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Direct Parametric Reconstruction with Joint Motion Estimation/Correction for Dynamic Brain PET Data

Research output: Contribution to journalArticle

Jieqing Jiao, Alexandre Bousse, Kris Thielemans, Ninon Burgos, Philip S.J. Weston, Jonathan M. Schott, David Atkinson, Simon R. Arridge, Brian F. Hutton, Pawel Markiewicz, Sebastien Ourselin

Original languageEnglish
Article number7551132
Pages (from-to)203-213
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume36
Issue number1
Early online date24 Aug 2016
DOIs
Publication statusPublished - Jan 2017

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Abstract

Direct reconstruction of parametric images from raw photon counts has been shown to improve the quantitative analysis of dynamic positron emission tomography (PET) data. However it suffers from subject motion which is inevitable during the typical acquisition time of 1-2 hours. In this work we propose a framework to jointly estimate subject head motion and reconstruct the motion-corrected parametric images directly from raw PET data, so that the effects of distorted tissue-to-voxel mapping due to subject motion can be reduced in reconstructing the parametric images with motion-compensated attenuation correction and spatially aligned temporal PET data. The proposed approach is formulated within the maximum likelihood framework, and efficient solutions are derived for estimating subject motion and kinetic parameters from raw PET photon count data. Results from evaluations on simulated [11C]raclopride data using the Zubal brain phantom and real clinical [18F]florbetapir data of a patient with Alzheimer's disease show that the proposed joint direct parametric reconstruction motion correction approach can improve the accuracy of quantifying dynamic PET data with large subject motion.

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