Simulation-based Evaluation of Iterative Reconstructions in Dynamic [18F]MPPF PET studies

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