Balibrea-Correa, J., Lerendegui-Marco, J., Babiano-Suarez, V., Caballero, L., Calvo, D., Ladarescu, I., et al. (2021). Machine Learning aided 3D-position reconstruction in large LaCl3 crystals. Nucl. Instrum. Methods Phys. Res. A, 1001, 165249–17pp.
Abstract: We investigate five different models to reconstruct the 3D gamma-ray hit coordinates in five large LaCl3(Ce) monolithic crystals optically coupled to pixelated silicon photomultipliers. These scintillators have a base surface of 50 x 50 mm(2) and five different thicknesses, from 10 mm to 30 mm. Four of these models are analytical prescriptions and one is based on a Convolutional Neural Network. Average resolutions close to 1-2 mm fwhm are obtained in the transverse crystal plane for crystal thicknesses between 10 mm and 20 mm using analytical models. For thicker crystals average resolutions of about 3-5 mm fwhm are obtained. Depth of interaction resolutions between 1 mm and 4 mm are achieved depending on the distance of the interaction point to the photosensor surface. We propose a Machine Learning algorithm to correct for linearity distortions and pin-cushion effects. The latter allows one to keep a large field of view of about 70%-80% of the crystal surface, regardless of crystal thickness. This work is aimed at optimizing the performance of the so-called Total Energy Detector with Compton imaging capability (i-TED) for time-of-flight neutron capture cross-section measurements.
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Roser, J., Barrientos, L., Bernabeu, J., Borja-Lloret, M., Muñoz, E., Ros, A., et al. (2022). Joint image reconstruction algorithm in Compton cameras. Phys. Med. Biol., 67(15), 155009–15pp.
Abstract: Objective. To demonstrate the benefits of using an joint image reconstruction algorithm based on the List Mode Maximum Likelihood Expectation Maximization that combines events measured in different channels of information of a Compton camera. Approach. Both simulations and experimental data are employed to show the algorithm performance. Main results. The obtained joint images present improved image quality and yield better estimates of displacements of high-energy gamma-ray emitting sources. The algorithm also provides images that are more stable than any individual channel against the noisy convergence that characterizes Maximum Likelihood based algorithms. Significance. The joint reconstruction algorithm can improve the quality and robustness of Compton camera images. It also has high versatility, as it can be easily adapted to any Compton camera geometry. It is thus expected to represent an important step in the optimization of Compton camera imaging.
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Borja-Lloret, M., Barrientos, L., Bernabeu, J., Lacasta, C., Muñoz, E., Ros, A., et al. (2023). Influence of the background in Compton camera images for proton therapy treatment monitoring. Phys. Med. Biol., 68(14), 144001–16pp.
Abstract: Objective. Background events are one of the most relevant contributions to image degradation in Compton camera imaging for hadron therapy treatment monitoring. A study of the background and its contribution to image degradation is important to define future strategies to reduce the background in the system. Approach. In this simulation study, the percentage of different kinds of events and their contribution to the reconstructed image in a two-layer Compton camera have been evaluated. To this end, GATE v8.2 simulations of a proton beam impinging on a PMMA phantom have been carried out, for different proton beam energies and at different beam intensities. Main results. For a simulated Compton camera made of Lanthanum (III) Bromide monolithic crystals, coincidences caused by neutrons arriving from the phantom are the most common type of background produced by secondary radiations in the Compton camera, causing between 13% and 33% of the detected coincidences, depending on the beam energy. Results also show that random coincidences are a significant cause of image degradation at high beam intensities, and their influence in the reconstructed images is studied for values of the time coincidence windows from 500 ps to 100 ns. Significance. Results indicate the timing capabilities required to retrieve the fall-off position with good precision. Still, the noise observed in the image when no randoms are considered make us consider further background rejection methods.
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Torres-Sanchez, P., Lerendegui-Marco, J., Balibrea-Correa, J., Babiano-Suarez, V., Gameiro, B., Ladarescu, I., et al. (2025). The potential of the i-TED Compton camera array for real-time boron imaging and determination during treatments in Boron Neutron Capture Therapy. Appl. Radiat. Isot., 217, 111649–9pp.
Abstract: This paper explores the adaptation and application of i-TED Compton imagers for real-time dosimetry in Boron Neutron Capture Therapy (BNCT). The i-TED array, previously utilized in nuclear astrophysics experiments at CERN, is being optimized for detecting and imaging 478 keV gamma-rays, critical for accurate BNCT dosimetry. Detailed Monte Carlo simulations were used to optimize the i-TED detector configuration and enhance its performance in the challenging radiation environment typical of BNCT. Additionally, advanced 3D image reconstruction algorithms, including a combination of back-projection and List-Mode Maximum Likelihood Expectation Maximization (LM-MLEM), are implemented and validated through simulations. Preliminary experimental tests at the Institut Laue-Langevin (ILL) demonstrate the potential of i-TED in simplified conditions, with ongoing experiments focusing on testing imaging capabilities in realistic BNCT conditions.
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Babiano-Suarez, V., Balibrea-Correa, J., Ladarescu, I., Lerendegui-Marco, J., & Domingo-Pardo, C. (2025). A computer-vision aided Compton-imaging system for radioactive waste characterization and decommissioning of nuclear power plants. Nucl. Instrum. Methods Phys. Res. A, 1076, 170449–14pp.
Abstract: Nuclear energy production is inherently tied to the management and disposal of radioactive waste. Enhancing classification and monitoring tools is therefore crucial, with significant socioeconomic implications. This paper reports on the applicability and performance of a high-efficiency, cost-effective and portable Compton camera for detecting and visualizing low-and medium-level radioactive waste from the decommissioning and regular operation of nuclear power plants. The results demonstrate the good performance of Compton imaging for this type of application, both in terms of image resolution and reduced measuring time. A technical readiness level of TRL7 has been thus achieved with this system prototype, as demonstrated with dedicated field measurements carried out at the radioactive-waste disposal plant of El Cabril (Spain) utilizing a pluarility of radioactive-waste drums from decomissioned nuclear power plants. The performance of the system has been enhanced by means of computer-vision techniques in combination with advanced Compton-image reconstruction algorithms based on Maximum-Likelihood Expectation Maximization. Finally, we also show the feasibility of 3D tomographic reconstruction from a series of relatively short measurements around the objects of interest. The potential of this imaging system to enhance nuclear waste management makes it a promising innovation for the nuclear industry.
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