Gabriel Richard1, Christophe Noll1, Mélanie Archambault1, Luc Tremblay1, Serge Phoenix1, Samia Ait-Mohand1, Réjean Lebel1, Brigitte Guérin1, André C. Carpentier1, and Martin Lepage1
1Université de Sherbrooke, Sherbrooke, QC, Canada
Synopsis
Brown adipose tissue (BAT) oxidative metabolism can be
measured by 11C-acetate PET with a 3-tissue pharmacokinetic model.
However, this model can have trouble distinguishing between increased oxidation
and increased blood volume, both of which occur in active BAT. A sequential
DCE-MRI and 11C-acetate PET protocol was performed in male Wistar
rats with and without BAT activation. DCE-MRI perfusion measures were comparable
to those obtained previously with 68Ga-DOTA PET. Incorporating the
DCE-MRI blood volume information into the 11C-acetate model revealed
higher oxidation in activated BAT than indicated by the unconstrained model.
INTRODUCTION
The PET radiotracer 11C-acetate assesses oxidative
metabolism which is directly linked to thermogenesis, rather than ATP production, in brown adipose tissue (BAT)1. This measure
of thermogenesis is essential to characterize anti-obesity drugs aimed at
increasing energy expenditure through BAT.
11C-acetate is metabolized quickly after
injection (first ~5 minutes)2. Therefore,
oxidation and perfusion (first-pass bolus) signals are intermingled (Fig. 1).
Moreover, BAT activation increases both blood volume and oxidation making it
impossible to measure the change in oxidation with a pharmacokinetic model
without prior information about blood volume3.
This work measures
perfusion with gadobutrol DCE-MRI in active and inactive BAT. The 11C-acetate
pharmacokinetic model is then constrained using these data. Finally, the
perfusion measure is validated against 68Ga-DOTA PET4,5 to account for
differences between MRI and PET (e.g. methods to calculate imaging probe
concentration in a tissue).METHODS
The experimental protocol is summarized on Figure 2. Briefly,
6-week old male Wistar rats (N = 10) underwent sequential DCE-MRI (7 T
Varian) and 11C-acetate PET/CT (LabPET-8) twice, once after BAT
activation (48 h at 10 °C and injection of β3-agonist) and
once after inhibition (48 h at 30 °C and continuous heating on
scanners). The MRI-PET sessions were separated by 3 weeks (n = 6
animals started with BAT activation, n = 4 started with inhibition).
An additional group of 4 rats were scanned in a PET only protocol with perfusion measured by 68Ga-DOTA. Animals
where anesthetized with isoflurane (1.5 %).
Concentration maps were generated for DCE-MRI using
the T1 map and post-contrast images (17 s time resolution). PET images
were reconstructed using 3D maximum likelihood estimation method and the following
time resolution: 1x30 s, 12x10 s, 8x30 s, 10x1 min, and 1x4 min.
Regions of interest were drawn by hand on the
interscapular BAT depot based on the pre-contrast images for DCE-MRI or sum
image of all PET frames and anatomical CT. Pharmacokinetic modeling using
appropriate compartment models (1‑tissue for gadobutrol6 and 68Ga-DOTA5; 3-tissue for 11C-acetate3) was performed
in MATLAB. The arterial input function was derived using a region of reference
for MRI7, and blood
counter data (Gamma Medica-Ideas) for PET.
Finally, Pearson correlations of pharmacokinetic
parameters for gadobutrol/68Ga-DOTA vs. 11C-acetate
were examined. The effect of BAT activation on parameters was assessed by
paired t-test with significance level set at p ≤ 0.05.RESULTS
Figure 3 shows examples of DCE-MRI and PET
concentration maps.
Figure 4A compares the signals from DCE-MRI and 68Ga-DOTA
PET. As expected, perfusion is higher in active BAT than inactive BAT. Average
signals in active or inactive BAT are the same for both probes, but DCE-MRI
results have larger inter-individual variations. A similar increase in signal
for active BAT is observed with 11C-acetate (Fig. 4B). However,
the 11C-acetate signal differs from the gadobutrol or 68Ga-DOTA
signals by the presence of a large initial peak.
Correlations were found for the rate of entry into the
tissue (K1, r = 0.51) and the tissue blood
volume fraction (vb, r = 0.57) of 11C-acetate
and gadobutrol. The 68Ga-DOTA cohort was too small to be analyzed
for correlations but showed similar tendencies. With a fixed K1, 11C-acetate
data fit quality (based on residual plots) was poor. Table 1 shows fit results for
the non-constrained model and the vb-constrained model. The a
priori information leads to an increase in k2 (oxidation)
only for cold-exposed animals (active BAT) and does not affect inter-subject
variability. However, the difference in oxidation between cold (active) and warm (inactive) conditions is not significant even with the DCE-constrained model
(unconstrained p = 0.59; constrained p = 0.081).DISCUSSION
Constraining the 11C-acetate
pharmacokinetic model using vb derived from DCE-MRI reveals
the expected increase in oxidation for active BAT which is otherwise
interpreted as increased blood volume. The mean blood volume fractions inferred
using DCE-MRI (6.6 % ± 2.7 % in active BAT and 3.5 % ± 1.6 %
in inactive BAT) are similar to those measured by 15O-H2O
PET in humans8. However, in
this cohort, the increase in oxidation is not significant, and both DCE-MRI and
11C-acetate show high variability in cold-exposed animals. Possible
causes are variations in animal age/weight during the 3-week study, individual responses to cold/β3-agonist, and temperature fluctuations during the scans (which seem to affect perfusion
irrespective of metabolism). Experiments are underway to better control these
parameters and obtain a direct comparison with 68Ga-DOTA.CONCLUSION
Blood volume estimation by DCE-MRI improves the assessment of oxidative
metabolism with 11C-acetate in BAT. This technique would be
particularly useful in simultaneous PET-MRI to assess blood volume changes under
different pharmacological agents.Acknowledgements
The authors want to thank
the Sherbrooke Molecular Imaging Center for access to PET scanners as well as
the PET operators Jean-François Beaudoin and Maxime Paillé for their technical
expertise.References
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