Development of integrated prompt gamma imaging and positron emission tomography system for in vivo 3-D dose verification: a Monte Carlo study. (22nd May 2020) - Choi, Hyun Joon; Jang, Ji Won; Shin, Wook-Geun; Park, Hyojun; Incerti, Sebastien; Min, Chul Hee
Abstract: An accurate knowledge of in vivo proton dose distribution is key to fully utilizing the potential advantages of proton therapy. Two representative indirect methods for in vivo range verification, namely, prompt gamma (PG) imaging and positron emission tomography (PET), are available. This study proposes a PG-PET system that combines the advantages of these two methods and presents detector geometry and background reduction techniques optimized for the PG-PET system. The characteristics of the secondary radiations emitted by a water phantom by interaction with a 150 MeV proton beam were analysed using Geant4.10.00, and the 2-D PG distributions were obtained and assessed for different detector geometries. In addition, the energy window (EW), depth-of-interaction (DOI), and time-of-flight (TOF) techniques are proposed as the background reduction techniques. To evaluate the performance of the PG-PET system, the 3-D dose distribution in the water phantom caused by two proton beams of energies 80 MeV and 100 MeV was verified using 16 optimal detectors. The thickness of the parallel-hole tungsten collimator of pitch 8 mm and width 7 mm was determined as 200 mm, and that of the GAGG scintillator was determined as 30 mm, by an optimization study. Further, 3–7 MeV and 2–7 MeV were obtained as the optimal EWs when the DOI and both the DOI and TOF techniques were applied for data processing, respectively; the detector performances were improved by about 38% and 167%, respectively, compared with that when applying only the 3–5 MeV EW. In this study, we confirmed that the PG distribution can be obtained by simply combining the 2-D parallel hole collimator and the PET detector module. In the future, we will develop an accurate 3-D dose evaluation technique using deep learning algorithms based on the image sets of dose, PG, and PET distributions for various proton energies.