Analysis methods for in-beam PET images in proton therapy treatment verification: a comparison based on Monte Carlo simulations

Abstract

Background and purpose: in-beam Positron Emission Tomography (PET) is one of the modalities that can be used for in-vivo non-invasive treatment monitoring in proton therapy. PET distributions obtained during various treatment sessions can be compared in order to identify regions that have anatomical changes. The purpose of this work is to test and compare different analysis methods in the context of inter-fractional PET image comparison for proton treatment verification. Methods: for our study we used the FLUKA Monte Carlo code and artificially generated CT scans to simulate in-beam PET distributions at different stages during proton therapy treatment. We compared the Beam-Eye-View method, the Most-Likely-Shift method, the Voxel-Based-Morphology method and the gamma evaluation method to compare PET images at the start of treatment, and after a few weeks of treatment. The results were compared to the CT scan. Results and conclusions: three-dimensional methods like VBM and gamma are preferred above two-dimensional methods like MLS and BEV if much statistics is available, since the these methods allow to identify the regions with anomalous activity. The VBM approach has as disadvantage that a larger number of MC simulations is needed. The gamma analysis has the disadvantage that no clinical indication exist on tolerance criteria. In terms of calculation time, the BEV and MLS method are preferred. We recommend to use the four methods together, in order to best identify the location and cause of the activity changes.

Synthetic CT imaging for PET monitoring in proton therapy: a simulation study

“…A small cohort of them (clinical trial registered under ID: NCT03662373) has been monitored with the INSIDE IB-PET scanner (Bisogni et al 2016, Bisogni 2019. Recent studies based both on MC simulation and the acquired INSIDE IB-PET data have shown the possibility of detecting morphological changes using range variation or 3D methods (Fiorina et al 2021, Kraan et al 2022, Moglioni et al 2022, Moglioni et al 2023. To the best of our knowledge, these are the most recent papers regarding attempts to correctly locate and identify the morphological change in patients starting from PET data.…”