Imaging dopamine receptors in the rat striatum with the MicroPET R4: kinetic analysis of [11C]raclopride binding using graphical methods

Cue-induced dopamine release predicts cocaine preference: positron emission tomography studies in freely moving rodents

Positron emission tomography studies in drug-addicted patients have shown that exposure to drug-related cues increases striatal dopamine, which displaces binding of the D(2) ligand, [(11)C]-raclopride. However, it is not known if animals will also show cue-induced displacement of [(11)C]-raclopride binding. In this study, we use [(11)C]-raclopride imaging in awake rodents to capture cue-induced changes in dopamine release associated with the conditioned place preference model of drug craving. Ten animals were conditioned to receive cocaine in a contextually distinct environment from where they received saline. Following conditioning, each animal was tested for preference and then received two separate [(11)C]-raclopride scans. For each scan, animals were confined to the cocaine and/or the saline-paired environment for the first 25 min of uptake, after which they were anesthetized and scanned. [(11)C]-raclopride uptake in the saline-paired environment served as a within-animal control for uptake in the cocaine-paired environment. Cocaine produced a significant place preference (p = 0.004) and exposure to the cocaine-paired environment decreased [(11)C]-raclopride binding relative to the saline-paired environment in both the dorsal (20%; p < 0.002) and ventral striatum (22%; p < 0.05). The change in [(11)C]-raclopride binding correlated with preference in the ventral striatum (R(2) = -0.87; p = 0.003). In this region, animals who showed little or no preference exhibited little or no change in [(11)C]-raclopride binding in the cocaine-paired environment. This noninvasive procedure of monitoring neurochemical events in freely moving, behaving animals advances preclinical molecular imaging by interrogating the degree to which animal models reflect the human condition on multiple dimensions, both biological and behavioral.

Wavelet denoising in voxel-based parametric estimation of small animal PET images: a systematic evaluation of spatial constraints and noise reduction algorithms

Voxel-based estimation of PET images, generally referred to as parametric imaging, can provide invaluable information about the heterogeneity of an imaging agent in a given tissue. Due to high level of noise in dynamic images, however, the estimated parametric image is often noisy and unreliable. Several approaches have been developed to address this challenge, including spatial noise reduction techniques, cluster analysis and spatial constrained weighted nonlinear least-square (SCWNLS) methods. In this study, we develop and test several noise reduction techniques combined with SCWNLS using simulated dynamic PET images. Both spatial smoothing filters and wavelet-based noise reduction techniques are investigated. In addition, 12 different parametric imaging methods are compared using simulated data. With the combination of noise reduction techniques and SCWNLS methods, more accurate parameter estimation can be achieved than with either of the two techniques alone. A less than 10% relative root-mean-square error is achieved with the combined approach in the simulation study. The wavelet denoising based approach is less sensitive to noise and provides more accurate parameter estimation at higher noise levels. Further evaluation of the proposed methods is performed using actual small animal PET datasets. We expect that the proposed method would be useful for cardiac, neurological and oncologic applications.