Proton therapy is nowadays a major clinical modality in the fight against cancer due to the advantages offered by its peculiar depth dose profile, that allows to improve its efficacy on tumors while reducing damages to healthy tissues. The number of worldwide facilities and of treated patients is increasing every year. A challenge of proton therapy is that treatment planning systems require accurate dose computation, but the golden standard in accuracy are Monte Carlo algorithms that are slow. For this reason, accurate and faster dose calculation algorithms are needed. In this paper we use Deep Learning to achieve both speed and accuracy in dose calculation for proton therapy. The results positively compare with previous existing literature in the thorax cases, that are usually the most difficult to calculate by fast algorithms.

Using Deep Learning for Fast Dose Refinement in Proton Therapy

Spezialetti, Matteo
;
Caianiello, Pasquale;Placidi, Giuseppe;Mignosi, Filippo
2021

Abstract

Proton therapy is nowadays a major clinical modality in the fight against cancer due to the advantages offered by its peculiar depth dose profile, that allows to improve its efficacy on tumors while reducing damages to healthy tissues. The number of worldwide facilities and of treated patients is increasing every year. A challenge of proton therapy is that treatment planning systems require accurate dose computation, but the golden standard in accuracy are Monte Carlo algorithms that are slow. For this reason, accurate and faster dose calculation algorithms are needed. In this paper we use Deep Learning to achieve both speed and accuracy in dose calculation for proton therapy. The results positively compare with previous existing literature in the thorax cases, that are usually the most difficult to calculate by fast algorithms.
978-1-6654-4207-7
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/178198
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