Synthetic T2-weighted fat sat based on a generative adversarial network shows potential for scan time reduction in spine imaging in a multicenter test dataset.

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作者： Sarah,Schlaeger ^[1] ; Katharina,Drummer ^[2] ; Malek,El Husseini ^[2] ; Florian,Kofler ^[2] ; Nico,Sollmann ^[3] ; Severin,Schramm ^[4] ; Claus,Zimmer ^[5] ; Benedikt,Wiestler ^[2] ; Jan S,Kirschke ^[6]

作者单位： Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany. sarah.schlaeger@tum.de. ^[1] Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany. ^[2] Department of Informatics, Technical University of Munich, Munich, Germany. ^[3] TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany. ^[4] Helmholtz AI, Helmholtz Zentrum München, Munich, Germany. ^[5] TUM-NeuroImaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany. ^[6] Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany. ^[7]

关键词 Artificial intelligence Magnetic resonance imaging Spine

主题词人类(Humans);脊柱(Spine);磁共振成像(Magnetic Resonance Imaging);放射性核素显像(Radionuclide Imaging)

DOI 10.1007/s00330-023-09512-4

PMID 36928566

发布时间 2023-10-13

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