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%.

Abstract: This work describes new electronics for the EX-OGAM2 (HP-Ge detector array) and NEDA (BC501A-based neutron detector array). A new digitizing card with high resolution has been designed for gamma-ray and neutron spectroscopy experiments. The higher bandwidth requirement of the NEDA signals, together with the necessity for accuracy, require a high sampling rate in order to preserve the shape for real-time Pulse Shape Analysis (PSA). The PSA is of paramount importance for the NEDA to discriminate between neutrons and gamma-ray signals. Both high resolution and high speed parameters are often difficult to achieve in a single electronic unit. These constraints, together with the need to build new digitizing electronics to improve performance and flexibility of signal analysis in nuclear physics experiments, led to the development a new FADC mezzanine card. In this work, the design and development are described, including the characterization procedure and the preliminary measurement results.

Abstract: This paper presents the final design and results of the FADC Mezzanine for the EXOGAM (EXOtic GAMma array spectrometer) and NEDA (Neutron Detector Array) detectors. The measurements performed include those of studying the effective number of bits, the energy resolution using HP-Ge detectors, as well as timing histograms and discrimination performance. Finally, the conclusion shows how a common digitizing device has been integrated in the experimental environment of two very different detectors which combine both low-noise acquisition and fast sampling rates. Not only the integration fulfilled the expected specifications on both systems, but it also showed how a study of synergy between detectors could lead to the reduction of resources and time by applying a common strategy.