title: A Variational Bayesian inference method for parametric imaging of PET data.
abstract: In dynamic Positron Emission Tomography (PET) studies, compartmental models provide the richest information on the tracer kinetics of the tissue. Inverting such models at the voxel level is however quite challenging due to the low signal-to-noise ratio of the time activity curves. In this study, we propose the use of a Variational Bayesian (VB) approach to efficiently solve this issue and thus obtain robust quantitative parametric maps. VB was adapted to the non-uniform noise distribution of PET data. Moreover, we propose a novel hierarchical scheme to define the model parameter priors directly from the images in case such information are not available from the literature, as often happens with new PET tracers. VB was initially tested on synthetic data generated using compartmental models of increasing complexity, providing accurate (%bias<2%±2%, root mean square error<15%±5%) parameter estimates. When applied to real data on a paradigmatic set of PET tracers (L-[1-11C]leucine, [11C]WAY100635 and [18F]FDG), VB was able to generate reliable parametric maps even in presence of high noise in the data (unreliable estimates<11%±5%).
authors.affiliation: Department of Information Engineering, University of Padova, Italy.
authors.forename: M
authors.initials: M
authors.lastname: Castellaro
authors.affiliation: Department of Information Engineering, University of Padova, Italy.
authors.forename: G
authors.initials: G
authors.lastname: Rizzo
authors.affiliation: Department of Information Engineering, University of Padova, Italy.
authors.forename: M
authors.initials: M
authors.lastname: Tonietto
authors.affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
authors.forename: M
authors.initials: M
authors.lastname: Veronese
authors.affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
authors.forename: F E
authors.initials: FE
authors.lastname: Turkheimer
authors.affiliation: Institute of Biomedical Engineering, University of Oxford, Old Road Campus Research Building, Old Road Campus, Headington, Oxford, United Kingdom.
authors.forename: M A
authors.initials: MA
authors.lastname: Chappell
authors.affiliation: Department of Information Engineering, University of Padova, Italy. Electronic address: bertoldo@dei.unipd.it.
authors.forename: A
authors.initials: A
authors.lastname: Bertoldo
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008959:Models, Neurological; D008962:Models, Theoretical; D049268:Positron-Emission Tomography / Q000379:methods*; D059629:Signal-To-Noise Ratio
keywords: L[1-(11)C]leucine; Positron Emission Tomography; Variational Bayes; Voxel-wise kinetic analysis; [(11)C]WAY-100635; [(18)F]FDG