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Joint Parametric Reconstruction and Motion Correction Framework for Dynamic PET Data

  • Jieqing Jiao
  • Alexandre Bousse
  • Kris Thielemans
  • Pawel Markiewicz
  • Ninon Burgos
  • David Atkinson
  • Simon Arridge
  • Brian F. Hutton
  • Sébastien Ourselin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

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.

Keywords

Dynamic PET direct parametric reconstruction motion correction optimisation transfer kinetic analysis 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jieqing Jiao
    • 1
  • Alexandre Bousse
    • 2
  • Kris Thielemans
    • 2
  • Pawel Markiewicz
    • 1
  • Ninon Burgos
    • 1
  • David Atkinson
    • 3
  • Simon Arridge
    • 1
  • Brian F. Hutton
    • 2
  • Sébastien Ourselin
    • 1
    • 4
  1. 1.Translational Imaging Group, Centre for Medical Image ComputingUCLUK
  2. 2.Institute of Nuclear MedicineUniversity College LondonUK
  3. 3.Centre for Medical ImagingUniversity College LondonUK
  4. 4.Dementia Research Centre, Institute of NeurologyUniversity College LondonQueen SquareUK

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