Properties of different scintillating fibers were examined and compared, as a part of the design optimization of the SiFi-CC detector, currently under development for proton therapy monitoring. Three scintillating materials were considered as candidates to constitute the active part of the detector: LYSO:Ce, LuAG:Ce and GAGG:Ce. All investigated samples had an elongated, fiber-like shape and were read out on both ends using silicon photomultipliers (SiPMs). Samples of LYSO:Ce material provided by four different manufacturers were included in the survey. Additionally, different types of optical coupling media, wrapping and coating materials were investigated. The following properties of the scintillating fibers were determined: attenuation length, position-, energy-, timing resolution and light collection. Two models were used to describe the propagation of scintillating light in the fiber and quantify the light attenuation: exponential light attenuation model (ELA) and exponential light attenuation model with light reflection (ELAR). Energy and position reconstruction were also performed using the two above methods. It was shown, that the ELAR model performs better in terms of description of the light attenuation process. However, energy and position reconstruction results are comparable for the two proposed methods. Based on the results of measurements with scintillating fibers in different configurations we concluded that LYSO:Ce fibers wrapped in Al foil (bright side facing towards the fiber) provided the best trade-off between the energy- (8.56% at 511 keV) and position (32 mm) resolutions and thus will be the optimal choice for the SiFi-CC detector. Additionally, the study of different optical coupling media showed, that the silicone pads coupling ensures good stability of the system performance and parameters.
Objective. Online monitoring of dose distribution in proton therapy is currently being investigated with the detection of prompt gamma (PG) radiation emitted from a patient during irradiation. The SiPM and scintillation Fiber based Compton Camera (SiFi-CC) setup is being developed for this aim. Approach. A machine learning approach to recognize Compton events is proposed, reconstructing the PG emission profile during proton therapy. The proposed method was verified on pseudo-data generated by a Geant4 simulation for a single proton beam impinging on a polymethyl methacrylate (PMMA) phantom. Three different models including the boosted decision tree (BDT), multilayer perceptron (MLP) neural network, and k-nearest neighbour (k-NN) were trained using 10-fold cross-validation and then their performances were assessed using the receiver operating characteristic (ROC) curves. Subsequently, after event selection by the most robust model, a software based on the List-Mode Maximum Likelihood Estimation Maximization (LM-MLEM) algorithm was applied for the reconstruction of the PG emission distribution profile. Main results. It was demonstrated that the BDT model excels in signal/background separation compared to the other two. Furthermore, the reconstructed PG vertex distribution after event selection showed a significant improvement in distal falloff position determination. Significance. A highly satisfactory agreement between the reconstructed distal edge position and that of the simulated Compton events was achieved. It was also shown that a position resolution of 3.5 mm full width at half maximum (FWHM) in distal edge position determination is feasible with the proposed setup.
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