The low spatial resolution of PET scanners results in partial volume (PV) effects which limit the accurate quantification in anatomical structures such as the cerebral cortex and subcortical regions. Several correction methods have been proposed to deal with these effects. In a first study 1, using simulated data, we compared the PV correction of some cortical regions, obtained with i) the correction algorithm implemented at the Research Center Juelich (PVC-J) and ii) the one implemented at the Brain Imaging Centre of Montreal (PVC-M). Here, still using simulated data, the impact of mis-registration between the PET and the anatomical data on accuracy of the correction, as well as the correction of real dynamic PET data are presented. The PVC-J algorithm - based on the work of Muller-Gartner - has a fully 3D implementation. The resulting corrected grey matter (GM) activity images are obtained by dividing voxel-wise the uncorrected GM images (without white matter contribution) by the GM probability map, derived from the convolution of the corresponding MR segmented image by a 3D spatially variant gaussian function, which reproduces the actual PET image resolution. The PVC-M algorithm - based on the work of Rousset - accounts for the mutual PV effects between any possible tissue structure. After tissue classification of the high resolution MR data, cerebral structures of interest are chosen. Each structure is convolved with a spatially invariant 3D gaussian kernel yielding a set of 3D probability maps, from which recovery and cross-contamination factors are computed. Measured time-activity curves are finally corrected using the correction factor set. A PET dynamic acquisition of an adenosin receptor study was first corrected by both methods and furthermore used to build simulated dynamic data 1 which were translated and rotated by 1 & 2 mm and 1 & 2 degrees, respectively, with respect to the simulated MR image. In case of real data, the mean recovery factor over the dynamic data (6–90 min) of GM is 1.360.05 for PVC-M and 1.410.01 for PVC-J, while in the thalamus region it is 1.170.03 for PVC-M and 1.220.02 for PVC-J. For the mismatched data, the relative difference between the corrected activity values, obtained with the two correction algorithms, and the references was compared. In all but the rotation about the z-axis case, the results are highly consistent. For the rotations about the z axis, the values given by PVC-M and PVC-J show a difference up to about 2.0%. Furthermore, some curves show an instability in the values during the last 30 minutes of the dynamic series, independent from region, mismatch, and method. In this time period the receptor data are affected by lower statistics and low signal-to-contrast ratio. As in the first comparison study, the presented data show an overall high consistency of the results obtained from the two different methods concerning the adenosine receptor. Some of the observed discrepancies could result from the ways the time activity curves are computed with both correction algorithms.