Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic (18)F-FDG PET Brain Studies

This work set out to develop a motion-correction approach aided by conditional generative adversarial network (cGAN) methodology that allows reliable, data-driven determination of involuntary subject motion during dynamic (18)F-FDG brain studies. Methods: Ten healthy volunteers (5 men/5 women; mean...

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Detalles Bibliográficos
Autores principales: Shiyam Sundar, Lalith Kumar, Iommi, David, Muzik, Otto, Chalampalakis, Zacharias, Klebermass, Eva-Maria, Hienert, Marius, Rischka, Lucas, Lanzenberger, Rupert, Hahn, Andreas, Pataraia, Ekaterina, Traub-Weidinger, Tatjana, Hummel, Johann, Beyer, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Nuclear Medicine 2021
Materias:
Basic Science Investigation
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8729870/
https://www.ncbi.nlm.nih.gov/pubmed/33246982
http://dx.doi.org/10.2967/jnumed.120.248856

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8729870/
https://www.ncbi.nlm.nih.gov/pubmed/33246982
http://dx.doi.org/10.2967/jnumed.120.248856

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