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Molecular imaging systems, such as positron emission tomography (PET), use detectors providing energy and a 3-D interaction position of a gamma ray within a scintillation block. Monolithic crystals are becoming an alternative to crystal arrays in PET. However, calibration processes are required to correct for nonuniformities, mainly produced by the truncation of the scintillation light distribution at the edges. We propose a calibration method based on the Voronoi diagrams. We have used 50 × 50 × 15 mm 3 LYSO blocks coupled to a 12 × 12 SiPMs array. We have first studied two different interpolation algorithms: 1) weighted average method (WAM) and 2) natural neighbor (NN). We have compared them with an existing calibration based on 1-D monomials. Here, the crystal was laterally black painted and a retroreflector (RR) layer added to the entrance face. The NN exhibited the best results in terms of XY impact position, depth of Interaction, and energy, allowing us to calibrate the whole scintillation volume. Later, the NN interpolation has been tested against different crystal surface treatments, allowing always to correct edge effects. Best energy resolutions were observed when using the reflective layers (12%-14%). However, better linearity was observed with the treatments using black paint. In particular, we obtained the best overall performance when lateral black paint is combined with the RR.

We propose an ensemble of multilayer feedforward neural networks to estimate the 3D position of photon 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.

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