An Advanced Simulation and Reconstruction Framework for a Novel In-Beam PET Scanner for Pre-Clinical Proton Irradiation

摘要

Within the project “Small animal proton Irradiator for Research in Molecular Image-guided radiation-Oncology” (SIRMIO) we have designed an in-beam PET scanner for preclinical application. The system is based on a novel spherical geometry, and in order to fully exploit its potential we are developing an integrated computational framework for simulation, image reconstruction and range verification. The software comprises a full Monte Carlo engine to simulate the proton treatment with related detector response, and an image reconstruction tool for simulated and experimental data. The platform is designed to integrate robust analytical reconstruction algorithms and new statistical approaches based on deep learning. The core of the framework is based on MEGAlib (The Medium Energy Gamma-ray Astronomy software library). The physical simulation is based on GEANT4. The machine learning method for the event classification is implemented with the ROOT based Toolkit for Multivariate Data Analysis (TMVA). The first prototype of the SIRMIO irradiation platform foresees a fixed beam, thus requiring the movement of the mouse for scanned beam delivery. Hence, we have extended the MEGAlib image reconstruction algorithm based on maximum-likelihood expectation-maximization (ML-EM) to correct for geometrical efficiency and attenuation taking into account the mouse motion. The goal is to be able to discriminate proton range shifts of ~ 0.5 mm. Moreover, we are augmenting the image reconstruction framework with a new approach based on machine learning, which aims at using all photon events collected during irradiation (dominated by prompt gamma) to retrieve on-the-fly the range of the beam, to complement the PET information.