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Title:

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.

Document type:
Multicenter Study; Journal Article
Author(s):
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...     »
Journal title abbreviation:
Eur Radiol
Year:
2023
Journal volume:
33
Journal issue:
8
Pages contribution:
5882-5893
Fulltext / DOI:
doi:10.1007/s00330-023-09512-4
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/36928566
Print-ISSN:
0938-7994
TUM Institution:
Professur für AI for Image-Guided Diagnosis and Therapy (Prof. Wiestler); Professur für Neuroradiologie (Prof. Zimmer)
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