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In the present work we describe the design, construction, and testing of the optical prototype developed for the BOLDPET project, with the objective of creating a PET detection module with high spatial and time resolution. The BOLDPET technology uses an innovative detection liquid, trimethylbismuth, for detecting 511 keV $\gamma$-quanta resulting from positron annihilation. The optical signal is exclusively produced through the Cherenkov mechanism, and the produced photons are detected using Planacon microchannel-plate photomultiplier. We achieve an excellent time resolution of 150 ps (FWHM) within a sizable detection volume measuring 55 mm x 55 mm x 25 mm. Through detailed Geant4 simulations, we examine the limiting factors affecting time resolution and explore potential avenues for improvement. Furthermore, we demonstrate the feasibility of coarse 2D localization of interactions using the optical signal alone, achieving a precision of about 5-8 mm (FWHM) within the homogeneous detection volume., Comment: Accepted by JINST

This article presents a physics-informed deep learning method for the quantitative estimation of the spatial coordinates of gamma interactions within a monolithic scintillator, with a focus on Positron Emission Tomography (PET) imaging. A Density Neural Network approach is designed to estimate the 2-dimensional gamma photon interaction coordinates in a fast lead tungstate (PbWO4) monolithic scintillator detector. We introduce a custom loss function to estimate the inherent uncertainties associated with the reconstruction process and to incorporate the physical constraints of the detector. This unique combination allows for more robust and reliable position estimations and the obtained results demonstrate the effectiveness of the proposed approach and highlights the significant benefits of the uncertainties estimation. We discuss its potential impact on improving PET imaging quality and show how the results can be used to improve the exploitation of the model, to bring benefits to the application and how to evaluate the validity of the given prediction and the associated uncertainties. Importantly, our proposed methodology extends beyond this specific use case, as it can be generalized to other applications beyond PET imaging., Comment: Submitted to Artificial Intelligence

The ClearMind project aims to develop the TOF-PET position sensitive detection module optimized for the time resolution, spatial resolution, and detection efficiency. For this, the ClearMind project uses a large (59 $\times$ 59 mm$^2$) monolithic PbWO$_4$ (PWO) scintillating crystal with a bialkali photo-electric layer deposited directly on the crystal. Scintillation and Cherenkov photons result together from the 511 keV gamma-ray interation into the PWO crystal. A micro-channel plate photomultiplier tube (MCP-PMT) encapsulating the PWO crystal amplifies photoelectrons generated at the photocathode, and the corresponding anode signals are collected through the transmission lines read out at both ends and digitized by a SAMPIC module. In this work, we present a realistic Geant4 simulation of the ClearMind prototype detector, including the propagation of the visible photons in the crystal, the modelling of a realistic response of the photocathode and of the PMT, and the propagation of the electrical signals over the transmission lines. The reconstruction of the gamma conversion in the detector volume is performed from the signals registered at both ends of the transmission lines. We compare the reconstruction precision of a statistical algorithm against machine learning algorithms developed using the TMVA package. We expect to reach a spatial resolution down to a few mm$^3$ (FWHM). Finally, we will discuss prospects for the ClearMind detector.