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Resolution modeling in PET imaging: theory, practice, benefits, and pitfalls.

Medical physics

Sensitivity and Specificity, Algorithms, Computer Simulation, Image Enhancement, methods, Image Interpretation, Computer-Assisted, Models, Biological, Models, Statistical, Positron-Emission Tomography, Reproducibility of Results

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      In this paper, the authors review the field of resolution modeling in positron emission tomography (PET) image reconstruction, also referred to as point-spread-function modeling. The review includes theoretical analysis of the resolution modeling framework as well as an overview of various approaches in the literature. It also discusses potential advantages gained via this approach, as discussed with reference to various metrics and tasks, including lesion detection observer studies. Furthermore, attention is paid to issues arising from this approach including the pervasive problem of edge artifacts, as well as explanation and potential remedies for this phenomenon. Furthermore, the authors emphasize limitations encountered in the context of quantitative PET imaging, wherein increased intervoxel correlations due to resolution modeling can lead to significant loss of precision (reproducibility) for small regions of interest, which can be a considerable pitfall depending on the task of interest.

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