Position estimation using neural networks in semi-monolithic PET detectors.

Objective. The goal of this work is to experimentally compare the 3D spatial and energy resolution of a semi-monolithic detector suitable for total-body positron emission tomography (TB-PET) scanners using different surface crystal treatments and silicon photomultiplier (SiPM) models.Approach. An array of 1 × 8 lutetium yttrium oxyorthosilicate (LYSO) slabs of 25.8 × 3.1 × 20 mm3separated with Enhanced Specular Reflector (ESR) was coupled to an array of 8 × 8 SiPMs. Three different treatments for the crystal were evaluated: ESR + RR + B,with lateral faces black (B) painted and a retroreflector (RR) layer added to the top face; ESR +RR, with lateral faces covered with ESR and a RR layer on the top face and; All ESR, with lateral and top sides with ESR. Additionally, two SiPM array models from Hamamatsu Photonics belonging to the series S13361-3050AE-08 (S13) and S14161-3050AS-08 (S14) have been compared. Coincidence data was experimentally acquired using a22Na point source, a pinhole collimator, a reference detector and moving the detector under study in 1 mm steps in thex- andDOI- directions. The spatial performance was evaluated by implementing a neural network (NN) technique for the impact position estimation in thex- (monolithic) andDOIdirections.Results. Energy resolution values of 16 ± 1%, 11 ± 1%, 16 ± 1%, 15 ± 1%, and 13 ± 1% were obtained for theS13-ESR + B + RR,S13-AllESR,S14-ESR + B + RR,S14-ESR + RR,andS14-AllESR, respectively. Regarding positioning accuracy, mean average error of 1.1 ± 0.5, 1.3 ± 0.5 and 1.3 ± 0.5 were estimated for thex- direction and 1.7 ± 0.8, 2.0 ± 0.9 and 2.2 ± 1.0 for theDOI- direction, for the ESR + B + RR, ESR + RR and All ESR cases, respectively, regardless of the SiPM model.Significance. Overall, the obtained results show that the proposed semi-monolithic detectors are good candidates for building TB-PET scanners.

Large medical datasets on the Grid.

OBJECTIVE: This paper shows the use of the emerging Grid technology for gathering underused resources that are distributed among a corporate network. The work of these resources is coordinated for facing tasks which are not affordable by the individual usage of each of them. METHODS: This paper shows an application for the projection, using Volume Rendering techniques, of huge medical volumes obtained from CTs and RMIs, adapted to Grid computing. RESULTS: As a result the article shows the feasibility of the creation of an application based up on Grid technology, which solves problems that cannot be addressed by using common techniques. As an example, the article describes the projection of a huge medical dataset, which exceeds the resources of most common PCs, carried out by taking profit of idle CPU cycles from the computers of an organization. CONCLUSIONS: Grid technology is emerging as a new framework which allows gathering and coordinating resources distributed among a network (LAN or WAN), for addressing problems which cannot be solved through the single use of any of these resources. Medical Imaging is a clear application area for this technology.