Phantom and clinical evaluation of the effect of a new Bayesian penalized likelihood reconstruction algorithm (HYPER Iterative) on (68)Ga-DOTA-NOC PET/CT image quality

BACKGROUND: Bayesian penalized likelihood (BPL) algorithm is an effective way to suppress noise in the process of positron emission tomography (PET) image reconstruction by incorporating a smooth penalty. The strength of the smooth penalty is controlled by the penalization factor. The aim was to inv...

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Detalles Bibliográficos
Autores principales: Xu, Lei, Cui, Can, Li, Rushuai, Yang, Rui, Liu, Rencong, Meng, Qingle, Wang, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9742075/
https://www.ncbi.nlm.nih.gov/pubmed/36504014
http://dx.doi.org/10.1186/s13550-022-00945-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9742075/
https://www.ncbi.nlm.nih.gov/pubmed/36504014
http://dx.doi.org/10.1186/s13550-022-00945-4

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