Simulated data is an important tool for evaluation of reconstruction and image processing algorithms in the frequent absence of ground truth, in-vivo data from living subjects. This is especially true in the case of dynamic PET studies, in which counting statistics of the volume can vary widely over the time-course of the acquisition. Realistic simulated data-sets which model anatomy and physiology, and make explicit the spatial and temporal image acquisition characteristics, facilitate experimentation with a wide range of the conditions anticipated in practice, and which can severely challenge algorithm performance and reliability. As a first example, we have developed a realistic dynamic FDG-PET data-set using the PET-SORTEO Monte Carlo simulation code and the MNI digital brain phantom. The phantom is a three-dimensional data-set that defines the spatial distribution of different tissues. Time activity curves were calculated using an impulse response function specified by generally accepted rate constants, convolved with an input function obtained by blood sampling, and assigned to grey and white matter tissue regions. We created a dynamic PET study using PET-SORTEO configured to simulate an ECAT Exact HR+. The resulting sinograms were reconstructed with all corrections, using variations of FBP and OSEM. Having constructed the dynamic PET data-sets, we used them to evaluate the performance of intensity-based registration as part of a tool for quantifying hyper/hypo perfusion with particular application to analysis of brain dementia scans, and a study of the stability of kinetic parameter estimation.