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Schlaeger, S.* ; Drummer, K.* ; El Husseini, M.* ; Kofler, F. ; Sollmann, N.* ; Schramm, S.* ; Zimmer, C.* ; Wiestler, B.* ; Kirschke, J.S.*

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.

Eur. Radiol. 33, 5882-5893 (2023)

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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 dataset comprised scans of 73 patients from two scanners, and the test dataset, scans of 101 patients from 38 multicenter scanners. Apparent signal- and contrast-to-noise ratios (aSNR/aCNR) were measured in true and synthetic T2-w fs. Two neuroradiologists graded image (5-point scale) and fat saturation quality (3-point scale). To evaluate whether the T2-w fs images are indistinguishable, a Turing test was performed by eleven neuroradiologists. Six pathologies were graded on the synthetic protocol (with synthetic T2-w fs) and the original protocol (with true T2-w fs) by the two neuroradiologists. RESULTS: aSNR and aCNR were not significantly different between the synthetic and true T2-w fs images. Subjective image quality was graded higher for synthetic T2-w fs (p = 0.023). In the Turing test, synthetic and true T2-w fs could not be distinguished from each other. The intermethod agreement between synthetic and original protocol ranged from substantial to almost perfect agreement for the evaluated pathologies. DISCUSSION: The synthetic T2-w fs might replace a physical T2-w fs. Our approach validated on a challenging, multicenter dataset is highly generalizable and allows for shorter scan protocols. KEY POINTS: • Generative adversarial networks can be used to generate synthetic T2-weighted fat sat images from T1- and non-fat sat T2-weighted images of the spine. • The synthetic T2-weighted fat sat images might replace a physically acquired T2-weighted fat sat showing a better image quality and excellent diagnostic agreement with the true T2-weighted fat images. • The present approach validated on a challenging, multicenter dataset is highly generalizable and allows for significantly shorter scan protocols.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Artificial Intelligence ; Magnetic Resonance Imaging ; Spine; Lumbar Spine; Resonance; Mri; Degeneration; Suppression; Fractures; Infection; Sequence; System; Echo

ISSN (print) / ISBN 0938-7994

e-ISSN 1432-1084

Journal European Radiology

Quellenangaben Volume: 33, Issue: 8, Pages: 5882-5893

Publisher Springer

Publishing Place One New York Plaza, Suite 4600, New York, Ny, United States

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)

Grants European Research Council (ERC) under the European Union
ERC
BMBF (German Ministry of Education and Research)
DFG