worker Roles and the Reducer Role respectively. Calculation times up to 33 min and costs of 16 € were achieved for the verification cases presented when a statistical uncertainty below 2% (2σ) was required. The costs were reduced to 3–6 € when uncertainty requirements are relaxed to 4%. Conclusions Advantages like high computational power, scalability, easy access and pay-per-usage model, make Monte Carlo cloud-based solutions, like the one presented in this work, an important step forward to solve the long-lived problem of truly introducing the Monte Carlo algorithms in the daily routine of the radiotherapy planning process.