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Publication Detail
Joint parametric reconstruction and motion correction framework for dynamic PET data.
  • Publication Type:
    Conference
  • Authors:
    Jiao J, Bousse A, Thielemans K, Markiewicz P, Burgos N, Atkinson D, Arridge S, Hutton BF, Ourselin S
  • Publication date:
    01/01/2014
  • Pagination:
    114, 121
  • Published proceedings:
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
  • Volume:
    17
  • Issue:
    Pt 1
  • Status:
    Published
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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