Background. An important step in the analysis of positron emission tomography (PET) studies of the brain is the definition of regions of interest (ROI). Image coregistration, ROI analysis, and quantification of brain PET data in small animals can be observer dependent. The purpose of this study was to investigate the feasibility of ROI analysis based on a standard MR template and an additional [18F]NaF scan. Methods. [18F]NaF scans of 10 Wistar rats were coregistered with a standard MR template by 3 observers and derived transformation matrices were applied to corresponding [11C]AF150(S) images. Uptake measures were derived for several brain regions delineated using the MR template. Overall agreement between the 3 observers was assessed by interclass correlation coefficients (ICC) of uptake data. In addition, [11C]AF150(S) ROI data were compared with ex vivo biodistribution data. Results. For all brain regions, ICC analysis showed excellent agreement between observers. Reproducibility, estimated by calculation of standard deviation of the between-observer differences, was demonstrated by an average of 17% expressed as coefficient of variation. Uptake of [11C]AF150(S) derived from ROI analysis closely matched ex vivo biodistribution data. Conclusions. The proposed method provides a reproducible and tracer-independent method for ROI analysis of rat brain PET data. 1. Introduction An important step in the analysis of positron emission tomography (PET) data is the definition of regions of interest (ROI). For brain studies in man and large mammals, like primates and pigs, it becomes more common practice to use magnetic-resonance- (MR-) based templates [1–4]. For brain studies in small animals, for example, rats, several methods to define ROI have been proposed, for example, spatial normalization to an MR brain atlas [5], predefined PET templates [6, 7], direct ROI definition on PET images [8], ROI definition using a coregistered segmented rat brain atlas (based on autoradiography) [9], and probabilistic atlases based on PET data (e.g., [18F]FDG, [18F]FECT, and [11C]raclopride) for voxel-based functional mapping [10]. In case of small animals, automated image coregistration of PET data with MR templates is difficult, because of the relatively large difference in spatial resolution between MR and PET. In this study, small animal imaging was performed on a high-resolution PET scanner (i.e., ECAT high-resolution research tomograph, HRRT) with a spatial resolution of 2.5 to 3？mm and an MR scanner with a spatial resolution of 0.1？mm, resulting in an MR to PET
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