Mapping 18F-FDG Kinetics Together with Patient-Specific Bootstrap Assessment of Uncertainties: An Illustration with Data from a PET/CT Scanner with a Long Axial Field of View

The purpose of this study was to examine a nonparametric approach to mapping kinetic parameters and their uncertainties with data from the emerging generation of dynamic whole-body PET/CT scanners. Methods: Dynamic PET 18F-FDG data from a set of 24 cancer patients studied on a long-axial-field-of-view PET/CT scanner were considered. Kinetics were mapped using a nonparametric residue mapping (NPRM) technique. Uncertainties were evaluated using an image-based bootstrapping methodology. Kinetics and bootstrap-derived uncertainties are reported for voxels, maximum-intensity projections, and volumes of interest (VOIs) corresponding to several key organs and lesions. Comparisons between NPRM and standard 2-compartment (2C) modeling of VOI kinetics are carefully examined. Results: NPRM-generated kinetic maps were of good quality and well aligned with vascular and metabolic 18F-FDG patterns, reasonable for the range of VOIs considered. On a single 3.2-GHz processor, the specification of the bootstrapping model took 140 min; individual bootstrap replicates required 80 min each. VOI time-course data were much more accurately represented, particularly in the early time course, by NPRM than by 2C modeling constructs, and improvements in fit were statistically highly significant. Although 18F-FDG flux values evaluated by NPRM and 2C modeling were generally similar, significant deviations between vascular blood and distribution volume estimates were found. The bootstrap enables the assessment of quite complex summaries of mapped kinetics. This is illustrated with maximum-intensity maps of kinetics and their uncertainties. Conclusion: NPRM kinetics combined with image-domain bootstrapping is practical with large whole-body dynamic 18F-FDG datasets. The information provided by bootstrapping could support more sophisticated uses of PET biomarkers used in clinical decision-making for the individual patient.