• regularization
• image quality
• PET/MRI
• PET

This thesis presents the study of the performance of the TRIMAGE brain PET scanner obtained through experimental detectors characterization, simulated phantom acquisitions and image reconstruction optimization. The TRIMAGE scanner uses dual-layer staggered LYSO:Ce crystal matrices coupled to silicon photomultipliers (SiPM). The dual layer architecture provides depth of interaction (DOI) capabilities and a finer sampling of the lines of response, although it demonstrated to exacerbate SiPM saturation effects. For this reason, and because of the relatively high cost of staggered crystals, a lower cost detector alternative based on single layer crystals is also considered. Detectors characterization showed that the single layer configuration provides better results in terms of energy resolution (ER) and coincidence time resolution (CTR) with respect to the dual layer configuration. Using Monte Carlo simulations, a standard-like phantom for brain imaging has been simulated. The used phantom is a smaller version of the standard image quality phantom used in full body PET imaging, as it has been done already in similar works available in literature. Measurements of image noise and image resolution have been found according the prescriptions published by the National Electrical Manufacturers Association (NEMA), the former in terms of uniformity, the latter in terms of recovery coefficient (RC) and spill-over ratio (SOR). Results point out that, despite the better raw detector performance of the single layer configuration, the dual layer configuration produces better images according to NEMA standards. In order to improve image quality, the reconstruction software has been enhanced by introducing image regularization. Regularization allows to control the noise during along the iterations of the image reconstruction process. Different regularization algorithms have been used, including Gaussian filtering, patch-based regularization and a novel gradient-enhancer algorithm. The latter is the only iterative procedure used so far that combines a denoising filter with a feature restoration one. Image quality analysis have been performed for all the configurations mentioned above, showing that the proposed gradient-minimization algorithm is very stable and performs well in terms of uniformity versus recovery coefficients, although the asymptotic values of RC and SOR are lower than in patch-based regularization. Results show that the performance of the TRIMAGE PET scanner, with the staggered dual layer configuration and the novel regularized image reconstruction, is at the state of the art for brain PET imaging.