Abstract #1513
Synthetic T2-weighted fat sat delivers valuable information on spine pathologies: multicenter validation of a Generative Adversarial Network
Sarah Schlaeger1, Katharina Drummer1, Malek El Husseini1, Florian Kofler1,2,3, Nico Sollmann1,4,5, Severin Schramm1, Claus Zimmer1, Dimitrios C. Karampinos6, Benedikt Wiestler1, and Jan S. Kirschke1
1Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany, 2Department of Informatics, Technical University of Munich, Munich, Germany, 3TranslaTUM - Central Insitute for Translational Cancer Research, Technical University of Munich, Munich, Germany, 4TUM-NeuroImaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany, 5Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany, 6Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
Synopsis
Generative Adversarial Networks (GANs) can synthesize missing Magnetic Resonance (MR) contrasts from existing MR data. In spine imaging, sagittal T2-w fat sat (fs) sequences are an important additional MR contrast next to conventional T1-w and T2-w sequences. In this study, the diagnostic performance of a GAN-based, synthetic T2-w fs is evaluated in a multicenter dataset. By comparing the synthetic T2-w fs with its true counterpart regarding ability to detect spinal pathologies not seen on T1-w and non-fs T2-w, diagnostics agreement, and image and fs quality our work shows that a synthetic T2-w fs delivers valuable information on spine pathologies.
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