Effects of PET image reconstruction parameters and tumor-to-background uptake ratio on quantification of PET images from PET/MRI and PET/CT systems

Abstract

PET/CT and PET/MRI are valuable multimodality imaging techniques for visualizing both functional and anatomical information. The most used PET reconstruction algorithm is Ordered Subset Expectation Maximization (OSEM). In OSEM, the image noise increases with increased number of iterations, and the reconstruction needs to be stopped before complete convergence. The Bayesian penalized likelihood (BPL) algorithm, recently introduced, uses a noise penalty factor (β) to achieve full convergence while controlling noise. This study aims to evaluate how reconstruction algorithms and lesion radioactivity levels affect PET image quality and quantitative accuracy across three different PET systems. Materials and Methods: A NEMA phantom was filled with 18F and scanned by one PET/MRI and two PET/CT systems with sphere-to-background concentration ratio (SBR) of 2:1, 4:1, or 10:1. PET images were reconstructed with OSEM or BPL with TOF. The number of iterations and β-values were varied, while the matrix size, number of subsets, and filter size remained constant. Contrast recovery (CR) and background variability (BV) were measured in images. Results: CR increased with increased sphere size and SBR. CR and BV decreased with increased β for the 10mm sphere. Increased number of iterations in OSEM showed increased BV with limited variation in CR. BPL gave higher CR and lower BV values than OSEM. The optimal reconstruction was BPL with β between 150 and 350, where BPL was available, and OSEM with two iterations and 21 subsets for the PET/CT without BPL. Conclusion: BPL outperforms OSEM, and SBR significantly influences tracer uptake quantification in small lesions. Future studies should explore the clinical implications of these findings on diagnosis, staging, prognosis, and treatment follow-up.