Abstract:
A detector design especially for small animal PET systems requires taking into account three main factors: these are high energy and spatial resolution and price. When examining the state-of-the-art PET detectors, it can be seen that many researchers have preferred to use continuous (monolithic), block or discrete crystals for small animal PET systems. Although, the discrete crystal detector designs have provided high spatial resolution, they also have caused many complications such as, reduced light collection (low packing fraction), labour-intensive use and increased costs. In this study, to overcome these limitations, the feasibility of using a continuous crystal instead of block or discrete designs has been explored for high resolution small animal PET applications. For this aim, a PET detector for small animals based on continuous block Lutetium oxyorthosilicate crystal (LSO) (16mm x 16mm) coupled to a PS-PMT (Hamamatsu H8711-03) has been designed. When working with continuous crystals, surface treatment and crystal thickness are important factors that strongly determine the main characteristics of the detector module. Therefore, for the development of this explored small animal PET detector, the effects of these factors on the detector module performances have been investigated, in order to optimize crystal configuration. In this study 4 different surface treatments (Polish + Black, Ground + Black, Ground + Methacrylate, Ground + Air), 3 different crystal thickness (3mm-6mm-9mm) and 41 different source coordinates were used. The obtained results for the energy resolution, spatial resolution and image compression have been presented when using different surface treatments and thicknesses in continuous LSO crystals. The simulation results have been carried out by using DETECT2000 package. The end word, high spatial resolution is the most important parameter for a PET detector. In our study, Ground + Air (GA) surface treatment gives the highest special resolution but, the image compression is poor. However, this poorness can be avoided by using certain statistics based positioning (SBP) algorithms.
