2012
DOI: 10.2967/jnumed.111.095240
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Methods for Motion Correction Evaluation Using 18F-FDG Human Brain Scans on a High-Resolution PET Scanner

Abstract: Many authors have reported the importance of motion correction (MC) for PET. Patient motion during scanning disturbs kinetic analysis and degrades resolution. In addition, using misaligned transmission for attenuation and scatter correction may produce regional quantification bias in the reconstructed emission images. The purpose of this work was the development of quality control (QC) methods for MC procedures based on external motion tracking (EMT ) for human scanning using an optical motion tracking system.… Show more

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Cited by 38 publications
(30 citation statements)
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“…Emission PET scans were reconstructed using the iterative ordered-subset expectation-maximization algorithm correcting for attenuation, scatter, randoms and dead-time [45] and including inter-frame head motion correction including transmission-emission alignment for the individual frames [46]. The radioactivity was corrected for physical decay to the injection time.…”
Section: Methodsmentioning
confidence: 99%
“…Emission PET scans were reconstructed using the iterative ordered-subset expectation-maximization algorithm correcting for attenuation, scatter, randoms and dead-time [45] and including inter-frame head motion correction including transmission-emission alignment for the individual frames [46]. The radioactivity was corrected for physical decay to the injection time.…”
Section: Methodsmentioning
confidence: 99%
“…Such motion can occur either between frames or within frames and can cause significant changes in voxel-wise time-activity curves (TACs), especially on the boundaries of regions with significantly different kinetics and high activity gradient. Such blurring across frames could subsequently lead to blurring of the kinetic parameter maps (Herzog et al 2005, Dinelle et al 2011, Keller et al 2012 or even affect the extraction of image-derived input functions from the dynamic emission data (Mourik et al 2011). Furthermore, patient movement will most likely cause an emission/attenuation mismatch in the affected frames, resulting in errors during attenuation correction and scatter estimation (Anton-Rodriguez et al 2010, Häggström et al 2014.…”
Section: Introductionmentioning
confidence: 99%
“…Most important, it eliminates the effect of plastic walls that most other phantoms have, thus making it more similar to the patient geometry. Compared with this phantom, patient images can be expected to give slightly degraded results due to motion, such as from breathing, although many studies are performed using motion correction (29)(30)(31). Another difference is that the phantom has a homogeneous activity concentration within both the tumor and the background, which usually is not the case in patient images.…”
Section: Discussionmentioning
confidence: 99%