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

*Corresponding author for this work

Research output: Contribution to journalConference paperpeer-review

6 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)114-121
Number of pages8
JournalMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Volume17
Issue numberPt 1
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, MA, United States
Duration: 14 Sep 201418 Sep 2014

Keywords

  • Dynamic PET
  • direct parametric reconstruction
  • motion correction
  • optimisation transfer
  • kinetic analysis

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