Quantitative measures of 11C-raclopride receptor binding can be used as a correlate of postsynaptic D2 receptor density in the striatum, allowing 11C-raclopride positron emission tomography (PET) to be used for the differentiation of Parkinson’s disease from atypical parkinsonian syndromes. Comparison with reference values is recommended to establish a reliable diagnosis. A PET template specific to raclopride may facilitate direct computation of parametric maps without the need for an additional MR scan, aiding automated image analysis.
Sixteen healthy volunteers underwent a dynamic 11C-raclopride PET and a high-resolution T1-weighted MR scan of the brain. PET data from eight healthy subjects was processed to generate a raclopride-specific PET template normalized to standard space. Subsequently, the data processing based on the PET template was validated against the standard magnetic resonance imaging (MRI)-based method in 8 healthy subjects and 20 patients with suspected parkinsonian syndrome. Semi-quantitative image analysis was performed in Montreal Neurological Institute (MNI) and in original image space (OIS) using VOIs derived from a probabilistic brain atlas previously validated by Hammers et al. (Hum Brain Mapp, 15:165–174, 2002).
The striatal-to-cerebellar ratio (SCR) of 11C-raclopride uptake obtained using the PET template was in good agreement with the MRI-based image processing method, yielding a Lin’s concordance coefficient of 0.87. Bland-Altman analysis showed that all measurements were within the ±1.96 standard deviation range. In all 20 patients, the PET template-based processing was successful and manual volume of interest optimization had no further impact on the diagnosis of PD in this patient group. A maximal difference of <5% was found between the measured SCR in MNI space and OIS.
The PET template-based method for automated quantification of postsynaptic D2 receptor density is simple to implement and facilitates rapid, robust and reliable image analysis. There was no significant difference between the SCR values obtained with either PET- or MRI-based image processing. The method presented alleviates the clinical workflow and facilitates automated image analysis.