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.

A Data-Driven Fragmentation Model for Carbon Therapy GPU-Accelerated Monte-Carlo Dose Recalculation / De Simoni, M.; Battistoni, G.; De Gregorio, A.; De Maria, P.; Fischetti, M.; Franciosini, G.; Marafini, M.; Patera, V.; Sarti, A.; Toppi, M.; Traini, G.; Trigilio, A.; Schiavi, A.. - In: FRONTIERS IN ONCOLOGY. - ISSN 2234-943X. - 12:(2022). [10.3389/fonc.2022.780784]

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

De Simoni M.;De Gregorio A.;De Maria P.;Fischetti M.;Franciosini G.;Patera V.;Sarti A.;Toppi M.;Trigilio A.;Schiavi 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.
2022
carbon ion (C12); fast MC; fragmentation; graphics processing unit (GPU); hadrontherapy; quality assurance (QA)
01 Pubblicazione su rivista::01a Articolo in rivista
A Data-Driven Fragmentation Model for Carbon Therapy GPU-Accelerated Monte-Carlo Dose Recalculation / De Simoni, M.; Battistoni, G.; De Gregorio, A.; De Maria, P.; Fischetti, M.; Franciosini, G.; Marafini, M.; Patera, V.; Sarti, A.; Toppi, M.; Traini, G.; Trigilio, A.; Schiavi, A.. - In: FRONTIERS IN ONCOLOGY. - ISSN 2234-943X. - 12:(2022). [10.3389/fonc.2022.780784]
File allegati a questo prodotto
File Dimensione Formato  
De Simoni_A Data-Driven_2022.pdf

accesso aperto

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 5.99 MB
Formato Adobe PDF
5.99 MB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1640304
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 2
social impact