Reconstruction multiclasse pour l'imagerie TEP 3-photons

TL;DR

This paper introduces a theoretical framework and an iterative maximum likelihood algorithm for multi-class image reconstruction in 3-photon PET imaging, demonstrated through Monte Carlo simulations on a preclinical Compton camera.

Contribution

It develops a novel multi-class reconstruction method and a framework to quantify information from different detection classes in 3-photon PET imaging.

Findings

First model elements demonstrated via Monte Carlo simulations.

Framework quantifies information contribution of each detection class.

Algorithm effectively reconstructs radioactivity distribution across classes.

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.