Reconstruction multiclasse pour l'imagerie TEP 3-photons
Abstract
This contribution addresses the problem of image reconstruction of radioactivity distribution for which the available information arises from several classes of data, each associated with a specific combination of detections. We introduce a theoretical framework to measure the amount of information brought by each class and we develop an iterative algorithm dedicated to multi-class reconstruction based on maximum likelihood. We apply our approach to the XEMIS2 camera, a preclinical prototype of a Compton telescope dedicated to 3-photon PET imaging for which four distinct classes of partial detections coexist with the full detection class. Based on Monte Carlo simulations, we present the first elements of our model.
Type
Publication
In XXIXème Colloque Francophone de Traitement du Signal et des Images (GRETSI'23).
