|
|
Muñoz, E., Barrio, J., Bemmerer, D., Etxebeste, A., Fiedler, F., Hueso-Gonzalez, F., et al. (2018). Tests of MACACO Compton telescope with 4.44 MeV gamma rays. J. Instrum., 13, P05007–13pp.
Abstract: Hadron therapy offers the possibility of delivering a large amount of radiation dose to tumors with minimal absorption by the surrounding healthy tissue. In order to fully exploit the advantages of this technique, the use of real-time beam monitoring devices becomes mandatory. Compton imaging devices can be employed to map the distribution of prompt gamma emission during the treatment and thus assess its correct delivery. The Compton telescope prototype developed at IFIC-Valencia for this purpose is made of three layers of LaBr3 crystals coupled to silicon photomultipliers. The system has been tested in a 4.44 MeV gamma field at the 3 MV Tandetron accelerator at HZDR, Dresden. Images of the target with the system in three different positions separated by 10 mm were successfully reconstructed. This indicates the ability of MACACO for imaging the prompt gamma rays emitted at such energies.
Keywords: Compton imaging; Instrumentation for hadron therapy; Gamma detectors (scintillators, CZT, HPG, HgI etc); Photon detectors for UV, visible and IR photons (solid state) (PIN diodes, APDs, Si PMTs, G APDs, CCDs, EBCCDs, EMCCDs etc)
|
|
|
|
Muñoz, E., Barrio, J., Bernabeu, J., Etxebeste, A., Lacasta, C., Llosa, G., et al. (2018). Study and comparison of different sensitivity models for a two-plane Compton camera. Phys. Med. Biol., 63(13), 135004–19pp.
Abstract: Given the strong variations in the sensitivity of Compton cameras for the detection of events originating from different points in the field of view (FoV), sensitivity correction is often necessary in Compton image reconstruction. Several approaches for the calculation of the sensitivity matrix have been proposed in the literature. While most of these models are easily implemented and can be useful in many cases, they usually assume high angular coverage over the scattered photon, which is not the case for our prototype. In this work, we have derived an analytical model that allows us to calculate a detailed sensitivity matrix, which has been compared to other sensitivity models in the literature. Specifically, the proposed model describes the probability of measuring a useful event in a two-plane Compton camera, including the most relevant physical processes involved. The model has been used to obtain an expression for the system and sensitivity matrices for iterative image reconstruction. These matrices have been validated taking Monte Carlo simulations as a reference. In order to study the impact of the sensitivity, images reconstructed with our sensitivity model and with other models have been compared. Images have been reconstructed from several simulated sources, including point-like sources and extended distributions of activity, and also from experimental data measured with Na-22 sources. Results show that our sensitivity model is the best suited for our prototype. Although other models in the literature perform successfully in many scenarios, they are not applicable in all the geometrical configurations of interest for our system. In general, our model allows to effectively recover the intensity of point-like sources at different positions in the FoV and to reconstruct regions of homogeneous activity with minimal variance. Moreover, it can be employed for all Compton camera configurations, including those with low angular coverage over the scatterer.
|
|
|
|
Muñoz, E., Barrio, J., Etxebeste, A., Ortega, P. G., Lacasta, C., Oliver, J. F., et al. (2017). Performance evaluation of MACACO: a multilayer Compton camera. Phys. Med. Biol., 62(18), 7321–7341.
Abstract: Compton imaging devices have been proposed and studied for a wide range of applications. We have developed a Compton camera prototype which can be operated with two or three detector layers based on monolithic lanthanum bromide (LaBr3) crystals coupled to silicon photomultipliers (SiPMs), to be used for proton range verification in hadron therapy. In this work, we present the results obtained with our prototype in laboratory tests with radioactive sources and in simulation studies. Images of a Na-22 and an Y-88 radioactive sources have been successfully reconstructed. The full width half maximum of the reconstructed images is below 4 mm for a Na-22 source at a distance of 5 cm.
|
|
|
|
Muñoz, E., Ros, A., Borja-Lloret, M., Barrio, J., Dendooven, P., Oliver, J. F., et al. (2021). Proton range verification with MACACO II Compton camera enhanced by a neural network for event selection. Sci Rep, 11(1), 9325–12pp.
Abstract: The applicability extent of hadron therapy for tumor treatment is currently limited by the lack of reliable online monitoring techniques. An active topic of investigation is the research of monitoring systems based on the detection of secondary radiation produced during treatment. MACACO, a multi-layer Compton camera based on LaBr3 scintillator crystals and SiPMs, is being developed at IFIC-Valencia for this purpose. This work reports the results obtained from measurements of a 150 MeV proton beam impinging on a PMMA target. A neural network trained on Monte Carlo simulations is used for event selection, increasing the signal to background ratio before image reconstruction. Images of the measured prompt gamma distributions are reconstructed by means of a spectral reconstruction code, through which the 4.439 MeV spectral line is resolved. Images of the emission distribution at this energy are reconstructed, allowing calculation of the distal fall-off and identification of target displacements of 3 mm.
|
|
|
|
Oliver, J. F., Fuster-Garcia, E., Cabello, J., Tortajada, S., & Rafecas, M. (2013). Application of Artificial Neural Network for Reducing Random Coincidences in PET. IEEE Trans. Nucl. Sci., 60(5), 3399–3409.
Abstract: Positron Emission Tomography (PET) is based on the detection in coincidence of the two photons created in a positron annihilation. In conventional PET, this coincidence identification is usually carried out through a coincidence electronic unit. An accidental coincidence occurs when two photons arising from different annihilations are classified as a coincidence. Accidental coincidences are one of the main sources of image degradation in PET. Some novel systems allow coincidences to be selected post-acquisition in software, or in real time through a digital coincidence engine in an FPGA. These approaches provide the user with extra flexibility in the sorting process and allow the application of alternative coincidence sorting procedures. In this work a novel sorting procedure based on Artificial Neural Network (ANN) techniques has been developed. It has been compared to a conventional coincidence sorting algorithm based on a time coincidence window. The data have been obtained from Monte-Carlo simulations. A small animal PET scanner has been implemented to this end. The efficiency (the ratio of correct identifications) can be selected for both methods. In one case by changing the actual value of the coincidence window used, and in the other by changing a threshold at the output of the neural network. At matched efficiencies, the ANN-based method always produces a sorted output with a smaller random fraction. In addition, two differential trends are found: the conventional method presents a maximum achievable efficiency, while the ANN-based method is able to increase the efficiency up to unity, the ideal value, at the cost of increasing the random fraction. Images reconstructed using ANN sorted data (no compensation for randoms) present better contrast, and those image features which are more affected by randoms are enhanced. For the image quality phantom used in the paper, the ANN method decreases the spill-over ratio by a factor of 18%.
|
|
