title: 4-D generative model for PET/MRI reconstruction.
abstract: We introduce a 4-dimensional joint generative probabilistic model for estimation of activity in a PET/MRI imaging system. The model is based on a mixture of Gaussians, relating time dependent activity and MRI image intensity to a hidden static variable, allowing one to estimate jointly activity, the parameters that capture the interdependence of the two images and motion parameters. An iterative algorithm for optimisation of the model is described. Noisy simulation data, modeling 3-D patient head movements, is obtained with realistic PET and MRI simulators and with a brain phantom from the BrainWeb database. Joint estimation of activity and motion parameters within the same framework allows us to use information from the MRI images to improve the activity estimate in terms of noise and recovery.
authors: The Centre for Medial Image Computing, UCL, London, United Kingdom.
authors: Stefano
authors: S
authors: Pedemonte
authors: Alexandre
authors: A
authors: Bousse
authors: Brian F
authors: BF
authors: Hutton
authors: Simon
authors: S
authors: Arridge
authors: Sebastien
authors: S
authors: Ourselin
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D009038:Motion; D016011:Normal Distribution; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods*; D012984:Software