title: Direct Parametric Reconstruction With Joint Motion Estimation/Correction for Dynamic Brain PET Data.
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.
authors: Jieqing
authors: J
authors: Jiao
authors: Alexandre
authors: A
authors: Bousse
authors: Kris
authors: K
authors: Thielemans
authors: Ninon
authors: N
authors: Burgos
authors: Philip S J
authors: PS
authors: Weston
authors: Jonathan M
authors: JM
authors: Schott
authors: David
authors: D
authors: Atkinson
authors: Simon R
authors: SR
authors: Arridge
authors: Brian F
authors: BF
authors: Hutton
authors: Pawel
authors: P
authors: Markiewicz
authors: Sebastien
authors: S
authors: Ourselin
mesh_terms: D000465:Algorithms; D001921:Brain; D006801:Humans; D007091:Image Processing, Computer-Assisted; D009038:Motion; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography