Population derived principle component analysis based model for the <tex>$\left[^{18}F\right]$</tex>PBR111 arterial input function in rats
Population derived principle component analysis based model for the <tex>$\left[^{18}F\right]$</tex>PBR111 arterial input function in rats
Faculty of Medicine and Health Sciences

conferenceObject

2013
New york :Ieee
, 2013

Physics

Computer. Automation

2013 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (NSS/MIC)

60th IEEE Nuclear Science Symposium (NSS) / Medical Imaging Conference, (MIC) / 20th International Workshop on Room-Temperature Semiconductor, X-ray and Gamma-ray Detectors, OCT 27-NOV 02, 2013, Seoul, SOUTH KOREA

(2013)
, 4 p.

978-1-4799-0534-8

000347163501207

E

English (eng)

University of Antwerp

Translocator protein (TSPO) PET ligands such as [F-18] PBR111 with high specificity for TSPO or formerly the peripheral benzodiazepine (PBR) receptor, are frequently used as a brain inflammation biomarker. In this study a population derived model for the metabolite corrected arterial plasma input function (IF) of [F-18] PBR111 in rat is developed. Such population derived models are of interest as they allow to obtain the IF using a limited number of samples and do not require draining the animal for blood. We have therefore developed and evaluated two models based on principle component analysis (PCA) of the population data. The first model follows the conventional approach and builds a model of the IF. The second model, introduced here, builds a model for the area under the curve (AUC) of the IF. The coefficients for this newly developed model are estimated using multivariate linear regression analysis of the population data. The two approaches are evaluated using in vivo data from rats (n=8) comprising both manual sampling and the use of an extracorporeal arterio-venous shunt and coincidence detector system. Optimal time points for the manual sampling of a single or of two samples are estimated. Performance is evaluated in terms of the error on the estimated AUC and the error on the estimated total volume of distribution calculated using the Logan graphical analysis. We find that the proposed PCA model for AUC outperforms the PCA model for IF. This is of interest for cases where one models the reversible tracer binding using the Logan plot that requires AUC(t) rather than IF(t).

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https://repository.uantwerpen.be/docman/iruaauth/1b7a4d/134448.pdf