Evaluation of a Method Combining Physical Experiment and Data Insertion For the Assessment of PET-CT Systems

Abstract

Background: To evaluate and compare Positron Emission Tomography (PET) devices among them, tests are performed on phantoms that generally consist in simple geometrical objects, fillable with radiotracers. On one hand, those tests bring a control over the experiment through the operator preparation but on the other hand, they are limited in terms of reproducibility, repeatability and are time-consuming, in particular, if several replications are required. To overcome these restrictions, we designed a method combining physical experiment and data insertion that aims to avoid experimental repetitions while testing multiple configurations for the performance evaluation of PET scanners.Methods: Based on the National Electrical Manufacturers Association Image Quality standard, four experiments, with different spheres-to-background ratios: 2:1, 4:1, 6:1 and 8:1, were performed. An additional acquisition was done with a radioactive background and no activity within the spheres. It was created as a baseline to artificially simulate the radioactive spheres and reproduce initial experiments. Standard sphere set was replaced by smaller target sizes (4, 5, 6, 8, 10 and 13 mm) to match current detectability performance of PET scanners. Images were reconstructed following standard guidelines, i.e. using OSEM algorithm, and an additional BPL reconstruction was performed. We visually compared experimental and simulated images. We measured the activity concentration values into the spheres to calculate the mean and maximum recovery coefficient (RCmean and RCmax ) which we used in a quantitative analysis.Results: No significant visual discrepancies were identified between experimental and simulated series. Mann-Whitney U tests comparing simulated and experimental distributions showed no statistical differences for both RCmean (P value = 0.611) and RCmax (P value = 0.720). Spearman tests revealed high correlation for RCmean (ρ = 0.974, P value < 0.001) and RCmax (ρ = 0.974, P value < 0.001) between both datasets. According to Bland-Altman plots, we highlighted slight shifts in RCmean and RCmax of respectively 2.1 ± 16.9 % and 3.3 ± 22.3 %.Conclusions: The method produced realistic results compared to experimental data. Known synthesized information fused with original data allows full exploration of the system's capabilities while avoiding the limitations associated with repeated experiments.