Multi-Scale hierarchical generation of PET parametric maps: application and testing on a [11C]DPN study |
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Authors: | Rizzo G Turkheimer F E Keihaninejad S Bose S K Hammers A Bertoldo A |
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Institution: | a Department of Information Engineering, University of Padova, Padova, Italyb Division of Experimental Medicine, Imperial College London, London, UKc MRC Clinical Science Centre, Imperial College London, London, UKd The Neurodis Foundation (Fondation Neurodis), Lyon, France |
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Abstract: | We propose a general approach to generate parametric maps. It consists in a multi-stage hierarchical scheme where, 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 voxel level. A-priori classes of voxels are generated either by anatomical atlas segmentation or by functional segmentation using unsupervised clustering. Kinetic properties are transmitted to the voxels in each class using maximum a posteriori (MAP) estimation method. We validate the novel method on a 11C]diprenorphine (DPN) test-retest data-set that represents a challenge to estimation given 11C]DPN's slow equilibration in tissue.The estimated parametric maps of volume of distribution (VT) reflect the opioid receptor distributions known from previous 11C]DPN studies. When priors are derived from the anatomical atlas, there is an excellent agreement and strong correlation among voxel MAP and ROI results and excellent test-retest reliability for all subjects but one. Voxel level results did not change when priors were defined through unsupervised clustering.This new method is fast (i.e. 15 min per subject) and applied to 11C]DPN data achieves accurate quantification of VT as well as high quality VT images. Moreover, the way the priors are defined (i.e. using an anatomical atlas or unsupervised clustering) does not affect the estimates. |
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Keywords: | Positron emission tomography Parametric imaging Bayesian estimation |
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