Room: AAPM ePoster Library
Purpose: We performed a Monte Carlo simulation study on the feasibility of a new positron emission tomography (PET) approach for proton therapy (PT) range monitoring. The monitoring of proton range has the potential to improve conformity and accuracy of PT treatments.
Methods: A modular PET detector technology based on thin, 50cm long plastic scintillator strips has been developed. 13 strips compose a PET module, which can be integrated and freely configured in the PT treatment room. The beta+ emitters produced by proton beams in patient are the source of back-to-back gamma quanta that, when interacting in the detector, produce light pulses in a strip. The light pulses are propagated to the strip edges and converted to electrical signals by silicon photomultipliers, read-out by front-end electronics. We performed GATE/Geant4 simulations and reconstructed PET images using CASTOR software toolkit for multi-layer barrel and dual head PET system designs. For these designs, we evaluated the detection efficiency and number of coincidences that can be registered during a real PT patient treatment. We show the results of validation of the computational tools and first patient treatment simulations.
Results: We implemented plastic scintillator based PET system designs of geometrical acceptance ranging from about 0.2 to 0.4 and efficiency for detection of two gamma quanta in coincidence of about 1%. We demonstrated that GATE and CASTOR toolkits can be used for simulation of the signal registered by the detector during proton therapy patient treatment and tomographic image reconstruction, respectively. We found that plastic scintillator based PET system can register an order of magnitude of E-5 coincidence back-to-back events per primary proton.
Conclusion: The developed tools enable simulation of the detector response during proton therapy treatments and optimization of the PET system design. We will perform validation experiments in autumn 2020.
Funding Support, Disclosures, and Conflict of Interest: This research was supported by the National Centre for Research and Development (NCBiR), grant no. LIDER/26/0157/L-8/16/NCBR/2017, InterDokMed programme grant no. POWR.03.02.00-00-I013/16, and in part by PLGrid Infrastructure.