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4-D generative model for PET/MRI reconstruction

Pedemonte, S; Bousse, A; Hutton, BF; Arridge, S; Ourselin, S; (2011) 4-D generative model for PET/MRI reconstruction. Presented at: UNSPECIFIED, Germany.

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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.

Type: Conference item (UNSPECIFIED)
Title: 4-D generative model for PET/MRI reconstruction
Location: Germany
Language: English
Keywords: Algorithms, Bayes Theorem, Brain, Brain Mapping, Computer Simulation, Databases, Factual, Humans, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Markov Chains, Models, Statistical, Motion, Normal Distribution, Phantoms, Imaging, Positron-Emission Tomography, Software
UCL classification: UCL > Office of the President and Provost
UCL > School of Life and Medical Sciences
UCL > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
URI: http://discovery.ucl.ac.uk/id/eprint/1322292
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