Optimization of the k(2)' Parameter Estimation for the Pharmacokinetic Modeling of Dynamic PIB PET Scans Using SRTM2Peretti, D. E., Reesink, F. E., Doorduin, J., de Jong, B. M., De Deyn, P. P., Dierckx, R. A. J. O., Boellaard, R. & Garcia, D. V., 12-Dec-2019, In : Frontiers of Physics. 7, 11 p., 212.
Research output: Contribution to journal › Article › Academic › peer-review
Background: This study explores different approaches to estimate the clearance rate of the reference tissue (k2 ') parameter used for pharmacokinetic modeling, using the simplified reference tissue model 2 (SRMT2) and further explores the effect on the binding potential (BPND) of C-11-labeled 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 ': the whole brain, eight anatomical region based on the Hammer's atlas, one region based on a SPM comparison between groups on a voxel level, and three regions using different BPNDSRTM thresholds. Results: The different approaches resulted in distinct k2 ' estimations per subject. The median value of the estimated k2 ' across all subjects in the whole brain was 0.057. In general, PIB+ subjects presented smaller k2 ' estimates than this median, and PIB-, larger. Furthermore, only threshold and white matter methods resulted in non-significant differences between groups. Moreover, threshold approaches yielded the best correlation between BPNDSRTM and BPNDSRTM2 for both groups (R-2 = 0.85 for PIB+, and R-2 = 0.88 for PIB-). Lastly, a sensitivity analysis showed that overestimating k2 ' values resulted in less biased BPNDSRTM2 estimates. Conclusion: Setting a threshold on BPNDSRTM might be the best method to estimate k2 ' in voxel-based modeling approaches, while the use of a white matter region might be a better option for a volume of interest based analysis.
|Number of pages||11|
|Journal||Frontiers of Physics|
|Publication status||Published - 12-Dec-2019|
- Alzheimer's disease, pharmacokinetic modeling, Pittsburgh compound B, SRTM, SRTM2, REFERENCE TISSUE MODEL, ALZHEIMERS-DISEASE, SENILE PLAQUES, HUMAN BRAIN, DIAGNOSIS, BINDING, CEREBELLUM, DEMENTIA