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.
Highlights
D YNAMIC Positron Emission Tomography (PET) imaging plays an important role in medical research as it allows for the in vivo quantification of a wide range of biological parameters [1]–[3]
In this work we propose a framework to jointly estimate subject head motion and reconstruct the motioncorrected parametric images directly from raw positron emission tomography (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
Summary
D YNAMIC Positron Emission Tomography (PET) imaging plays an important role in medical research as it allows for the in vivo quantification of a wide range of biological parameters [1]–[3]. To derive the biological parameters of interest, conventionally the raw projection data recorded by PET detectors are divided and reconstructed into a series of temporal frames to provide the spatial distribution of the PET tracer over time, and the time activity curves on a voxel/region basis are extracted for kinetic analysis with a selected model. For such indirect methods, modelling the noise distribution in the kinetic analysis of the sequence of reconstructed activity images is difficult, and direct parametric reconstruction approaches [4]–[11] have been developed to reduce noise amplification in kinetic quantification, by incorporating the kinetic model into the reconstruction to derive the kinetic parameters directly from the raw PET data, with improved modelling of the noise statistics. This requires additional devices and calibrations, as well as synchronisation of motion tracking data with the dynamic PET data
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