Publicly available framework for simulating and experimentally validating clinical PET systems

• Published in: Medical physics (Lancaster) 2023-03, Vol.50 (3), p.1549-1559
• Main Authors: O'Briain, Teaghan B., Uribe, Carlos, Sechopoulos, Ioannis, Michel, Christian, Bazalova‐Carter, Magdalena
• Format: Article
• Language: English
• Subjects: Computer Simulation; Models, Biological; Monte Carlo; Monte Carlo Method; PET imaging; phantoms; Phantoms, Imaging; Positron Emission Tomography Computed Tomography; Positron-Emission Tomography - methods
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1002/mp.16032

Background: Monte Carlo (MC) simulations are a powerful tool to model medical imaging systems. However, before simulations can be considered the ground truth, they have to be validated with experiments. Purpose: To provide a pipeline that models a clinical positron emission tomography (PET)/CT system using MC simulations after extensively validating the results against experimental measurements. Methods: A clinical four‐ring PET imaging system was modeled using Geant4 application for tomographic emission (v. 9.0). To validate the simulations, PET images were acquired of a cylindrical phantom, point source, and image quality phantom with the modeled system and the simulations of the experimental procedures. For the purpose of validating the quantification capabilities and image quality provided by the simulation pipeline, the simulations were compared against the measurements in terms of their count rates and sensitivity as well as their image uniformity, resolution, recovery coefficients (RCs), coefficients of variation, contrast, and background variability. Results: When compared to the measured data, the number of true detections in the MC simulations was within 5%. The scatter fraction was found to be 30.0% ± 2.2% and 28.8% ± 1.7% in the measured and simulated scans, respectively. Analyzing the measured and simulated sinograms, the sensitivities were found to be 8.2 and 7.8 cps/kBq, respectively. The fraction of random coincidences were 19% in the measured data and 25% in the simulation. When calculating the image uniformity within the axial slices, the measured image exhibited a uniformity of 0.015 ± 0.005, whereas the simulated image had a uniformity of 0.029 ± 0.011. In the axial direction, the uniformity was measured to be 0.024 ± 0.006 and 0.040 ± 0.015 for the measured and simulated data, respectively. Comparing the image resolution, an average percentage difference of 2.9% was found between the measurements and simulations. The RCs calculated in both the measured and simulated images were found to be within the EARL ranges, except for that of the simulation of the smallest sphere. The coefficients of variation for the measured and simulated images were found to be 12% and 13%, respectively. Lastly, the background variability was consistent between the measurements and simulations, whereas the average percentage difference in the sphere contrasts was found to be 8.8%. Conclusion: The clinical PET/CT system was modeled and validated to provide a si