A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET

PURPOSE: A critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from low-dose scans on different scanners and tracers. METH...

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Detalles Bibliográficos
Autores principales: Xue, Song, Guo, Rui, Bohn, Karl Peter, Matzke, Jared, Viscione, Marco, Alberts, Ian, Meng, Hongping, Sun, Chenwei, Zhang, Miao, Zhang, Min, Sznitman, Raphael, El Fakhri, Georges, Rominger, Axel, Li, Biao, Shi, Kuangyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9015984/
https://www.ncbi.nlm.nih.gov/pubmed/34950968
http://dx.doi.org/10.1007/s00259-021-05644-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9015984/
https://www.ncbi.nlm.nih.gov/pubmed/34950968
http://dx.doi.org/10.1007/s00259-021-05644-1

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