Simulation-based evaluation of OSEM iterative reconstruction methods in dynamic brain PET studies

The reconstruction of dynamic PET data is usually performed using filtered backprojection algorithms (FBP). This method is fast, robust, linear and yields reliable quantitative results. However, the use of FBP for low count data, such as dynamic PET data, generally results in poor visual image quality, exhibiting high noise, disturbing streak artifacts and low contrast. These signal-to-noise ratio and contrast in the reconstructed images may alter the quantification of physiological indexes, such as the regional Binding Potential (BP) obtained from kinetic modeling. Iterative reconstruction methods are often presented as viable alternatives to FBP reconstruction. In this study, we investigated the characteristics of the UW-OSEM and the ANW-OSEM iterative reconstruction methods in the context of ligand-receptor PET studies with low counts. The assessment was conducted using replicates of simulated [18F]MPPF acquisitions. The quantitative accuracy obtained with the iterative and analytical methods was compared. The results show that analytical methods are more robust to the low count data than iterative methods, and therefore enable a better estimate of the regional activity values and binding potential. The positivity constraint in MLEM-based algorithms leads to overestimations of the activity in regions with low activity concentration, typically the cerebellum. This overestimation results in significant bias in BP estimates with iterative reconstruction methods. The bias is confirmed from the reconstruction of real PET data.