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Joint parametric reconstruction and motion correction framework for dynamic PET data

Abstract : In this paper we propose a novel algorithm for jointly performing data based motion correction and direct parametric reconstruction of dynamic PET data. We derive a closed form update for the penalised likelihood maximisation which greatly enhances the algorithm’s computational efficiency for practical use. Our algorithm achieves sub-voxel motion correction residual with noisy data in the simulation-based validation and reduces the bias of the direct estimation of the kinetic parameter of interest. A preliminary evaluation on clinical brain data using [18F]Choline shows improved contrast for regions of high activity. The proposed method is based on a data-driven kinetic modelling method and is directly applicable to reversible and irreversible PET tracers, covering a range of clinical applications.
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https://hal.inria.fr/hal-01827218
Contributor : Ninon Burgos <>
Submitted on : Sunday, July 1, 2018 - 10:43:04 PM
Last modification on : Friday, January 15, 2021 - 3:33:38 AM

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Jieqing Jiao, Alexandre Bousse, Kris Thielemans, Pawel Markiewicz, Ninon Burgos, et al.. Joint parametric reconstruction and motion correction framework for dynamic PET data. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Sep 2014, Boston, United States. pp.114-121, ⟨10.1007/978-3-319-10404-1_15⟩. ⟨hal-01827218⟩

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