MICCAI 2013 - LNCS 8149-8151

A Generalised Spatio-Temporal Registration Framework for Dynamic PET Data: Application to Neuroreceptor Imaging

Jieqing Jiao^{1, 2}, Julia A. Schnabel¹, and Roger N. Gunn^{1, 2, 3}

¹Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK

²Imanova Limited, Hammersmith Hospital, London, UK

³Department of Medicine, Imperial College, London, UK

Abstract. This work presents a novel pharmacokinetic model based registration algorithm for the motion correction of dynamic positron emission tomography (PET) images. The algorithm employs a generalised model that derives the input function from the tomographic data itself to model the PET tracer kinetics and thus eliminates the need of arterial blood sampling. Both the temporal constraint from the tracer kinetic behaviour and spatial constraint from the image similarity are integrated in a joint probabilistic model, in which the subject motion and tracer kinetic parameters are iteratively optimised, leading to a groupwise registration framework of motion corrupted dynamic PET data. The algorithm is evaluated with simulated and measured human dopamine D3 receptor imaging data using [¹¹C]-(+)-PHNO. The simulation-based validation demonstrates that the new algorithm has a subvoxel registration accuracy on average for noisy data with simulated motion artefacts. The algorithm also shows reductions in motion on initial experiments with measured clinical [¹¹C]-(+)-PHNO brain data.

Keywords: Groupwise spatio-temporal registration, dynamic PET, motion correction, basis pursuit denoising

LNCS 8149, p. 211 ff.

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