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Barrientos, D., Bellato, M., Bazzacco, D., Bortolato, D., Cocconi, P., Gadea, A., et al. (2015). Performance of the Fully Digital FPGA-Based Front-End Electronics for the GALILEO Array. IEEE Trans. Nucl. Sci., 62(6), 3134–3139.
Abstract: In this work we present the architecture and results of a fully digital Front End Electronics (FEE) read out system developed for the GALILEO array. The FEE system, developed in collaboration with the Advanced Gamma Tracking Array (AGATA) collaboration, is composed of three main blocks: preamplifiers, digitizers and preprocessing electronics. The slow control system contains a custom Linux driver, a dynamic library and a server implementing network services. This work presents the first results of the digital FEE system coupled with a GALILEO germanium detector, which has demonstrated the capability to achieve an energy resolution of 1.53% at an energy of 1.33 MeV, similar to the one obtained with a conventional analog system. While keeping a good performance in terms of energy resolution, digital electronics will allow to instrument the full GALILEO array with a versatile system with high integration and low power consumption and costs.
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DEPFET collaboration(Alonso, O. et al), Boronat, M., Esperante-Pereira, D., Fuster, J., Garcia, I. G., Lacasta, C., et al. (2013). DEPFET Active Pixel Detectors for a Future Linear e(+)e(-) Collider. IEEE Trans. Nucl. Sci., 60(2), 1457–1465.
Abstract: The DEPFET collaboration develops highly granular, ultra-transparent active pixel detectors for high-performance vertex reconstruction at future collider experiments. The characterization of detector prototypes has proven that the key principle, the integration of a first amplification stage in a detector-grade sensor material, can provide a comfortable signal to noise ratio of over 40 for a sensor thickness of 50-75 μm. ASICs have been designed and produced to operate a DEPFET pixel detector with the required read-out speed. A complete detector concept is being developed, including solutions for mechanical support, cooling, and services. In this paper, the status of the DEPFET R & D project is reviewed in the light of the requirements of the vertex detector at a future linear e(+)e(-) collider.
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Brown, J. M. C., Gillam, J. E., Paganin, D. M., & Dimmock, M. R. (2013). Laplacian Erosion: An Image Deblurring Technique for Multi-Plane Gamma-Cameras. IEEE Trans. Nucl. Sci., 60(5), 3333–3342.
Abstract: Laplacian Erosion, an image deblurring technique for multi-plane Gamma-cameras, has been developed and tested for planar imaging using a GEANT4 Monte Carlo model of the Pixelated Emission Detector for RadioisOtopes (PEDRO) as a test platform. A contrast and Derenzo-like phantom composed of I-125 were both employed to investigate the dependence of detection plane and pinhole geometry on the performance of Laplacian Erosion. Three different pinhole geometries were tested. It was found that, for the test system, the performance of Laplacian Erosion was inversely proportional to the detection plane offset, and directly proportional to the pinhole diameter. All tested pinhole geometries saw a reduction in the level of image blurring associated with the pinhole geometry. However, the reduction in image blurring came at the cost of signal to noise ratio in the image. The application of Laplacian Erosion was shown to reduce the level of image blurring associated with pinhole geometry and improve recovered image quality in multi-plane Gamma-cameras for the targeted radiotracer I-125.
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Cabello, J., Torres-Espallardo, I., Gillam, J. E., & Rafecas, M. (2013). PET Reconstruction From Truncated Projections Using Total-Variation Regularization for Hadron Therapy Monitoring. IEEE Trans. Nucl. Sci., 60(5), 3364–3372.
Abstract: Hadron therapy exploits the properties of ion beams to treat tumors by maximizing the dose released to the target and sparing healthy tissue. With hadron beams, the dose distribution shows a relatively low entrance dose which rises sharply at the end of the range, providing the characteristic Bragg peak that drops quickly thereafter. It is of critical importance in order not to damage surrounding healthy tissues and/or avoid targeting underdosage to know where the delivered dose profile ends-the location of the Bragg peak. During hadron therapy, short-lived beta(+)-emitters are produced along the beam path, their distribution being correlated with the delivered dose. Following positron annihilation, two photons are emitted, which can be detected using a positron emission tomography (PET) scanner. The low yield of emitters, their short half-life, and the wash out from the target region make the use of PET, even only a few minutes after hadron irradiation, a challenging application. In-beam PET represents a potential candidate to estimate the distribution of beta(+)-emitters during or immediately after irradiation, at the cost of truncation effects and degraded image quality due to the partial rings required of the PET scanner. Time-of-flight (ToF) information can potentially be used to compensate for truncation effects and to enhance image contrast. However, the highly demanding timing performance required in ToF-PET makes this option costly. Alternatively, the use of maximum-a-posteriori-expectation-maximization (MAP-EM), including total variation (TV) in the cost function, produces images with low noise, while preserving spatial resolution. In this paper, we compare data reconstructed with maximum-likelihood-expectation-maximization (ML-EM) and MAP-EM using TV as prior, and the impact of including ToF information, from data acquired with a complete and a partial-ring PET scanner, of simulated hadron beams interacting with a polymethyl methacrylate (PMMA) target. The results show that MAP-EM, in the absence of ToF information, produces lower noise images and more similar data compared to the simulated beta(+) distributions than ML-EM with ToF information in the order of 200-600 ps. The investigation is extended to the combination of MAP-EM and ToF information to study the limit of performance using both approaches.
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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%.
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