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Application of image-derived and venous input functions in major depression using [carbonyl-(11)C]WAY-100635
Andreas Hahn1, Lukas Nics, Pia Baldinger
1Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria.
Introduction:
Image-derived input functions (IDIFs) represent a promising non-invasive alternative to arterial blood sampling for quantification in positron emission tomography (PET) studies. However, routine applications in patients and longitudinal designs are largely missing despite widespread attempts in healthy subjects. The aim of this study was to apply a previously validated approach to a clinical sample of patients with major depressive disorder (MDD) before and after electroconvulsive therapy (ECT).
Methods:
Eleven scans from 5 patients with venous blood sampling were obtained with the radioligand [carbonyl-(11)C]WAY-100635 at baseline, before and after 11.0±1.2 ECT sessions. IDIFs were defined by two different image reconstruction algorithms 1) OSEM with subsequent partial volume correction (OSEM+PVC) and 2) reconstruction based modelling of the point spread function (TrueX). Serotonin-1A receptor (5-HT1A) binding potentials (BPP, BPND) were quantified with a two-tissue compartment (2TCM) and reference region model (MRTM2).
Results:
Compared to MRTM2, good agreement in 5-HT1A BPND was found when using input functions from OSEM+PVC (R(2)=0.82) but not TrueX (R(2)=0.57, p<0.001), which is further reflected by lower IDIF peaks for TrueX (p<0.001). Following ECT, decreased 5-HT1A BPND and BPP were found with the 2TCM using OSEM+PVC (23%-35%), except for one patient showing only subtle changes. In contrast, MRTM2 and IDIFs from TrueX gave unstable results for this patient, most probably due to a 2.4-fold underestimation of non-specific binding.
Conclusions:
Using image-derived and venous input functions defined by OSEM with subsequent PVC we confirm previously reported decreases in 5-HT1A binding in MDD patients after ECT. In contrast to reference region modeling, quantification with image-derived input functions showed consistent results in a clinical setting due to accurate modeling of non-specific binding with OSEM+PVC.
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