Optimization of the k2′ Parameter Estimation for the Pharmacokinetic Modeling of Dynamic PIB PET Scans Using SRTM2 - Peretti, Débora E.; Reesink, Fransje E.; Doorduin, Janine; de Jong, Bauke M.; De Deyn, Peter P.; Dierckx, Rudi A. J. O.; Boellaard, Ronald; Vállez García, David (Frontiers in Physics, 2019)
Background: This study explores different approaches for estimating the clearance rate of reference tissue (k2') parameter, used for pharmacokinetic modelling using the simplified reference tissue model 2 (SRMT2) and its effect on the binding potential (BPnd) of 11C-labelled Pittsburgh Compound B (PIB) PET scans. Methods: Thirty subjects underwent a dynamic PIB PET scan and were classified as PIB positive (+) or negative (-). Thirteen regions were defined from where to estimate k2': whole masked imagebrain, eight anatomical based on the Hammer’s atlas, one SPM voxel-based comparison between groups, and three using different BPnd(SRTM) thresholds. Results: The different approaches resulted in distinct k2' estimations. The median value of the estimated k2' across all subjects was of 0.057. In general, PIB+ subjects presented smaller k2' estimates than this median, and PIB-, larger. Furthermore, only grey matter and SPMthreshold and white matter methods resulted in non-significant bigger differences between groups in the k2' estimations. A sensitivity analysis was done by fixing k2' to a range of values and assessing the effects on BPnd(SRTM2), and a non-linear relationship between the parameters was observed. Furthermore, Tthreshold approaches yielded the best correlation between BPnd(SRTM) and BPnd(SRTM2) for both groups (R²=0.85 for PIB+, and R²=0.88 for PIB-). Lastly, sensitivity analysis shows that overestimating k2' values resulted in less bias in the BPnd(SRTM2) estimates. Conclusion: Results suggested that setting a threshold on BPnd(SRTM) might be the best method for estimating k2' in voxel-based modelling approaches, while the use of a white matter region of interest might be a better option for volume of interest-based analysis.