Design and characterisation of a real time proton and carbon ion radiography system based on scintillating optical fibres.
Phys Med
; 32(9): 1124-34, 2016 Sep.
Article
em En
| MEDLINE
| ID: mdl-27575280
ABSTRACT
This paper describes the design and characterization of a charged particle imaging system composed of a position sensitive detector and residual range detector. The position detector consists of two identical overlying and orthogonal planes each of which consists of two layers of pre-aligned and juxtaposed scintillating fibres. The 500µm square section fibres are optically coupled to two Silicon Photomultiplier arrays using a channel reduction system patented by the Istituto Nazionale di Fisica Nucleare. The residual range detector consists of sixty parallel layers of the same fibres used in the position detector each of which is optically coupled to a Silicon Photomultiplier array by wavelength shifting fibres. The sensitive area of the two detectors is 9×9cm(2). Characterising the position sensitive and the residual range detectors to reconstruct the radiography, is fundamental to validating the detectors' designs. The proton radiography of a calibrated target in imaging conditions is presented. The spatial resolution of the position sensitive detector is about 150µm and the range resolution is about 170µm. The performance of the prototypes were tested at CATANA proton therapy facility (Laboratori Nazionali del Sud, INFN, Catania) with energy up to 58MeV and rate of about 10(6) particles per second. The comparison between the simulations and measurements confirms the validity of this system.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Contexto em Saúde:
1_ASSA2030
Problema de saúde:
1_financiamento_saude
Assunto principal:
Carbono
/
Radiografia
/
Fibras Ópticas
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Terapia com Prótons
/
Íons
/
Neoplasias
Tipo de estudo:
Diagnostic_studies
/
Health_economic_evaluation
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Phys Med
Assunto da revista:
BIOFISICA
/
BIOLOGIA
/
MEDICINA
Ano de publicação:
2016
Tipo de documento:
Article