Independent attenuation correction of whole body [F-18]FDG-PET using a deep learning approach with Generative Adversarial Networks

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dc.contributor.author Armanious, Karim
dc.contributor.author Hepp, Tobias
dc.contributor.author Küstner, Thomas
dc.contributor.author Dittmann, Helmut
dc.contributor.author Nikolaou, Konstantin
dc.contributor.author La Fougère, Christian Jean
dc.contributor.author Gatidis, Sergios
dc.date.accessioned 2020-09-03T14:29:38Z
dc.date.available 2020-09-03T14:29:38Z
dc.date.issued 2020
dc.identifier.issn 2191-219X
dc.identifier.uri http://hdl.handle.net/10900/106197
dc.language.iso en de_DE
dc.publisher Springer de_DE
dc.relation.uri http://dx.doi.org/10.1186/s13550-020-00644-y de_DE
dc.subject.ddc 610 de_DE
dc.title Independent attenuation correction of whole body [F-18]FDG-PET using a deep learning approach with Generative Adversarial Networks de_DE
dc.type Article de_DE
utue.quellen.id 20200715072417_00402
utue.personen.roh Armanious, Karim
utue.personen.roh Hepp, Tobias
utue.personen.roh Kuestnert, Thomas
utue.personen.roh Dittmann, Helmut
utue.personen.roh Nikolaou, Konstantin
utue.personen.roh La Fougere, Christian
utue.personen.roh Yang, Bin
utue.personen.roh Gatidis, Sergios
dcterms.isPartOf.ZSTitelID Ejnmmi Research de_DE
dcterms.isPartOf.ZS-Issue Article 53 de_DE
dcterms.isPartOf.ZS-Volume 10 de_DE
utue.fakultaet 04 Medizinische Fakultät de_DE


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