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Titel:

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.

Dokumenttyp:
Multicenter Study; Journal Article
Autor(en):
Schlaeger, Sarah; Drummer, Katharina; El Husseini, Malek; Kofler, Florian; Sollmann, Nico; Schramm, Severin; Zimmer, Claus; Wiestler, Benedikt; Kirschke, Jan S
Abstract:
OBJECTIVES: T2-weighted (w) fat sat (fs) sequences, which are important in spine MRI, require a significant amount of scan time. Generative adversarial networks (GANs) can generate synthetic T2-w fs images. We evaluated the potential of synthetic T2-w fs images by comparing them to their true counterpart regarding image and fat saturation quality, and diagnostic agreement in a heterogenous, multicenter dataset. METHODS: A GAN was used to synthesize T2-w fs from T1- and non-fs T2-w. The training...     »
Zeitschriftentitel:
Eur Radiol
Jahr:
2023
Band / Volume:
33
Heft / Issue:
8
Seitenangaben Beitrag:
5882-5893
Volltext / DOI:
doi:10.1007/s00330-023-09512-4
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/36928566
Print-ISSN:
0938-7994
TUM Einrichtung:
Professur für AI for Image-Guided Diagnosis and Therapy (Prof. Wiestler); Professur für Neuroradiologie (Prof. Zimmer)
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