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Jiao, J.* ; Schnabel, J.A.* ; Gunn, R.N.*

A generalised spatio-temporal registration framework for dynamic PET data: Application to neuroreceptor imaging.

In: (International Conference on Medical Image Computing and Computer-Assisted Intervention). Berlin [u.a.]: Springer, 2013. 211-218 (Lect. Notes Comput. Sc. ; 8149 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Basis Pursuit Denoising ; Dynamic Pet ; Groupwise Spatio-temporal Registration ; Motion Correction
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel International Conference on Medical Image Computing and Computer-Assisted Intervention
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 8149 LNCS, Heft: PART 1, Seiten: 211-218 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)