Variance reduction with synaptic density imaging in Parkinson’s disease using direct-4D PET image reconstruction
Paul Gravel, Jean-Dominique Gallezot, Kathryn Fontaine, David Matuskey, Richard E Carson
TL;DR
This study shows that direct-4D PET image reconstruction reduces noise and variability in Parkinson’s disease scans compared to traditional methods.
Contribution
First demonstration that direct-4D reconstruction lowers variability and bias in both within- and between-subject analyses for Parkinson’s disease.
Findings
DR reduced within-subject variability for K1 and VT compared to IR across all count levels.
Between-subject variability was significantly lower with DR than IR for K1 and VT.
DR at 5% count level matched IR at 20% count level in variability for K1 and VT.
Abstract
Objective. Direct reconstruction (DR) of parametric images from dynamic positron emission tomography data has been shown to provide substantial noise reduction compared to the conventional indirect reconstruction (IR) approach where frames are first reconstructed and then voxel time-activity curves are fitted to a kinetic model. The main goal was to compare DR and IR, on both within-subject and between-subject variability. Approach. This work evaluated the Parametric motion-compensation OSEM List-mode algorithm for resolution-recovery-1T DR method, using multiple scans of Parkinson’s disease patients with [11C]UCB-J, a radioligand for synaptic vesicle glycoprotein 2A (SV2A), a marker for synaptic density. This was achieved by comparing K1, k2, and VT parametric images estimated, at full- and lower-count levels (20%, 10%, and 5%), between DR and IR. Main Results. DR delivered…
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Taxonomy
TopicsParkinson's Disease Mechanisms and Treatments · Medical Imaging Techniques and Applications · Neurological disorders and treatments