Kinetic modeling of PET data derived from mouse models remains hampered by the technical inaccessibility of an accurate input function (IF). In this work, we tested the feasibility of IF measurement with an arteriovenous shunt and a coincidence counter in mice and compared the method with an image-derived IF (IDIF) obtained by ensemble-learning independent component analysis of the heart region.
(18)F-FDG brain kinetics were quantified in 2 mouse strains, CD1 and C57BL/6, using the standard 2-tissue-compartment model. Fits obtained with the 2 IFs were compared regarding their goodness of fit as assessed by the residuals, fit parameter SD, and Bland-Altman analysis.
On average, cerebral glucose metabolic rate was 10% higher for IDIF-based quantification. The precision of model parameter fitting was significantly higher using the shunt-based IF, rendering the quantification of single process rate constants feasible.
We demonstrated that the arterial IF can be measured in mice with a femoral arteriovenous shunt. This technique resulted in higher precision for kinetic modeling parameters than did use of the IDIF. However, for longitudinal or high-throughput studies, the use of a minimally invasive IDIF based on ensemble-learning independent component analysis represents a suitable alternative.