Optimization of Q.Clear reconstruction for dynamic (18)F PET imaging

BACKGROUND: Q.Clear, a Bayesian penalized likelihood reconstruction algorithm, has shown high potential in improving quantitation accuracy in PET systems. The Q.Clear algorithm controls noise during the iterative reconstruction through a β penalization factor. This study aimed to determine the optim...

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Detalles Bibliográficos
Autores principales: Lysvik, Elisabeth Kirkeby, Mikalsen, Lars Tore Gyland, Rootwelt-Revheim, Mona-Elisabeth, Emblem, Kyrre Eeg, Hjørnevik, Trine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10589167/
https://www.ncbi.nlm.nih.gov/pubmed/37861929
http://dx.doi.org/10.1186/s40658-023-00584-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10589167/
https://www.ncbi.nlm.nih.gov/pubmed/37861929
http://dx.doi.org/10.1186/s40658-023-00584-1

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