Introduction: We investigate a general approach to generate parametric maps that consists in a multi-stage hierarchical scheme whereas starting from the kinetic analysis of the whole brain we then cascade the kinetic information to anatomical systems that are akin in terms of receptor densities and then down to the pixel level. A-priori classes of voxels are generated either by anatomical segmentation or by functional segmentation using unsupervised clustering. Kinetic properties are then transmitted to the voxels in each class using Maximum a Posteriori (MAP) estimation approaches. We validate the algorithm on a test–retest data-sets of [C]diprenorphine (DPN), which represents a challenge to estimation given its slow equilibration in tissue. We further offer internal validation by comparing resulting parametric maps generated from the anatomical and functional a-priori segmentation.