Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for (18)F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom

BACKGROUND: The Bayesian penalized likelihood (BPL) algorithm Q.Clear (GE Healthcare) allows fully convergent iterative reconstruction that results in better image quality and quantitative accuracy, while limiting image noise. The present study aimed to optimize BPL reconstruction parameters for (18...

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Detalles Bibliográficos
Autores principales: Yoshii, Tokiya, Miwa, Kenta, Yamaguchi, Masashi, Shimada, Kai, Wagatsuma, Kei, Yamao, Tensho, Kamitaka, Yuto, Hiratsuka, Seiya, Kobayashi, Rinya, Ichikawa, Hajime, Miyaji, Noriaki, Miyazaki, Tsuyoshi, Ishii, Kenji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2020
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7486353/
https://www.ncbi.nlm.nih.gov/pubmed/32915344
http://dx.doi.org/10.1186/s40658-020-00325-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7486353/
https://www.ncbi.nlm.nih.gov/pubmed/32915344
http://dx.doi.org/10.1186/s40658-020-00325-8

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