Deep learning–based time-of-flight (ToF) image enhancement of non-ToF PET scans

PURPOSE: To improve the quantitative accuracy and diagnostic confidence of PET images reconstructed without time-of-flight (ToF) using deep learning models trained for ToF image enhancement (DL-ToF). METHODS: A total of 273 [(18)F]-FDG PET scans were used, including data from 6 centres equipped with...

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Detalles Bibliográficos
Autores principales: Mehranian, Abolfazl, Wollenweber, Scott D., Walker, Matthew D., Bradley, Kevin M., Fielding, Patrick A., Huellner, Martin, Kotasidis, Fotis, Su, Kuan-Hao, Johnsen, Robert, Jansen, Floris P., McGowan, Daniel R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399038/
https://www.ncbi.nlm.nih.gov/pubmed/35507059
http://dx.doi.org/10.1007/s00259-022-05824-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399038/
https://www.ncbi.nlm.nih.gov/pubmed/35507059
http://dx.doi.org/10.1007/s00259-022-05824-7

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