Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction

Despite the potential of deep learning (DL)-based methods in substituting CT-based PET attenuation and scatter correction for CT-free PET imaging, a critical bottleneck is their limited capability in handling large heterogeneity of tracers and scanners of PET imaging. This study employs a simple way...

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Detalles Bibliográficos
Autores principales: Guo, Rui, Xue, Song, Hu, Jiaxi, Sari, Hasan, Mingels, Clemens, Zeimpekis, Konstantinos, Prenosil, George, Wang, Yue, Zhang, Yu, Viscione, Marco, Sznitman, Raphael, Rominger, Axel, Li, Biao, Shi, Kuangyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9537165/
https://www.ncbi.nlm.nih.gov/pubmed/36202816
http://dx.doi.org/10.1038/s41467-022-33562-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9537165/
https://www.ncbi.nlm.nih.gov/pubmed/36202816
http://dx.doi.org/10.1038/s41467-022-33562-9

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