Abstract: Continuous crystals can potentially obtain better intrinsic detector spatial resolution compared to pixelated crystals, additionally providing depth of interaction (DoI) information from the light distribution. To achieve high performance sophisticated interaction position estimation algorithms are required. There are a number of algorithms in the literature applied to different crystal dimensions and different photodetectors. However, the different crystal properties and photodetector array geometries have an impact on the algorithm performance. In this work we analysed, through Monte Carlo simulations, different combinations of realistic crystals and photodetector parameters to better understand their influence on the interaction position estimation accuracy, with special emphasis on the DoI. We used an interaction position estimation based on an analytical model for the present work. Different photodetector granulation schemes were investigated. The impact of the number of crystal faces readout by photodetectors was studied by simulating scenarios with one and two photodetectors. In addition, crystals with different levels of reflection and aspect ratios (AR) were analysed. Results showed that the impact of photodetector granularity is mainly shown near the edges and specially in the corners of the crystal. The resulting intrinsic spatial resolution near the centre with a 12 x 12 x 10 mm(3) LYSO crystal was 0.7-0.9 mm, while the average spatial resolution calculated on the entire crystal was 0.77 +/- 0.18 mm for all the simulated geometries with one and two photodetectors. Having front and back photodetectors reduced the DoI bias (Euclidean distance between estimated DoI and real DoI) and improved the transversal resolution near the corners. In scenarios with one photodetector, small AR resulted in DoI inaccuracies for absorbed events at the entrance of the crystal. These inaccuracies were slightly reduced either by increasing the AR or reducing the amount of reflected light, and highly mitigated using two photodetectors. Using one photodetector, we obtained a piecewise DoI error model with a DoI resolution of 0.4-0.9 mm for a 1.2 AR crystal, and we observed that including a second photodetector or reducing the amount of reflections reduced the DoI bias but did not significantly improve the DoI resolution. Translating the piecewise DoI error model obtained in this study to image reconstruction we obtained a spatial resolution variability of 0.39 mm using 85% of the FoV, compared to 2.59 mm and 1.87 mm without DoI correction or with a dual layer system, respectively.

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.

Abstract: A high resolution PET detector head for small animal PET applications has been developed. The detector is composed of a 12 mm x 12 mm continuous LYSO crystal coupled to a 64-channel monolithic SiPM matrix from FBK-irst. Crystal thicknesses of 5 mm and 10 mm have been tested, both yielding an intrinsic spatial resolution around 0.7 mm FWHM with a position determination algorithm that can also provide depth-of-interaction information. The detectors have been tested in a rotating system that makes it possible to acquire tomographic data and reconstruct images of Na-22 sources. An image reconstruction method specifically adapted for continuous crystals has been employed. The Full Width at Half Maximum measured from a point source reconstructed with ML-EM was 0.7 mm with the 5 mm crystal and 0.8 mm with the 10 mm crystal.

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%.