Optimization of penalization function in Bayesian penalized likelihood reconstruction algorithm for [
Alzheimer’s disease
Amyloid imaging
Dementia
Quantitative analysis
Regularized reconstruction
Journal
Physical and engineering sciences in medicine
ISSN: 2662-4737
Titre abrégé: Phys Eng Sci Med
Pays: Switzerland
ID NLM: 101760671
Informations de publication
Date de publication:
12 Aug 2024
12 Aug 2024
Historique:
received:
16
05
2024
accepted:
06
08
2024
medline:
12
8
2024
pubmed:
12
8
2024
entrez:
12
8
2024
Statut:
aheadofprint
Résumé
Point-spread-function (PSF) correction is not recommended for amyloid PET images due to Gibbs artifacts. Q.Clear™, a Bayesian Penalized Likelihood (BPL) reconstruction method without incorporating PSF correction reduces these artifacts but degrades image contrast by our previous findings. The present study aimed to recover lost contrast by optimizing reconstruction parameters in time-of-flight (TOF) BPL reconstruction of amyloid PET images without PSF correction. We selected candidate conditions based on a phantom study and then determined which were optimal in a clinical study. Phantom images were reconstructed under conditions of 1‒9 iterations, β 300-1000 and γ factors from 2 to 10 in TOF-BPL without PSF correction. We evaluated the %contrast and the coefficients of variation (CV, %). Standardized uptake value ratios (SUVr) and Centiloid scales (CL) were calculated from PET images acquired from 71 participants after an [
Identifiants
pubmed: 39133373
doi: 10.1007/s13246-024-01476-z
pii: 10.1007/s13246-024-01476-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Japan Society for the Promotion of Science
ID : JP20K16747
Informations de copyright
© 2024. Australasian College of Physical Scientists and Engineers in Medicine.
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