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A Data-Driven Fragmentation Model for Carbon Therapy GPU-Accelerated Monte-Carlo Dose Recalculation

Authors :
Micol De Simoni
Giuseppe Battistoni
Angelica De Gregorio
Patrizia De Maria
Marta Fischetti
Gaia Franciosini
Michela Marafini
Vincenzo Patera
Alessio Sarti
Marco Toppi
Giacomo Traini
Antonio Trigilio
Angelo Schiavi
Source :
Frontiers in Oncology, Vol 12 (2022)
Publication Year :
2022
Publisher :
Frontiers Media S.A., 2022.

Abstract

The advent of Graphics Processing Units (GPU) has prompted the development of Monte Carlo (MC) algorithms that can significantly reduce the simulation time with respect to standard MC algorithms based on Central Processing Unit (CPU) hardware. The possibility to evaluate a complete treatment plan within minutes, instead of hours, paves the way for many clinical applications where the time-factor is important. FRED (Fast paRticle thErapy Dose evaluator) is a software that exploits the GPU power to recalculate and optimise ion beam treatment plans. The main goal when developing the FRED physics model was to balance accuracy, calculation time and GPU execution guidelines. Nowadays, FRED is already used as a quality assurance tool in Maastricht and Krakow proton clinical centers and as a research tool in several clinical and research centers across Europe. Lately the core software has been updated including a model of carbon ions interactions with matter. The implementation is phenomenological and based on carbon fragmentation data currently available. The model has been tested against the MC FLUKA software, commonly used in particle therapy, and a good agreement was found. In this paper, the new FRED data-driven model for carbon ion fragmentation will be presented together with the validation tests against the FLUKA MC software. The results will be discussed in the context of FRED clinical applications to 12C ions treatment planning.

Details

Language :
English
ISSN :
2234943X
Volume :
12
Database :
Directory of Open Access Journals
Journal :
Frontiers in Oncology
Publication Type :
Academic Journal
Accession number :
edsdoj.9b3a7dc585d042dda280cc2e1648133c
Document Type :
article
Full Text :
https://doi.org/10.3389/fonc.2022.780784