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Published on: April 24, 2020
A GATE Monte Carlo study on ICRP110 phantoms for BNCT dosimetry evaluation
Daniele Pistone1, Silva Bortolussi2, Setareh Fatemi3
1Università degli Studi della Campania "Luigi Vanvitelli", Dipartimento di Matematica e Fisica, Caserta, Italy; Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Napoli, Napoli, Italy.
None:
Boron Neutron Capture Therapy (BNCT) is attracting renewed attention due to advancements in compact proton accelerators for the production of neutron beams, and new BNCT facilities are being planned all around the world. A key aspect in BNCT treatments will be patient dosimetry, particularly given the complex radiation field created by neutron interactions with biological tissues. This study aimed at developing a prototype of BNCT dosimetry workflow based on Monte Carlo (MC) simulations for the GATE toolkit. Investigating the feasibility of performing voxel-level dosimetry through full MC transport in terms of simulation time and statistical uncertainties, the ICRP110 male and female adult voxelized phantoms were used to model the human body, adding the possibility to set in their organs user-defined concentrations of 10B. Irradiation simulations of the head district with two monoenergetic neutron beams and with a realistic clinical neutron spectrum were carried out. The absorbed dose matrices for each simulation, assuming both no 10B and then a systemic distribution of 15 ppm, were scored separating the contributions from 7Li, alpha particles, protons and photons. Results showed the expected increase, in presence of 10B distribution, of the 7Li and alpha average dose components in organs of interest of the head, such as brain, reaching in it about 1.3 and 2.3 fGy/evt, respectively, in presence of 15 ppm of 10B. The present prototype of dosimetric workflow, whose macros and files are freely shared for interested users and developers, will serve as a basis for future studies aiming at simulating similar BNCT scenarios with larger statistics, for example by exploiting high computing resources, to verify the obtained results with lower statistical uncertainties and possibly optimize the workflow to reduce simulation times while ensuring suitable dosimetric accuracy.
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