Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1619-7089

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners ; day:19 ; month:7 ; year:2022 ; pages:1-13
European journal of nuclear medicine and molecular imaging ; (19.7.2022), 1-13

Creator: Sari, Hasan
Teimoorisichani, Mohammadreza
Mingels, Clemens
Alberts, Ian
Panin, Vladimir
Bharkhada, Deepak
Xue, Song
Prenosil, George
Shi, Kuangyu
Conti, Maurizio
Rominger, Axel

Contributor: SpringerLink (Online service)

DOI: 10.1007/s00259-022-05909-3

URN: urn:nbn:de:101:1-2022100120091717656691

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:30 AM CEST

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Associated

Sari, Hasan
Teimoorisichani, Mohammadreza
Mingels, Clemens
Alberts, Ian
Panin, Vladimir
Bharkhada, Deepak
Xue, Song
Prenosil, George
Shi, Kuangyu
Conti, Maurizio
Rominger, Axel
SpringerLink (Online service)

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Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners

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