Phantom and clinical evaluation of the Bayesian penalised likelihood reconstruction algorithm Q.Clear without PSF correction in amyloid PET images.
Alzheimer’s disease
Amyloid imaging
Dementia
Quantitative analysis
Regularised reconstruction
Journal
EJNMMI physics
ISSN: 2197-7364
Titre abrégé: EJNMMI Phys
Pays: Germany
ID NLM: 101658952
Informations de publication
Date de publication:
22 Apr 2024
22 Apr 2024
Historique:
received:
18
05
2023
accepted:
12
04
2024
medline:
22
4
2024
pubmed:
22
4
2024
entrez:
22
4
2024
Statut:
epublish
Résumé
Bayesian penalised likelihood (BPL) reconstruction, which incorporates point-spread-function (PSF) correction, provides higher signal-to-noise ratios and more accurate quantitation than conventional ordered subset expectation maximization (OSEM) reconstruction. However, applying PSF correction to brain PET imaging is controversial due to Gibbs artefacts that manifest as unpredicted cortical uptake enhancement. The present study aimed to validate whether BPL without PSF would be useful for amyloid PET imaging. Images were acquired from Hoffman 3D brain and cylindrical phantoms for phantom study and 71 patients administered with [ The overestimated radioactivity in profile curves was eliminated using BPL without PSF correction. The %contrast and image noise decreased with increasing β values in phantom images. Image quality and RCs were better using BPL with, than without PSF correction or OSEM. An optimal β value of 600 was determined for BPL without PSF correction. Visual evaluation almost agreed perfectly (κ = 0.91-0.97), without depending on reconstruction methods. Composite SUVRs did not significantly differ between reconstruction methods. Gibbs artefacts disappeared from phantom images using the BPL without PSF correction. Visual and quantitative evaluation of [
Identifiants
pubmed: 38647924
doi: 10.1186/s40658-024-00641-3
pii: 10.1186/s40658-024-00641-3
doi:
Types de publication
Journal Article
Langues
eng
Pagination
37Subventions
Organisme : Japan Society for the Promotion of Science
ID : JP20K16747
Informations de copyright
© 2024. The Author(s).
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