Speaker
Description
Positron Emission Tomography (PET) is a medical imaging modality that is powerful to follow biological processes. Nevertheless, it is of importance to increase its sensitivity to improve the contrast on the PET images and to decrease the patient exposure to radiation. One promising way is the use of the Time-of-Flight (ToF) of coincident gamma ray photons to get a more precise information of the annihilation location event by event. New instrumental developments are necessary to achieve this objective, especially ultra-fast detectors. The ClearMind project has developed a detection system based on a fast lead tungstate (PbWo4) monolithic scintillator detector and its conception necessitates new processing methods of its signals to reconstruct the gamma photons interactions.
This work focuses on the processing of the recorded signals to estimate the spatial coordinates of the gamma interactions within the detector. The complexity of these signals brings the necessity to use advanced tools and we have developed Deep Learning models, trained on simulation. We introduce a custom loss function that aims at estimating the inherent uncertainties due to the randomness of the signal formation and that incorporates the physical constraints of the detector. The results show the effectiveness of the proposed approach that provides a robust and reliable estimation of the interaction location. They highlight the benefit of the uncertainties estimation that will be exploited in the future for PET image reconstruction to discard or weight each individual event in the objective to improve the signal to noise ratio on the reconstructed image.