While gadolinium-based contrast agents (GBCAs) have been dispensable for magnetic resonance imaging in clinic practice, recently there are rising concerns about the safety of GBCAs. In previous studies, we tested the feasibility of predicting contrast agents from pre-contrast images via deep convolutional neural networks. In this study, we further improved the results with deep attention generative adversarial network. The image similarity metrics show that the model can synthesize post-contrast T1w images with superior image quality and great resemblance to the ground truth images. Restoration of contrast uptake is clearly noted on the synthesized images even in small vessels and lesions.
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