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.

Ensemble of neural networks for 3D position estimation in monolithic PET detectors.

We propose an ensemble of multilayer feedforward neural networks to estimate the 3D position of photoelectric interactions in monolithic detectors. The ensemble is trained with data generated from optical Monte Carlo simulations only. The originality of our approach is to exploit simulations to obtain reference data, in combination with a variability reduction that the network ensembles offer, thus, removing the need of extensive per-detector calibration measurements. This procedure delivers an ensemble valid for any detector of the same design. We show the capability of the ensemble to solve the 3D positioning problem through testing four different detector designs with Monte Carlo data, measurements from physical detectors and reconstructed images from the MindView scanner. Network ensembles allow the detector to achieve a 2-2.4 mm FWHM, depending on its design, and the associated reconstructed images present improved SNR, CNR and SSIM when compared to those based on the MindView built-in positioning algorithm.