Ralph Buchert

Feasibility of central cannabinoid CB1 receptor imaging with [124I]AM281 PET demonstrated in a schizophrenic patient.

We studied central cannabinoid CB1 receptors in a schizophrenic patient using the pyrazole derivative AM281 labelled with the positron-emitting nuclide iodine-124. A dynamic positron emission tomography (PET) acquisition with simultaneous blood sampling was performed up to 1.5 h post-injection. The classical Logan plot analysis was applied to generate a three-dimensional map of distribution volume (DV). The map was spatially normalised into the Montreal Neurological Institute stereotactic space. Using a volume of interest (VOI) template, mean values of DV were extracted from multiple grey matter regions and white matter (as a reference). As a measure of regional receptor availability, ratios of DV in grey matter to DV in white matter minus one (DVR-1) were calculated. The highest receptor binding was observed in the striatum and the pallidum (DVR-1: 0.35-0.37). Binding in basal ganglia regions was lower on the left than the right side. Moderately high binding was seen in the frontal cortex (0.22), the temporal cortex (0.18) and the cerebellum (0.15). In conclusion, 124I-AM281 PET can be used to reveal areas with prominent CB1 receptor binding. Nevertheless, limited image contrast and relatively high radiation exposure (physical half-life of 124I: 4 days) have to be taken into account. Asymmetric receptor binding may possibly reflect pathologic changes in schizophrenia.

Optimal feature selection for automated classification of FDG-PET in patients with suspected dementia

FDG-PET is increasingly used for the evaluation of dementia patients, as major neurodegenerative disorders, such as Alzheimers disease (AD), Lewy body dementia (LBD), and Frontotemporal dementia (FTD), have been shown to induce specific patterns of regional hypo-metabolism. However, the interpretation of FDG-PET images of patients with suspected dementia is not straightforward, since patients are imaged at different stages of progression of neurodegenerative disease, and the indications of reduced metabolism due to neurodegenerative disease appear slowly over time. Furthermore, different diseases can cause rather similar patterns of hypo-metabolism. Therefore, classification of FDG-PET images of patients with suspected dementia may lead to misdiagnosis. This work aims to find an optimal subset of features for automated classification, in order to improve classification accuracy of FDG-PET images in patients with suspected dementia. A novel feature selection method is proposed, and performance is compared to existing methods. The proposed approach adopts a combination of balanced class distributions and feature selection methods. This is demonstrated to provide high classification accuracy for classification of FDG-PET brain images of normal controls and dementia patients, comparable with alternative approaches, and provides a compact set of features selected.