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

Implementation of GAN-based, synthetic T2-weighted fat saturated images in the routine radiological workflow improves spinal pathology detection.

Diagnostics 13:11 (2023)

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(1) Background and Purpose: In magnetic resonance imaging (MRI) of the spine, T2-weighted (T2-w) fat-saturated (fs) images improve the diagnostic assessment of pathologies. However, in the daily clinical setting, additional T2-w fs images are frequently missing due to time constraints or motion artifacts. Generative adversarial networks (GANs) can generate synthetic T2-w fs images in a clinically feasible time. Therefore, by simulating the radiological workflow with a heterogenous dataset, this study's purpose was to evaluate the diagnostic value of additional synthetic, GAN-based T2-w fs images in the clinical routine. (2) Methods: 174 patients with MRI of the spine were retrospectively identified. A GAN was trained to synthesize T2-w fs images from T1-w, and non-fs T2-w images of 73 patients scanned in our institution. Subsequently, the GAN was used to create synthetic T2-w fs images for the previously unseen 101 patients from multiple institutions. In this test dataset, the additional diagnostic value of synthetic T2-w fs images was assessed in six pathologies by two neuroradiologists. Pathologies were first graded on T1-w and non-fs T2-w images only, then synthetic T2-w fs images were added, and pathologies were graded again. Evaluation of the additional diagnostic value of the synthetic protocol was performed by calculation of Cohen's ĸ and accuracy in comparison to a ground truth (GT) grading based on real T2-w fs images, pre- or follow-up scans, other imaging modalities, and clinical information. (3) Results: The addition of the synthetic T2-w fs to the imaging protocol led to a more precise grading of abnormalities than when grading was based on T1-w and non-fs T2-w images only (mean ĸ GT versus synthetic protocol = 0.65; mean ĸ GT versus T1/T2 = 0.56; p = 0.043). (4) Conclusions: The implementation of synthetic T2-w fs images in the radiological workflow significantly improves the overall assessment of spine pathologies. Thereby, high-quality, synthetic T2-w fs images can be virtually generated by a GAN from heterogeneous, multicenter T1-w and non-fs T2-w contrasts in a clinically feasible time, which underlines the reproducibility and generalizability of our approach.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords T2-w Fat Saturated Images ; Data Augmentation ; Generative Adversarial Network ; Magnetic Resonance Imaging ; Spine; Mri; Suppression; Infection; Fractures; Sequence; Echo

ISSN (print) / ISBN 2075-4418

e-ISSN 2075-4418

Journal Diagnostics

Quellenangaben Volume: 13, Issue: 5, Article Number: 11

Publisher MDPI

Publishing Place St Alban-anlage 66, Ch-4052 Basel, Switzerland

Non-patent literature Publications

Reviewing status Peer reviewed

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

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