Applications of the line-of-response probability density function resolution model in PET list mode reconstruction

Partial volume correction analysis for 11C-UCB-J PET studies of Alzheimer's disease

Purpose:

¹¹C-UCB-J PET imaging, targeting synaptic vesicle glycoprotein 2A (SV2A), has been shown to be a useful indicator of synaptic density in Alzheimer’s disease (AD). For SV2A imaging, a decrease in apparent tracer uptake is often due to the combination of gray-matter (GM) atrophy and SV2A decrease in the remaining tissue. Our aim is to reveal the true SV2A change by performing partial volume correction (PVC).

Methods:

We performed two PVC algorithms, Müller-Gärtner (MG) and ‘iterative Yang’ (IY), on 17 AD participants and 11 cognitive normal (CN) participants using the brain-dedicated HRRT scanner. Distribution volume V_T, the rate constant K₁, binding potential BP_ND (centrum semiovale as reference region), and tissue volume were compared.

Results:

In most regions, both PVC algorithms reduced the between-group differences. Alternatively, in hippocampus, IY increased the significance of between-group differences while MG reduced it (V_T, BP_ND and K₁ group differences: uncorrected: 20%, 27%, 17%; MG: 18%, 22%, 14%; IY: 22%, 28%, 17%). The group difference in hippocampal volume (10%) was substantially smaller than any PET measures. MG increased GM binding values to a greater extent than IY due to differences in algorithm assumptions.

Conclusion:

¹¹C-UCB-J binding is significantly reduced in AD hippocampus, but PVC is important to adjust for significant volume reduction. After correction, PET measures are substantially more sensitive to group differences than volumetric MRI measures. Assumptions of each PVC algorithm are important and should be carefully examined and validated. For ¹¹C-UCB-J, the less stringent assumptions of IY support its use as a PVC algorithm over MG.

High-resolution dynamic inversion imaging with motion-aberrations-free using optical flow learning networks

Dynamic optical imaging (e.g. time delay integration imaging) is troubled by the motion blur fundamentally arising from mismatching between photo-induced charge transfer and optical image movements. Motion aberrations from the forward dynamic imaging link impede the acquiring of high-quality images. Here, we propose a high-resolution dynamic inversion imaging method based on optical flow neural learning networks. Optical flow is reconstructed via a multilayer neural learning network. The optical flow is able to construct the motion spread function that enables computational reconstruction of captured images with a single digital filter. This works construct the complete dynamic imaging link, involving the backward and forward imaging link, and demonstrates the capability of the back-ward imaging by reducing motion aberrations.