Variance reduction with synaptic density imaging in Parkinson's disease using direct-4D PET image reconstruction.

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 bothwithin-subjectandbetween-subjectvariability.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 comparingK1,k2, andVTparametric images estimated, at full- and lower-count levels (20%, 10%, and 5%), between DR and IR.Main Results.DR delivered considerable improvement, compared to IR, by substantially reducing variability for bothwithin-subjectandbetween-subjectanalyses, and dramatically reducing noise-induced bias forK1andVT. Conversely, IR increased thewithin-subjectvariability forK1by 79%-353% and forVTby 62%-79% across the lower count levels (averaged over regions at matched iterations). Thebetween-subjectvariability was also increased with IR over DR with an increase of 20%-221% forK1and 45%-48% forVT. Even at the full-count level, thebetween-subjectvariability was slightly increased forK1by 4%, but by 24% forVT. Furthermore, at 5% count level, DR delivered comparable variability to IR at 20% counts. The noise-induced %bias, relative to the full-count level, for IR was 3%-28% (from 20% to 5% count levels respectively) forK1and 12%-31% forVT, whilst for DR the %bias was only 1% forK1across count levels, and 7%-18% forVT.Significance.To the best of our knowledge, this is the first demonstration that direct-4D reconstruction delivers lower variability and bias not only forwithin-subjectanalysis, but also forbetween-subjectanalysis.