Synthetic T2-weighted fat sat delivers valuable information on spine pathologies: multicenter validation of a Generative Adversarial Network

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.