A generalised spatio-temporal registration framework for dynamic PET data: Application to neuroreceptor imaging
Jiao J, Schnabel JA, Gunn RN (2013)
Publication Type: Conference contribution
Publication year: 2013
Journal
Book Volume: 8149 LNCS
Pages Range: 211-218
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: JPN
ISBN: 9783642408106
DOI: 10.1007/978-3-642-40811-3_27
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. © 2013 Springer-Verlag.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Jiao, J., Schnabel, J.A., & Gunn, R.N. (2013). A generalised spatio-temporal registration framework for dynamic PET data: Application to neuroreceptor imaging. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 211-218). JPN.
MLA:
Jiao, Jieqing, Julia A. Schnabel, and Roger N. Gunn. "A generalised spatio-temporal registration framework for dynamic PET data: Application to neuroreceptor imaging." Proceedings of the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, JPN 2013. 211-218.
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