The recently developed GATE (GEANT4 application for tomographic emission) Monte Carlo package, designed to simulate positron emission tomography(PET) and single photon emission computed tomography (SPECT) scanners, provides the ability to model and account for the effects of photon noncollinearity, off-axis detector penetration, detector size and response, positron range, photon scatter, and patient motion on the resolution and quality of PETimages. The objective of this study is to validate a model within GATE of the General Electric (GE) Advance/Discovery Light Speed (LS) PETscanner. Our three-dimensional PET simulation model of the scanner consists of 12 096 detectors grouped into blocks, which are grouped into modules as per the vendor’s specifications. The GATE results are compared to experimental data obtained in accordance with the National Electrical Manufactures Association/Society of Nuclear Medicine (NEMA/SNM), NEMA NU 2-1994, and NEMA NU 2-2001 protocols. The respective phantoms are also accurately modeled thus allowing us to simulate the sensitivity, scatter fraction, count rate performance, and spatial resolution. In-house software was developed to produce and analyze sinograms from the simulated data. With our model of the GE Advance/Discovery LS PETscanner, the ratio of the sensitivities with sources radially offset 0 and from the scanner’s main axis are reproduced to within 1% of measurements. Similarly, the simulated scatter fraction for the NEMA NU 2-2001 phantom agrees to within less than 3% of measured values (the measured scatter fractions are 44.8% and and the simulated scatter fraction is ). The simulated count rate curves were made to match the experimental curves by using deadtimes as fit parameters. This resulted in deadtime values of 625 and at the Block and Coincidence levels, respectively. The experimental peak true count rate of and the peak activity concentration of were matched by the simulated results to within 0.5% and 0.1% respectively. The simulated count rate curves also resulted in a peak NECR of at compared to at from averaged experimental values. The spatial resolution of the simulated scanner matched the experimental results to within .
The authors want to thank Dr. Clifton Ling from the Department of Medical Physics, and Dr. Steven Larson from the Department of Radiology at Memorial Sloan-Kettering Cancer Center for their support of this work. In addition, the authors would like to acknowledge Brad Beattie from the Department of Neurology for many fruitful discussions regarding PET signal processing. Also, the authors are indebted to Dr. Sebastien Jan and Dr. Christian Morel for all of the help and the insight given by the OpenGATE collaboration. Finally, they would like to thank Dr. Tin-Su Pan of MD Anderson Cancer Center for his assistance with image reconstruction. This study was funded in part by a seed grant from the Department of Medical Physics at Memorial Sloan-Kettering Cancer Center and by the U.S. National Cancer Institute Grant No. CA059017-12.
II.A. Model description
II.A.2. Physics and Monte Carlo data
II.A.3. Signal processing
II.A.4. Coincidence processing
II.B. Evaluation protocols
II.C. Analytical single and random count rate model
III.B. Scatter fraction
III.C. Count rate performance
III.C.1. Comparison to experimental data
III.C.2. Comparison to model
III.D. Spatial resolution
IV.B. Scatter fraction
IV.C. Count rate performance
IV.C.1. Comparison of Monte Carlo to experimental data
IV.C.2. Comparison of Monte Carlo to the analytical model
IV.D. Spatial resolution
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